reliability-based uncertainty analysis: Topics by WorldWideScience.org

Sample records for reliability-based uncertainty analysis

Reliability analysis under epistemic uncertainty

International Nuclear Information System (INIS)

Nannapaneni, Saideep; Mahadevan, Sankaran

2016-01-01

This paper proposes a probabilistic framework to include both aleatory and epistemic uncertainty within model-based reliability estimation of engineering systems for individual limit states. Epistemic uncertainty is considered due to both data and model sources. Sparse point and/or interval data regarding the input random variables leads to uncertainty regarding their distribution types, distribution parameters, and correlations; this statistical uncertainty is included in the reliability analysis through a combination of likelihood-based representation, Bayesian hypothesis testing, and Bayesian model averaging techniques. Model errors, which include numerical solution errors and model form errors, are quantified through Gaussian process models and included in the reliability analysis. The probability integral transform is used to develop an auxiliary variable approach that facilitates a single-level representation of both aleatory and epistemic uncertainty. This strategy results in an efficient single-loop implementation of Monte Carlo simulation (MCS) and FORM/SORM techniques for reliability estimation under both aleatory and epistemic uncertainty. Two engineering examples are used to demonstrate the proposed methodology. - Highlights: • Epistemic uncertainty due to data and model included in reliability analysis. • A novel FORM-based approach proposed to include aleatory and epistemic uncertainty. • A single-loop Monte Carlo approach proposed to include both types of uncertainties. • Two engineering examples used for illustration.
Durability reliability analysis for corroding concrete structures under uncertainty

Science.gov (United States)

Zhang, Hao

2018-02-01

This paper presents a durability reliability analysis of reinforced concrete structures subject to the action of marine chloride. The focus is to provide insight into the role of epistemic uncertainties on durability reliability. The corrosion model involves a number of variables whose probabilistic characteristics cannot be fully determined due to the limited availability of supporting data. All sources of uncertainty, both aleatory and epistemic, should be included in the reliability analysis. Two methods are available to formulate the epistemic uncertainty: the imprecise probability-based method and the purely probabilistic method in which the epistemic uncertainties are modeled as random variables. The paper illustrates how the epistemic uncertainties are modeled and propagated in the two methods, and shows how epistemic uncertainties govern the durability reliability.
Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

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Kang Rui

2016-06-01

Full Text Available In this paper, a systematic review of non-probabilistic reliability metrics is conducted to assist the selection of appropriate reliability metrics to model the influence of epistemic uncertainty. Five frequently used non-probabilistic reliability metrics are critically reviewed, i.e., evidence-theory-based reliability metrics, interval-analysis-based reliability metrics, fuzzy-interval-analysis-based reliability metrics, possibility-theory-based reliability metrics (posbist reliability and uncertainty-theory-based reliability metrics (belief reliability. It is pointed out that a qualified reliability metric that is able to consider the effect of epistemic uncertainty needs to (1 compensate the conservatism in the estimations of the component-level reliability metrics caused by epistemic uncertainty, and (2 satisfy the duality axiom, otherwise it might lead to paradoxical and confusing results in engineering applications. The five commonly used non-probabilistic reliability metrics are compared in terms of these two properties, and the comparison can serve as a basis for the selection of the appropriate reliability metrics.
Hybrid Structural Reliability Analysis under Multisource Uncertainties Based on Universal Grey Numbers

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Xingfa Yang

2018-01-01

Full Text Available Nondeterministic parameters of certain distribution are employed to model structural uncertainties, which are usually assumed as stochastic factors. However, model parameters may not be precisely represented due to some factors in engineering practices, such as lack of sufficient data, data with fuzziness, and unknown-but-bounded conditions. To this end, interval and fuzzy parameters are implemented and an efficient approach to structural reliability analysis with random-interval-fuzzy hybrid parameters is proposed in this study. Fuzzy parameters are first converted to equivalent random ones based on the equal entropy principle. 3σ criterion is then employed to transform the equivalent random and the original random parameters to interval variables. In doing this, the hybrid reliability problem is transformed into the one only with interval variables, in other words, nonprobabilistic reliability analysis problem. Nevertheless, the problem of interval extension existed in interval arithmetic, especially for the nonlinear systems. Therefore, universal grey mathematics, which can tackle the issue of interval extension, is employed to solve the nonprobabilistic reliability analysis problem. The results show that the proposed method can obtain more conservative results of the hybrid structural reliability.
The explicit treatment of model uncertainties in the presence of aleatory and epistemic parameter uncertainties in risk and reliability analysis

International Nuclear Information System (INIS)

Ahn, Kwang Il; Yang, Joon Eon

2003-01-01

In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems
An enhanced unified uncertainty analysis approach based on first order reliability method with single-level optimization

International Nuclear Information System (INIS)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; Tooren, Michel van

2013-01-01

In engineering, there exist both aleatory uncertainties due to the inherent variation of the physical system and its operational environment, and epistemic uncertainties due to lack of knowledge and which can be reduced with the collection of more data. To analyze the uncertain distribution of the system performance under both aleatory and epistemic uncertainties, combined probability and evidence theory can be employed to quantify the compound effects of the mixed uncertainties. The existing First Order Reliability Method (FORM) based Unified Uncertainty Analysis (UUA) approach nests the optimization based interval analysis in the improved Hasofer–Lind–Rackwitz–Fiessler (iHLRF) algorithm based Most Probable Point (MPP) searching procedure, which is computationally inhibitive for complex systems and may encounter convergence problem as well. Therefore, in this paper it is proposed to use general optimization solvers to search MPP in the outer loop and then reformulate the double-loop optimization problem into an equivalent single-level optimization (SLO) problem, so as to simplify the uncertainty analysis process, improve the robustness of the algorithm, and alleviate the computational complexity. The effectiveness and efficiency of the proposed method is demonstrated with two numerical examples and one practical satellite conceptual design problem. -- Highlights: ► Uncertainty analysis under mixed aleatory and epistemic uncertainties is studied. ► A unified uncertainty analysis method is proposed with combined probability and evidence theory. ► The traditional nested analysis method is converted to single level optimization for efficiency. ► The effectiveness and efficiency of the proposed method are testified with three examples
Reliability- and performance-based robust design optimization of MEMS structures considering technological uncertainties

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Martowicz, Adam; Uhl, Tadeusz

2012-10-01

The paper discusses the applicability of a reliability- and performance-based multi-criteria robust design optimization technique for micro-electromechanical systems, considering their technological uncertainties. Nowadays, micro-devices are commonly applied systems, especially in the automotive industry, taking advantage of utilizing both the mechanical structure and electronic control circuit on one board. Their frequent use motivates the elaboration of virtual prototyping tools that can be applied in design optimization with the introduction of technological uncertainties and reliability. The authors present a procedure for the optimization of micro-devices, which is based on the theory of reliability-based robust design optimization. This takes into consideration the performance of a micro-device and its reliability assessed by means of uncertainty analysis. The procedure assumes that, for each checked design configuration, the assessment of uncertainty propagation is performed with the meta-modeling technique. The described procedure is illustrated with an example of the optimization carried out for a finite element model of a micro-mirror. The multi-physics approach allowed the introduction of several physical phenomena to correctly model the electrostatic actuation and the squeezing effect present between electrodes. The optimization was preceded by sensitivity analysis to establish the design and uncertain domains. The genetic algorithms fulfilled the defined optimization task effectively. The best discovered individuals are characterized by a minimized value of the multi-criteria objective function, simultaneously satisfying the constraint on material strength. The restriction of the maximum equivalent stresses was introduced with the conditionally formulated objective function with a penalty component. The yielded results were successfully verified with a global uniform search through the input design domain.
Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables

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Shujuan Wang

2015-01-01

Full Text Available This paper investigates the structural design optimization to cover both the reliability and robustness under uncertainty in design variables. The main objective is to improve the efficiency of the optimization process. To address this problem, a hybrid reliability-based robust design optimization (RRDO method is proposed. Prior to the design optimization, the Sobol sensitivity analysis is used for selecting key design variables and providing response variance as well, resulting in significantly reduced computational complexity. The single-loop algorithm is employed to guarantee the structural reliability, allowing fast optimization process. In the case of robust design, the weighting factor balances the response performance and variance with respect to the uncertainty in design variables. The main contribution of this paper is that the proposed method applies the RRDO strategy with the usage of global approximation and the Sobol sensitivity analysis, leading to the reduced computational cost. A structural example is given to illustrate the performance of the proposed method.
Interpretations of alternative uncertainty representations in a reliability and risk analysis context

International Nuclear Information System (INIS)

Aven, T.

2011-01-01

Probability is the predominant tool used to measure uncertainties in reliability and risk analyses. However, other representations also exist, including imprecise (interval) probability, fuzzy probability and representations based on the theories of evidence (belief functions) and possibility. Many researchers in the field are strong proponents of these alternative methods, but some are also sceptical. In this paper, we address one basic requirement set for quantitative measures of uncertainty: the interpretation needed to explain what an uncertainty number expresses. We question to what extent the various measures meet this requirement. Comparisons are made with probabilistic analysis, where uncertainty is represented by subjective probabilities, using either a betting interpretation or a reference to an uncertainty standard interpretation. By distinguishing between chances (expressing variation) and subjective probabilities, new insights are gained into the link between the alternative uncertainty representations and probability.
Uncertainty propagation and sensitivity analysis in system reliability assessment via unscented transformation

International Nuclear Information System (INIS)

Rocco Sanseverino, Claudio M.; Ramirez-Marquez, José Emmanuel

2014-01-01

The reliability of a system, notwithstanding it intended function, can be significantly affected by the uncertainty in the reliability estimate of the components that define the system. This paper implements the Unscented Transformation to quantify the effects of the uncertainty of component reliability through two approaches. The first approach is based on the concept of uncertainty propagation, which is the assessment of the effect that the variability of the component reliabilities produces on the variance of the system reliability. This assessment based on UT has been previously considered in the literature but only for system represented through series/parallel configuration. In this paper the assessment is extended to systems whose reliability cannot be represented through analytical expressions and require, for example, Monte Carlo Simulation. The second approach consists on the evaluation of the importance of components, i.e., the evaluation of the components that most contribute to the variance of the system reliability. An extension of the UT is proposed to evaluate the so called “main effects” of each component, as well to assess high order component interaction. Several examples with excellent results illustrate the proposed approach. - Highlights: • Simulation based approach for computing reliability estimates. • Computation of reliability variance via 2n+1 points. • Immediate computation of component importance. • Application to network systems
Uncertainties and reliability theories for reactor safety

International Nuclear Information System (INIS)

Veneziano, D.

1975-01-01

What makes the safety problem of nuclear reactors particularly challenging is the demand for high levels of reliability and the limitation of statistical information. The latter is an unfortunate circumstance, which forces deductive theories of reliability to use models and parameter values with weak factual support. The uncertainty about probabilistic models and parameters which are inferred from limited statistical evidence can be quantified and incorporated rationally into inductive theories of reliability. In such theories, the starting point is the information actually available, as opposed to an estimated probabilistic model. But, while the necessity of introducing inductive uncertainty into reliability theories has been recognized by many authors, no satisfactory inductive theory is presently available. The paper presents: a classification of uncertainties and of reliability models for reactor safety; a general methodology to include these uncertainties into reliability analysis; a discussion about the relative advantages and the limitations of various reliability theories (specifically, of inductive and deductive, parametric and nonparametric, second-moment and full-distribution theories). For example, it is shown that second-moment theories, which were originally suggested to cope with the scarcity of data, and which have been proposed recently for the safety analysis of secondary containment vessels, are the least capable of incorporating statistical uncertainty. The focus is on reliability models for external threats (seismic accelerations and tornadoes). As an application example, the effect of statistical uncertainty on seismic risk is studied using parametric full-distribution models
Uncertainty analysis of nonlinear systems employing the first-order reliability method

International Nuclear Information System (INIS)

Choi, Chan Kyu; Yoo, Hong Hee

2012-01-01

In most mechanical systems, properties of the system elements have uncertainties due to several reasons. For example, mass, stiffness coefficient of a spring, damping coefficient of a damper or friction coefficients have uncertain characteristics. The uncertain characteristics of the elements have a direct effect on the system performance uncertainty. It is very important to estimate the performance uncertainty since the performance uncertainty is directly related to manufacturing yield and consumer satisfaction. Due to this reason, the performance uncertainty should be estimated accurately and considered in the system design. In this paper, performance measures are defined for nonlinear vibration systems and the performance measure uncertainties are estimated employing the first order reliability method (FORM). It was found that the FORM could provide good results in spite of the system nonlinear characteristics. Comparing to the results obtained by Monte Carlo Simulation (MCS), the accuracy of the uncertainty analysis results obtained by the FORM is validated
Reliability analysis of offshore structures using OMA based fatigue stresses

DEFF Research Database (Denmark)

Silva Nabuco, Bruna; Aissani, Amina; Glindtvad Tarpø, Marius

2017-01-01

focus is on the uncertainty observed on the different stresses used to predict the damage. This uncertainty can be reduced by Modal Based Fatigue Monitoring which is a technique based on continuously measuring of the accelerations in few points of the structure with the use of accelerometers known...... points of the structure, the stress history can be calculated in any arbitrary point of the structure. The accuracy of the estimated actual stress is analyzed by experimental tests on a scale model where the obtained stresses are compared to strain gauges measurements. After evaluating the fatigue...... stresses directly from the operational response of the structure, a reliability analysis is performed in order to estimate the reliability of using Modal Based Fatigue Monitoring for long term fatigue studies....
An Evidential Reasoning-Based CREAM to Human Reliability Analysis in Maritime Accident Process.

Science.gov (United States)

Wu, Bing; Yan, Xinping; Wang, Yang; Soares, C Guedes

2017-10-01

This article proposes a modified cognitive reliability and error analysis method (CREAM) for estimating the human error probability in the maritime accident process on the basis of an evidential reasoning approach. This modified CREAM is developed to precisely quantify the linguistic variables of the common performance conditions and to overcome the problem of ignoring the uncertainty caused by incomplete information in the existing CREAM models. Moreover, this article views maritime accident development from the sequential perspective, where a scenario- and barrier-based framework is proposed to describe the maritime accident process. This evidential reasoning-based CREAM approach together with the proposed accident development framework are applied to human reliability analysis of a ship capsizing accident. It will facilitate subjective human reliability analysis in different engineering systems where uncertainty exists in practice. © 2017 Society for Risk Analysis.
Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty

International Nuclear Information System (INIS)

Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro

2015-01-01

Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis
A possibilistic uncertainty model in classical reliability theory

International Nuclear Information System (INIS)

De Cooman, G.; Capelle, B.

1994-01-01

The authors argue that a possibilistic uncertainty model can be used to represent linguistic uncertainty about the states of a system and of its components. Furthermore, the basic properties of the application of this model to classical reliability theory are studied. The notion of the possibilistic reliability of a system or a component is defined. Based on the concept of a binary structure function, the important notion of a possibilistic function is introduced. It allows to calculate the possibilistic reliability of a system in terms of the possibilistic reliabilities of its components
Reliability of a new biokinetic model of zirconium in internal dosimetry: part I, parameter uncertainty analysis.

Science.gov (United States)

Li, Wei Bo; Greiter, Matthias; Oeh, Uwe; Hoeschen, Christoph

2011-12-01

The reliability of biokinetic models is essential in internal dose assessments and radiation risk analysis for the public, occupational workers, and patients exposed to radionuclides. In this paper, a method for assessing the reliability of biokinetic models by means of uncertainty and sensitivity analysis was developed. The paper is divided into two parts. In the first part of the study published here, the uncertainty sources of the model parameters for zirconium (Zr), developed by the International Commission on Radiological Protection (ICRP), were identified and analyzed. Furthermore, the uncertainty of the biokinetic experimental measurement performed at the Helmholtz Zentrum München-German Research Center for Environmental Health (HMGU) for developing a new biokinetic model of Zr was analyzed according to the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization. The confidence interval and distribution of model parameters of the ICRP and HMGU Zr biokinetic models were evaluated. As a result of computer biokinetic modelings, the mean, standard uncertainty, and confidence interval of model prediction calculated based on the model parameter uncertainty were presented and compared to the plasma clearance and urinary excretion measured after intravenous administration. It was shown that for the most important compartment, the plasma, the uncertainty evaluated for the HMGU model was much smaller than that for the ICRP model; that phenomenon was observed for other organs and tissues as well. The uncertainty of the integral of the radioactivity of Zr up to 50 y calculated by the HMGU model after ingestion by adult members of the public was shown to be smaller by a factor of two than that of the ICRP model. It was also shown that the distribution type of the model parameter strongly influences the model prediction, and the correlation of the model input parameters affects the model prediction to a
Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application

International Nuclear Information System (INIS)

Baraldi, Piero; Podofillini, Luca; Mkrtchyan, Lusine; Zio, Enrico; Dang, Vinh N.

2015-01-01

The use of expert systems can be helpful to improve the transparency and repeatability of assessments in areas of risk analysis with limited data available. In this field, human reliability analysis (HRA) is no exception, and, in particular, dependence analysis is an HRA task strongly based on analyst judgement. The analysis of dependence among Human Failure Events refers to the assessment of the effect of an earlier human failure on the probability of the subsequent ones. This paper analyses and compares two expert systems, based on Bayesian Belief Networks and Fuzzy Logic (a Fuzzy Expert System, FES), respectively. The comparison shows that a BBN approach should be preferred in all the cases characterized by quantifiable uncertainty in the input (i.e. when probability distributions can be assigned to describe the input parameters uncertainty), since it provides a satisfactory representation of the uncertainty and its output is directly interpretable for use within PSA. On the other hand, in cases characterized by very limited knowledge, an analyst may feel constrained by the probabilistic framework, which requires assigning probability distributions for describing uncertainty. In these cases, the FES seems to lead to a more transparent representation of the input and output uncertainty. - Highlights: • We analyse treatment of uncertainty in two expert systems. • We compare a Bayesian Belief Network (BBN) and a Fuzzy Expert System (FES). • We focus on the input assessment, inference engines and output assessment. • We focus on an application problem of interest for human reliability analysis. • We emphasize the application rather than math to reach non-BBN or FES specialists
Uncertainties related to the fault tree reliability data

International Nuclear Information System (INIS)

Apostol, Minodora; Nitoi, Mirela; Farcasiu, M.

2003-01-01

Uncertainty analyses related to the fault trees evaluate the system variability which appears from the uncertainties of the basic events probabilities. Having a logical model which describes a system, to obtain outcomes means to evaluate it, using estimations for each basic event of the model. If the model has basic events that incorporate uncertainties, then the results of the model should incorporate the uncertainties of the events. Uncertainties estimation in the final result of the fault tree means first the uncertainties evaluation for the basic event probabilities and then combination of these uncertainties, to calculate the top event uncertainty. To calculate the propagating uncertainty, a knowledge of the probability density function as well as the range of possible values of the basic event probabilities is required. The following data are defined, using suitable probability density function: the components failure rates; the human error probabilities; the initiating event frequencies. It was supposed that the possible value distribution of the basic event probabilities is given by the lognormal probability density function. To know the range of possible value of the basic event probabilities, the error factor or the uncertainty factor is required. The aim of this paper is to estimate the error factor for the failure rates and for the human errors probabilities from the reliability data base used in Cernavoda Probabilistic Safety Evaluation. The top event chosen as an example is FEED3, from the Pressure and Inventory Control System. The quantitative evaluation of this top event was made by using EDFT code, developed in Institute for Nuclear Research Pitesti (INR). It was supposed that the error factors for the component failures are the same as for the failure rates. Uncertainty analysis was made with INCERT application, which uses the moment method and Monte Carlo method. The reliability data base used at INR Pitesti does not contain the error factors (ef
Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

Directory of Open Access Journals (Sweden)

Shaopeng Zhong

2013-01-01

Full Text Available Focusing on the first-best marginal cost pricing (MCP in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1 the effect of stochastic perception error (SPE on the perceived travel time distribution and the components of road toll; (2 the effect of road toll on the actual travel time distribution and its reliability measures; (3 the effect of road toll on the total network travel time distribution and its statistics; and (4 the effect of travel demand level and the value of reliability (VoR level on the components of road toll.

A Proposal of Estimation Methodology to Improve Calculation Efficiency of Sampling-based Method in Nuclear Data Sensitivity and Uncertainty Analysis

International Nuclear Information System (INIS)

Song, Myung Sub; Kim, Song Hyun; Kim, Jong Kyung; Noh, Jae Man

2014-01-01

The uncertainty with the sampling-based method is evaluated by repeating transport calculations with a number of cross section data sampled from the covariance uncertainty data. In the transport calculation with the sampling-based method, the transport equation is not modified; therefore, all uncertainties of the responses such as k eff , reaction rates, flux and power distribution can be directly obtained all at one time without code modification. However, a major drawback with the sampling-based method is that it requires expensive computational load for statistically reliable results (inside confidence level 0.95) in the uncertainty analysis. The purpose of this study is to develop a method for improving the computational efficiency and obtaining highly reliable uncertainty result in using the sampling-based method with Monte Carlo simulation. The proposed method is a method to reduce the convergence time of the response uncertainty by using the multiple sets of sampled group cross sections in a single Monte Carlo simulation. The proposed method was verified by estimating GODIVA benchmark problem and the results were compared with that of conventional sampling-based method. In this study, sampling-based method based on central limit theorem is proposed to improve calculation efficiency by reducing the number of repetitive Monte Carlo transport calculation required to obtain reliable uncertainty analysis results. Each set of sampled group cross sections is assigned to each active cycle group in a single Monte Carlo simulation. The criticality uncertainty for the GODIVA problem is evaluated by the proposed and previous method. The results show that the proposed sampling-based method can efficiently decrease the number of Monte Carlo simulation required for evaluate uncertainty of k eff . It is expected that the proposed method will improve computational efficiency of uncertainty analysis with sampling-based method
Reliability analysis for new technology-based transmitters

Energy Technology Data Exchange (ETDEWEB)

Brissaud, Florent, E-mail: florent.brissaud.2007@utt.f [Institut National de l' Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte (France); Universite de Technologie de Troyes (UTT), Institut Charles Delaunay (ICD) and STMR UMR CNRS 6279, 12 rue Marie Curie, BP 2060, 10010 Troyes cedex (France); Barros, Anne; Berenguer, Christophe [Universite de Technologie de Troyes (UTT), Institut Charles Delaunay (ICD) and STMR UMR CNRS 6279, 12 rue Marie Curie, BP 2060, 10010 Troyes cedex (France); Charpentier, Dominique [Institut National de l' Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte (France)

2011-02-15

The reliability analysis of new technology-based transmitters has to deal with specific issues: various interactions between both material elements and functions, undefined behaviours under faulty conditions, several transmitted data, and little reliability feedback. To handle these particularities, a '3-step' model is proposed, based on goal tree-success tree (GTST) approaches to represent both the functional and material aspects, and includes the faults and failures as a third part for supporting reliability analyses. The behavioural aspects are provided by relationship matrices, also denoted master logic diagrams (MLD), with stochastic values which represent direct relationships between system elements. Relationship analyses are then proposed to assess the effect of any fault or failure on any material element or function. Taking these relationships into account, the probabilities of malfunction and failure modes are evaluated according to time. Furthermore, uncertainty analyses tend to show that even if the input data and system behaviour are not well known, these previous results can be obtained in a relatively precise way. An illustration is provided by a case study on an infrared gas transmitter. These properties make the proposed model and corresponding reliability analyses especially suitable for intelligent transmitters (or 'smart sensors').
Analysis of the influence of input data uncertainties on determining the reliability of reservoir storage capacity

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Marton Daniel

2015-12-01

Full Text Available The paper contains a sensitivity analysis of the influence of uncertainties in input hydrological, morphological and operating data required for a proposal for active reservoir conservation storage capacity and its achieved values. By introducing uncertainties into the considered inputs of the water management analysis of a reservoir, the subsequent analysed reservoir storage capacity is also affected with uncertainties. The values of water outflows from the reservoir and the hydrological reliabilities are affected with uncertainties as well. A simulation model of reservoir behaviour has been compiled with this kind of calculation as stated below. The model allows evaluation of the solution results, taking uncertainties into consideration, in contributing to a reduction in the occurrence of failure or lack of water during reservoir operation in low-water and dry periods.
Watershed reliability, resilience and vulnerability analysis under uncertainty using water quality data.

Science.gov (United States)

Hoque, Yamen M; Tripathi, Shivam; Hantush, Mohamed M; Govindaraju, Rao S

2012-10-30

A method for assessment of watershed health is developed by employing measures of reliability, resilience and vulnerability (R-R-V) using stream water quality data. Observed water quality data are usually sparse, so that a water quality time-series is often reconstructed using surrogate variables (streamflow). A Bayesian algorithm based on relevance vector machine (RVM) was employed to quantify the error in the reconstructed series, and a probabilistic assessment of watershed status was conducted based on established thresholds for various constituents. As an application example, observed water quality data for several constituents at different monitoring points within the Cedar Creek watershed in north-east Indiana (USA) were utilized. Considering uncertainty in the data for the period 2002-2007, the R-R-V analysis revealed that the Cedar Creek watershed tends to be in compliance with respect to selected pesticides, ammonia and total phosphorus. However, the watershed was found to be prone to violations of sediment standards. Ignoring uncertainty in the water quality time-series led to misleading results especially in the case of sediments. Results indicate that the methods presented in this study may be used for assessing the effects of different stressors over a watershed. The method shows promise as a management tool for assessing watershed health. Copyright © 2012 Elsevier Ltd. All rights reserved.
effect of uncertainty on the fatigue reliability of reinforced concrete ...

African Journals Online (AJOL)

In this paper, a reliability time-variant fatigue analysis and uncertainty effect on the serviceability of reinforced concrete bridge deck was carried out. A simply supported 15m bridge deck was specifically used for the investigation. Mathematical models were developed and the uncertainties in structural resistance, applied ...
Incorporating reliability evaluation into the uncertainty analysis of electricity market price

International Nuclear Information System (INIS)

Kang, Chongqing; Bai, Lichao; Xia, Qing; Jiang, Jianjian; Zhao, Jing

2005-01-01

A novel model and algorithm for analyzing the uncertainties in electricity market is proposed in this paper. In this model, bidding decision is formulated as a probabilistic model that takes into account the decision-maker's willingness to bid, risk preferences, the fluctuation of fuel-price, etc. At the same time, generating unit's uncertain output model is considered by its forced outage rate (FOR). Based on the model, the uncertainty of market price is then analyzed. Taking the analytical results into consideration, not only the reliability of the power system can be conventionally analyzed, but also the possible distribution of market prices can be easily obtained. The probability distribution of market prices can be further used to calculate the expected output and the sales income of generating unit in the market. Based on these results, it is also possible to evaluate the risk involved by generating units. A simple system with four generating units is used to illustrate the proposed algorithm. The proposed algorithm and the modeling technique are expected to helpful to the market participants in making their economic decisions
Reliability analysis of software based safety functions

International Nuclear Information System (INIS)

Pulkkinen, U.

1993-05-01

The methods applicable in the reliability analysis of software based safety functions are described in the report. Although the safety functions also include other components, the main emphasis in the report is on the reliability analysis of software. The check list type qualitative reliability analysis methods, such as failure mode and effects analysis (FMEA), are described, as well as the software fault tree analysis. The safety analysis based on the Petri nets is discussed. The most essential concepts and models of quantitative software reliability analysis are described. The most common software metrics and their combined use with software reliability models are discussed. The application of software reliability models in PSA is evaluated; it is observed that the recent software reliability models do not produce the estimates needed in PSA directly. As a result from the study some recommendations and conclusions are drawn. The need of formal methods in the analysis and development of software based systems, the applicability of qualitative reliability engineering methods in connection to PSA and the need to make more precise the requirements for software based systems and their analyses in the regulatory guides should be mentioned. (orig.). (46 refs., 13 figs., 1 tab.)
Reliability analysis of shutdown system

International Nuclear Information System (INIS)

Kumar, C. Senthil; John Arul, A.; Pal Singh, Om; Suryaprakasa Rao, K.

2005-01-01

This paper presents the results of reliability analysis of Shutdown System (SDS) of Indian Prototype Fast Breeder Reactor. Reliability analysis carried out using Fault Tree Analysis predicts a value of 3.5 x 10 -8 /de for failure of shutdown function in case of global faults and 4.4 x 10 -8 /de for local faults. Based on 20 de/y, the frequency of shutdown function failure is 0.7 x 10 -6 /ry, which meets the reliability target, set by the Indian Atomic Energy Regulatory Board. The reliability is limited by Common Cause Failure (CCF) of actuation part of SDS and to a lesser extent CCF of electronic components. The failure frequency of individual systems is -3 /ry, which also meets the safety criteria. Uncertainty analysis indicates a maximum error factor of 5 for the top event unavailability
Structural hybrid reliability index and its convergent solving method based on random–fuzzy–interval reliability model

OpenAIRE

Hai An; Ling Zhou; Hui Sun

2016-01-01

Aiming to resolve the problems of a variety of uncertainty variables that coexist in the engineering structure reliability analysis, a new hybrid reliability index to evaluate structural hybrid reliability, based on the random–fuzzy–interval model, is proposed in this article. The convergent solving method is also presented. First, the truncated probability reliability model, the fuzzy random reliability model, and the non-probabilistic interval reliability model are introduced. Then, the new...
Uncertainty analysis methods for estimation of reliability of passive system of VHTR

International Nuclear Information System (INIS)

Han, S.J.

2012-01-01

An estimation of reliability of passive system for the probabilistic safety assessment (PSA) of a very high temperature reactor (VHTR) is under development in Korea. The essential approach of this estimation is to measure the uncertainty of the system performance under a specific accident condition. The uncertainty propagation approach according to the simulation of phenomenological models (computer codes) is adopted as a typical method to estimate the uncertainty for this purpose. This presentation introduced the uncertainty propagation and discussed the related issues focusing on the propagation object and its surrogates. To achieve a sufficient level of depth of uncertainty results, the applicability of the propagation should be carefully reviewed. For an example study, Latin-hypercube sampling (LHS) method as a direct propagation was tested for a specific accident sequence of VHTR. The reactor cavity cooling system (RCCS) developed by KAERI was considered for this example study. This is an air-cooled type passive system that has no active components for its operation. The accident sequence is a low pressure conduction cooling (LPCC) accident that is considered as a design basis accident for the safety design of VHTR. This sequence is due to a large failure of the pressure boundary of the reactor system such as a guillotine break of coolant pipe lines. The presentation discussed the obtained insights (benefit and weakness) to apply an estimation of reliability of passive system
Adjoint sensitivity analysis of dynamic reliability models based on Markov chains - I: Theory

International Nuclear Information System (INIS)

Cacuci, D. G.; Cacuci, D. G.; Ionescu-Bujor, M.

2008-01-01

The development of the adjoint sensitivity analysis procedure (ASAP) for generic dynamic reliability models based on Markov chains is presented, together with applications of this procedure to the analysis of several systems of increasing complexity. The general theory is presented in Part I of this work and is accompanied by a paradigm application to the dynamic reliability analysis of a simple binary component, namely a pump functioning on an 'up/down' cycle until it fails irreparably. This paradigm example admits a closed form analytical solution, which permits a clear illustration of the main characteristics of the ASAP for Markov chains. In particular, it is shown that the ASAP for Markov chains presents outstanding computational advantages over other procedures currently in use for sensitivity and uncertainty analysis of the dynamic reliability of large-scale systems. This conclusion is further underscored by the large-scale applications presented in Part II. (authors)
Adjoint sensitivity analysis of dynamic reliability models based on Markov chains - I: Theory

Energy Technology Data Exchange (ETDEWEB)

Cacuci, D. G. [Commiss Energy Atom, Direct Energy Nucl, Saclay, (France); Cacuci, D. G. [Univ Karlsruhe, Inst Nucl Technol and Reactor Safety, D-76021 Karlsruhe, (Germany); Ionescu-Bujor, M. [Forschungszentrum Karlsruhe, Fus Program, D-76021 Karlsruhe, (Germany)

2008-07-01

The development of the adjoint sensitivity analysis procedure (ASAP) for generic dynamic reliability models based on Markov chains is presented, together with applications of this procedure to the analysis of several systems of increasing complexity. The general theory is presented in Part I of this work and is accompanied by a paradigm application to the dynamic reliability analysis of a simple binary component, namely a pump functioning on an 'up/down' cycle until it fails irreparably. This paradigm example admits a closed form analytical solution, which permits a clear illustration of the main characteristics of the ASAP for Markov chains. In particular, it is shown that the ASAP for Markov chains presents outstanding computational advantages over other procedures currently in use for sensitivity and uncertainty analysis of the dynamic reliability of large-scale systems. This conclusion is further underscored by the large-scale applications presented in Part II. (authors)
Screening, sensitivity, and uncertainty for the CREAM method of Human Reliability Analysis

International Nuclear Information System (INIS)

Bedford, Tim; Bayley, Clare; Revie, Matthew

2013-01-01

This paper reports a sensitivity analysis of the Cognitive Reliability and Error Analysis Method for Human Reliability Analysis. We consider three different aspects: the difference between the outputs of the Basic and Extended methods, on the same HRA scenario; the variability in outputs through the choices made for common performance conditions (CPCs); and the variability in outputs through the assignment of choices for cognitive function failures (CFFs). We discuss the problem of interpreting categories when applying the method, compare its quantitative structure to that of first generation methods and discuss also how dependence is modelled with the approach. We show that the control mode intervals used in the Basic method are too narrow to be consistent with the Extended method. This motivates a new screening method that gives improved accuracy with respect to the Basic method, in the sense that (on average) halves the uncertainty associated with the Basic method. We make some observations on the design of a screening method that are generally applicable in Risk Analysis. Finally, we propose a new method of combining CPC weights with nominal probabilities so that the calculated probabilities are always in range (i.e. between 0 and 1), while satisfying sensible properties that are consistent with the overall CREAM method
Survey of sampling-based methods for uncertainty and sensitivity analysis

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; Sallaberry, C.J.; Storlie, C.B.

2006-01-01

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) definition of probability distributions to characterize epistemic uncertainty in analysis inputs (ii) generation of samples from uncertain analysis inputs (iii) propagation of sampled inputs through an analysis (iv) presentation of uncertainty analysis results, and (v) determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two-dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition
Survey of sampling-based methods for uncertainty and sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD. (.; .); Storlie, Curt B. (Colorado State University, Fort Collins, CO)

2006-06-01

Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.
An integrated reliability-based design optimization of offshore towers

International Nuclear Information System (INIS)

Karadeniz, Halil; Togan, Vedat; Vrouwenvelder, Ton

2009-01-01

After recognizing the uncertainty in the parameters such as material, loading, geometry and so on in contrast with the conventional optimization, the reliability-based design optimization (RBDO) concept has become more meaningful to perform an economical design implementation, which includes a reliability analysis and an optimization algorithm. RBDO procedures include structural analysis, reliability analysis and sensitivity analysis both for optimization and for reliability. The efficiency of the RBDO system depends on the mentioned numerical algorithms. In this work, an integrated algorithms system is proposed to implement the RBDO of the offshore towers, which are subjected to the extreme wave loading. The numerical strategies interacting with each other to fulfill the RBDO of towers are as follows: (a) a structural analysis program, SAPOS, (b) an optimization program, SQP and (c) a reliability analysis program based on FORM. A demonstration of an example tripod tower under the reliability constraints based on limit states of the critical stress, buckling and the natural frequency is presented.
An integrated reliability-based design optimization of offshore towers

Energy Technology Data Exchange (ETDEWEB)

Karadeniz, Halil [Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft (Netherlands)], E-mail: h.karadeniz@tudelft.nl; Togan, Vedat [Department of Civil Engineering, Karadeniz Technical University, Trabzon (Turkey); Vrouwenvelder, Ton [Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft (Netherlands)

2009-10-15

After recognizing the uncertainty in the parameters such as material, loading, geometry and so on in contrast with the conventional optimization, the reliability-based design optimization (RBDO) concept has become more meaningful to perform an economical design implementation, which includes a reliability analysis and an optimization algorithm. RBDO procedures include structural analysis, reliability analysis and sensitivity analysis both for optimization and for reliability. The efficiency of the RBDO system depends on the mentioned numerical algorithms. In this work, an integrated algorithms system is proposed to implement the RBDO of the offshore towers, which are subjected to the extreme wave loading. The numerical strategies interacting with each other to fulfill the RBDO of towers are as follows: (a) a structural analysis program, SAPOS, (b) an optimization program, SQP and (c) a reliability analysis program based on FORM. A demonstration of an example tripod tower under the reliability constraints based on limit states of the critical stress, buckling and the natural frequency is presented.
Uncertainty analysis and validation of environmental models. The empirically based uncertainty analysis

International Nuclear Information System (INIS)

Monte, Luigi; Hakanson, Lars; Bergstroem, Ulla; Brittain, John; Heling, Rudie

1996-01-01

The principles of Empirically Based Uncertainty Analysis (EBUA) are described. EBUA is based on the evaluation of 'performance indices' that express the level of agreement between the model and sets of empirical independent data collected in different experimental circumstances. Some of these indices may be used to evaluate the confidence limits of the model output. The method is based on the statistical analysis of the distribution of the index values and on the quantitative relationship of these values with the ratio 'experimental data/model output'. Some performance indices are described in the present paper. Among these, the so-called 'functional distance' (d) between the logarithm of model output and the logarithm of the experimental data, defined as d 2 =Σ n 1 ( ln M i - ln O i ) 2 /n where M i is the i-th experimental value, O i the corresponding model evaluation and n the number of the couplets 'experimental value, predicted value', is an important tool for the EBUA method. From the statistical distribution of this performance index, it is possible to infer the characteristics of the distribution of the ratio 'experimental data/model output' and, consequently to evaluate the confidence limits for the model predictions. This method was applied to calculate the uncertainty level of a model developed to predict the migration of radiocaesium in lacustrine systems. Unfortunately, performance indices are affected by the uncertainty of the experimental data used in validation. Indeed, measurement results of environmental levels of contamination are generally associated with large uncertainty due to the measurement and sampling techniques and to the large variability in space and time of the measured quantities. It is demonstrated that this non-desired effect, in some circumstances, may be corrected by means of simple formulae
Reliability Analysis of Free Jet Scour Below Dams

Directory of Open Access Journals (Sweden)

Chuanqi Li

2012-12-01

Full Text Available Current formulas for calculating scour depth below of a free over fall are mostly deterministic in nature and do not adequately consider the uncertainties of various scouring parameters. A reliability-based assessment of scour, taking into account uncertainties of parameters and coefficients involved, should be performed. This paper studies the reliability of a dam foundation under the threat of scour. A model for calculating the reliability of scour and estimating the probability of failure of the dam foundation subjected to scour is presented. The Maximum Entropy Method is applied to construct the probability density function (PDF of the performance function subject to the moment constraints. Monte Carlo simulation (MCS is applied for uncertainty analysis. An example is considered, and there liability of its scour is computed, the influence of various random variables on the probability failure is analyzed.
Uncertainty evaluation of reliability of shutdown system of a medium size fast breeder reactor

Energy Technology Data Exchange (ETDEWEB)

Zeliang, Chireuding; Singh, Om Pal, E-mail: singhop@iitk.ac.in; Munshi, Prabhat

2016-11-15

Highlights: • Uncertainty analysis of reliability of Shutdown System is carried out. • Monte Carlo method of sampling is used. • The effect of various reliability improvement measures of SDS are accounted. - Abstract: In this paper, results are presented on the uncertainty evaluation of the reliability of Shutdown System (SDS) of a Medium Size Fast Breeder Reactor (MSFBR). The reliability analysis results are of Kumar et al. (2005). The failure rate of the components of SDS are taken from International literature and it is assumed that these follow log-normal distribution. Fault tree method is employed to propagate the uncertainty in failure rate from components level to shutdown system level. The beta factor model is used to account different extent of diversity. The Monte Carlo sampling technique is used for the analysis. The results of uncertainty analysis are presented in terms of the probability density function, cumulative distribution function, mean, variance, percentile values, confidence intervals, etc. It is observed that the spread in the probability distribution of SDS failure rate is less than SDS components failure rate and ninety percent values of the failure rate of SDS falls below the target value. As generic values of failure rates are used, sensitivity analysis is performed with respect to failure rate of control and safety rods and beta factor. It is discovered that a large increase in failure rate of SDS rods is not carried to SDS system failure proportionately. The failure rate of SDS is very sensitive to the beta factor of common cause failure between the two systems of SDS. The results of the study provide insight in the propagation of uncertainty in the failure rate of SDS components to failure rate of shutdown system.

A Review: Passive System Reliability Analysis – Accomplishments and Unresolved Issues

Energy Technology Data Exchange (ETDEWEB)

Nayak, Arun Kumar, E-mail: arunths@barc.gov.in [Reactor Engineering Division, Reactor Design and Development Group, Bhabha Atomic Research Centre, Mumbai (India); Chandrakar, Amit [Homi Bhabha National Institute, Mumbai (India); Vinod, Gopika [Reactor Safety Division, Reactor Design and Development Group, Bhabha Atomic Research Centre, Mumbai (India)

2014-10-10

Reliability assessment of passive safety systems is one of the important issues, since safety of advanced nuclear reactors rely on several passive features. In this context, a few methodologies such as reliability evaluation of passive safety system (REPAS), reliability methods for passive safety functions (RMPS), and analysis of passive systems reliability (APSRA) have been developed in the past. These methodologies have been used to assess reliability of various passive safety systems. While these methodologies have certain features in common, but they differ in considering certain issues; for example, treatment of model uncertainties, deviation of geometric, and process parameters from their nominal values. This paper presents the state of the art on passive system reliability assessment methodologies, the accomplishments, and remaining issues. In this review, three critical issues pertaining to passive systems performance and reliability have been identified. The first issue is applicability of best estimate codes and model uncertainty. The best estimate codes based phenomenological simulations of natural convection passive systems could have significant amount of uncertainties, these uncertainties must be incorporated in appropriate manner in the performance and reliability analysis of such systems. The second issue is the treatment of dynamic failure characteristics of components of passive systems. REPAS, RMPS, and APSRA methodologies do not consider dynamic failures of components or process, which may have strong influence on the failure of passive systems. The influence of dynamic failure characteristics of components on system failure probability is presented with the help of a dynamic reliability methodology based on Monte Carlo simulation. The analysis of a benchmark problem of Hold-up tank shows the error in failure probability estimation by not considering the dynamism of components. It is thus suggested that dynamic reliability methodologies must be
Reliability-based failure cause assessment of collapsed bridge during construction

International Nuclear Information System (INIS)

Choi, Hyun-Ho; Lee, Sang-Yoon; Choi, Il-Yoon; Cho, Hyo-Nam; Mahadevan, Sankaran

2006-01-01

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures. Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach
Model uncertainty and multimodel inference in reliability estimation within a longitudinal framework.

Science.gov (United States)

Alonso, Ariel; Laenen, Annouschka

2013-05-01

Laenen, Alonso, and Molenberghs (2007) and Laenen, Alonso, Molenberghs, and Vangeneugden (2009) proposed a method to assess the reliability of rating scales in a longitudinal context. The methodology is based on hierarchical linear models, and reliability coefficients are derived from the corresponding covariance matrices. However, finding a good parsimonious model to describe complex longitudinal data is a challenging task. Frequently, several models fit the data equally well, raising the problem of model selection uncertainty. When model uncertainty is high one may resort to model averaging, where inferences are based not on one but on an entire set of models. We explored the use of different model building strategies, including model averaging, in reliability estimation. We found that the approach introduced by Laenen et al. (2007, 2009) combined with some of these strategies may yield meaningful results in the presence of high model selection uncertainty and when all models are misspecified, in so far as some of them manage to capture the most salient features of the data. Nonetheless, when all models omit prominent regularities in the data, misleading results may be obtained. The main ideas are further illustrated on a case study in which the reliability of the Hamilton Anxiety Rating Scale is estimated. Importantly, the ambit of model selection uncertainty and model averaging transcends the specific setting studied in the paper and may be of interest in other areas of psychometrics. © 2012 The British Psychological Society.
Neglect Of Parameter Estimation Uncertainty Can Significantly Overestimate Structural Reliability

Directory of Open Access Journals (Sweden)

Rózsás Árpád

2015-12-01

Full Text Available Parameter estimation uncertainty is often neglected in reliability studies, i.e. point estimates of distribution parameters are used for representative fractiles, and in probabilistic models. A numerical example examines the effect of this uncertainty on structural reliability using Bayesian statistics. The study reveals that the neglect of parameter estimation uncertainty might lead to an order of magnitude underestimation of failure probability.
Reliability Analysis of a Steel Frame

Directory of Open Access Journals (Sweden)

M. Sýkora

2002-01-01

Full Text Available A steel frame with haunches is designed according to Eurocodes. The frame is exposed to self-weight, snow, and wind actions. Lateral-torsional buckling appears to represent the most critical criterion, which is considered as a basis for the limit state function. In the reliability analysis, the probabilistic models proposed by the Joint Committee for Structural Safety (JCSS are used for basic variables. The uncertainty model coefficients take into account the inaccuracy of the resistance model for the haunched girder and the inaccuracy of the action effect model. The time invariant reliability analysis is based on Turkstra's rule for combinations of snow and wind actions. The time variant analysis describes snow and wind actions by jump processes with intermittencies. Assuming a 50-year lifetime, the obtained values of the reliability index b vary within the range from 3.95 up to 5.56. The cross-profile IPE 330 designed according to Eurocodes seems to be adequate. It appears that the time invariant reliability analysis based on Turkstra's rule provides considerably lower values of b than those obtained by the time variant analysis.
An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

Science.gov (United States)

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-01-01

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC
A Research Roadmap for Computation-Based Human Reliability Analysis

Energy Technology Data Exchange (ETDEWEB)

Boring, Ronald [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Joe, Jeffrey [Idaho National Lab. (INL), Idaho Falls, ID (United States); Smith, Curtis [Idaho National Lab. (INL), Idaho Falls, ID (United States); Groth, Katrina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-08-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is often secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.
A Research Roadmap for Computation-Based Human Reliability Analysis

International Nuclear Information System (INIS)

Boring, Ronald; Mandelli, Diego; Joe, Jeffrey; Smith, Curtis; Groth, Katrina

2015-01-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is often secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.
Reliability-based design code calibration for concrete containment structures

International Nuclear Information System (INIS)

Han, B.K.; Cho, H.N.; Chang, S.P.

1991-01-01

In this study, a load combination criteria for design and a probability-based reliability analysis were proposed on the basis of a FEM-based random vibration analysis. The limit state model defined for the study is a serviceability limit state of the crack failure that causes the emission of radioactive materials, and the results are compared with the case of strength limit state. More accurate reliability analyses under various dynamic loads such as earthquake loads were made possible by incorporating the FEM and random vibration theory, which is different from the conventional reliability analysis method. The uncertainties in loads and resistance available in Korea and the references were adapted to the situation of Korea, and especially in case of earthquake, the design earthquake was assessed based on the available data for the probabilistic description of earthquake ground acceleration in the Korea peninsula. The SAP V-2 is used for a three-dimensional finite element analysis of concrete containment structure, and the reliability analysis is carried out by modifying HRAS reliability analysis program for this study. (orig./GL)
Uncertainty in reliability estimation : when do we know everything we know?

NARCIS (Netherlands)

Houben, M.J.H.A.; Sonnemans, P.J.M.; Newby, M.J.; Bris, R.; Guedes Soares, C.; Martorell, S.

2009-01-01

In this paperwe demonstrate the use of an adapted GroundedTheory approach through interviews and their analysis to determine explicit uncertainty (known unknowns) for reliability estimation in the early phases of product development.We have applied the adapted Grounded Theory approach in a case
Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

International Nuclear Information System (INIS)

Dong, Feifei; Liu, Yong; Su, Han; Zou, Rui; Guo, Huaicheng

2015-01-01

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to
Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

Energy Technology Data Exchange (ETDEWEB)

Dong, Feifei [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Liu, Yong, E-mail: yongliu@pku.edu.cn [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Institute of Water Sciences, Peking University, Beijing 100871 (China); Su, Han [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Zou, Rui [Tetra Tech, Inc., 10306 Eaton Place, Ste 340, Fairfax, VA 22030 (United States); Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Kunming 650034 (China); Guo, Huaicheng [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China)

2015-05-15

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to
Quantification of Wave Model Uncertainties Used for Probabilistic Reliability Assessments of Wave Energy Converters

DEFF Research Database (Denmark)

Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

2015-01-01

Wave models used for site assessments are subjected to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Four different wave models are considered, and validation...... data are collected from published scientific research. The bias and the root-mean-square error, as well as the scatter index, are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example, this paper presents how the quantified...... uncertainties can be implemented in probabilistic reliability assessments....
Determination of Wave Model Uncertainties used for Probabilistic Reliability Assessments of Wave Energy Devices

DEFF Research Database (Denmark)

Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

2014-01-01

Wave models used for site assessments are subject to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Considered are four different wave models and validation...... data is collected from published scientific research. The bias, the root-mean-square error as well as the scatter index are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example it is shown how the estimated uncertainties can...... be implemented in probabilistic reliability assessments....
A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties

Science.gov (United States)

Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie

2017-09-01

Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.
Reliability-based design of wind turbine blades

DEFF Research Database (Denmark)

Toft, Henrik Stensgaard; Sørensen, John Dalsgaard

2011-01-01

Reliability-based design of wind turbine blades requires identification of the important failure modes/limit states along with stochastic models for the uncertainties and methods for estimating the reliability. In the present paper it is described how reliability-based design can be applied to wi...
Uncertainty Analysis of Few Group Cross Sections Based on Generalized Perturbation Theory

International Nuclear Information System (INIS)

Han, Tae Young; Lee, Hyun Chul; Noh, Jae Man

2014-01-01

In this paper, the methodology of the sensitivity and uncertainty analysis code based on GPT was described and the preliminary verification calculations on the PMR200 pin cell problem were carried out. As a result, they are in a good agreement when compared with the results by TSUNAMI. From this study, it is expected that MUSAD code based on GPT can produce the uncertainty of the homogenized few group microscopic cross sections for a core simulator. For sensitivity and uncertainty analyses for general core responses, a two-step method is available and it utilizes the generalized perturbation theory (GPT) for homogenized few group cross sections in the first step and stochastic sampling method for general core responses in the second step. The uncertainty analysis procedure based on GPT in the first step needs the generalized adjoint solution from a cell or lattice code. For this, the generalized adjoint solver has been integrated into DeCART in our previous work. In this paper, MUSAD (Modues of Uncertainty and Sensitivity Analysis for DeCART) code based on the classical perturbation theory was expanded to the function of the sensitivity and uncertainty analysis for few group cross sections based on GPT. First, the uncertainty analysis method based on GPT was described and, in the next section, the preliminary results of the verification calculation on a VHTR pin cell problem were compared with the results by TSUNAMI of SCALE 6.1
Fault-tolerant embedded system design and optimization considering reliability estimation uncertainty

International Nuclear Information System (INIS)

Wattanapongskorn, Naruemon; Coit, David W.

2007-01-01

In this paper, we model embedded system design and optimization, considering component redundancy and uncertainty in the component reliability estimates. The systems being studied consist of software embedded in associated hardware components. Very often, component reliability values are not known exactly. Therefore, for reliability analysis studies and system optimization, it is meaningful to consider component reliability estimates as random variables with associated estimation uncertainty. In this new research, the system design process is formulated as a multiple-objective optimization problem to maximize an estimate of system reliability, and also, to minimize the variance of the reliability estimate. The two objectives are combined by penalizing the variance for prospective solutions. The two most common fault-tolerant embedded system architectures, N-Version Programming and Recovery Block, are considered as strategies to improve system reliability by providing system redundancy. Four distinct models are presented to demonstrate the proposed optimization techniques with or without redundancy. For many design problems, multiple functionally equivalent software versions have failure correlation even if they have been independently developed. The failure correlation may result from faults in the software specification, faults from a voting algorithm, and/or related faults from any two software versions. Our approach considers this correlation in formulating practical optimization models. Genetic algorithms with a dynamic penalty function are applied in solving this optimization problem, and reasonable and interesting results are obtained and discussed
Structural reliability in context of statistical uncertainties and modelling discrepancies

International Nuclear Information System (INIS)

Pendola, Maurice

2000-01-01

Structural reliability methods have been largely improved during the last years and have showed their ability to deal with uncertainties during the design stage or to optimize the functioning and the maintenance of industrial installations. They are based on a mechanical modeling of the structural behavior according to the considered failure modes and on a probabilistic representation of input parameters of this modeling. In practice, only limited statistical information is available to build the probabilistic representation and different sophistication levels of the mechanical modeling may be introduced. Thus, besides the physical randomness, other uncertainties occur in such analyses. The aim of this work is triple: 1. at first, to propose a methodology able to characterize the statistical uncertainties due to the limited number of data in order to take them into account in the reliability analyses. The obtained reliability index measures the confidence in the structure considering the statistical information available. 2. Then, to show a methodology leading to reliability results evaluated from a particular mechanical modeling but by using a less sophisticated one. The objective is then to decrease the computational efforts required by the reference modeling. 3. Finally, to propose partial safety factors that are evolving as a function of the number of statistical data available and as a function of the sophistication level of the mechanical modeling that is used. The concepts are illustrated in the case of a welded pipe and in the case of a natural draught cooling tower. The results show the interest of the methodologies in an industrial context. [fr
Application of Metric-based Software Reliability Analysis to Example Software

International Nuclear Information System (INIS)

Kim, Man Cheol; Smidts, Carol

2008-07-01

The software reliability of TELLERFAST ATM software is analyzed by using two metric-based software reliability analysis methods, a state transition diagram-based method and a test coverage-based method. The procedures for the software reliability analysis by using the two methods and the analysis results are provided in this report. It is found that the two methods have a relation of complementary cooperation, and therefore further researches on combining the two methods to reflect the benefit of the complementary cooperative effect to the software reliability analysis are recommended

Selected examples of practical approaches for the assessment of model reliability - parameter uncertainty analysis

International Nuclear Information System (INIS)

Hofer, E.; Hoffman, F.O.

1987-02-01

The uncertainty analysis of model predictions has to discriminate between two fundamentally different types of uncertainty. The presence of stochastic variability (Type 1 uncertainty) necessitates the use of a probabilistic model instead of the much simpler deterministic one. Lack of knowledge (Type 2 uncertainty), however, applies to deterministic as well as to probabilistic model predictions and often dominates over uncertainties of Type 1. The term ''probability'' is interpreted differently in the probabilistic analysis of either type of uncertainty. After these discriminations have been explained the discussion centers on the propagation of parameter uncertainties through the model, the derivation of quantitative uncertainty statements for model predictions and the presentation and interpretation of the results of a Type 2 uncertainty analysis. Various alternative approaches are compared for a very simple deterministic model
Uncertainty analysis and design optimization of hybrid rocket motor powered vehicle for suborbital flight

Directory of Open Access Journals (Sweden)

Zhu Hao

2015-06-01

Full Text Available In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor (HRM powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity analysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization (DDO and uncertainty-based design optimization (UDO are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation (KMCS and Kriging-based Taylor series approximation (KTSA, are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods: DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.
FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D SUBJECTED TO INTERNAL PRESSURE

Directory of Open Access Journals (Sweden)

Entin Hartini

2016-06-01

Full Text Available ABSTRACT FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D SUBJECTED TO INTERNAL PRESSURE. The reactor pressure vessel (RPV is a pressure boundary in the PWR type reactor which serves to confine radioactive material during chain reaction process. The integrity of the RPV must be guaranteed either in a normal operation or accident conditions. In analyzing the integrity of RPV, especially related to the crack behavior which can introduce break to the reactor pressure vessel, a fracture mechanic approach should be taken for this assessment. The uncertainty of input used in the assessment, such as mechanical properties and physical environment, becomes a reason that the assessment is not sufficient if it is perfomed only by deterministic approach. Therefore, the uncertainty approach should be applied. The aim of this study is to analize the uncertainty of fracture mechanics calculations in evaluating the reliability of PWR`s reactor pressure vessel. Random character of input quantity was generated using probabilistic principles and theories. Fracture mechanics analysis is solved by Finite Element Method (FEM with MSC MARC software, while uncertainty input analysis is done based on probability density function with Latin Hypercube Sampling (LHS using python script. The output of MSC MARC is a J-integral value, which is converted into stress intensity factor for evaluating the reliability of RPV’s 2D. From the result of the calculation, it can be concluded that the SIF from probabilistic method, reached the limit value of fracture toughness earlier than SIF from deterministic method. The SIF generated by the probabilistic method is 105.240 MPa m0.5. Meanwhile, the SIF generated by deterministic method is 100.876 MPa m0.5. Keywords: Uncertainty analysis, fracture mechanics, LHS, FEM, reactor pressure vessels ABSTRAK ANALISIS KETIDAKPASTIAN FRACTURE MECHANIC PADA EVALUASI KEANDALAN
Reliability of Coulomb stress changes inferred from correlated uncertainties of finite-fault source models

KAUST Repository

Woessner, J.

2012-07-14

Static stress transfer is one physical mechanism to explain triggered seismicity. Coseismic stress-change calculations strongly depend on the parameterization of the causative finite-fault source model. These models are uncertain due to uncertainties in input data, model assumptions, and modeling procedures. However, fault model uncertainties have usually been ignored in stress-triggering studies and have not been propagated to assess the reliability of Coulomb failure stress change (ΔCFS) calculations. We show how these uncertainties can be used to provide confidence intervals for co-seismic ΔCFS-values. We demonstrate this for the MW = 5.9 June 2000 Kleifarvatn earthquake in southwest Iceland and systematically map these uncertainties. A set of 2500 candidate source models from the full posterior fault-parameter distribution was used to compute 2500 ΔCFS maps. We assess the reliability of the ΔCFS-values from the coefficient of variation (CV) and deem ΔCFS-values to be reliable where they are at least twice as large as the standard deviation (CV ≤ 0.5). Unreliable ΔCFS-values are found near the causative fault and between lobes of positive and negative stress change, where a small change in fault strike causes ΔCFS-values to change sign. The most reliable ΔCFS-values are found away from the source fault in the middle of positive and negative ΔCFS-lobes, a likely general pattern. Using the reliability criterion, our results support the static stress-triggering hypothesis. Nevertheless, our analysis also suggests that results from previous stress-triggering studies not considering source model uncertainties may have lead to a biased interpretation of the importance of static stress-triggering.
Hybrid time-variant reliability estimation for active control structures under aleatory and epistemic uncertainties

Science.gov (United States)

Wang, Lei; Xiong, Chuang; Wang, Xiaojun; Li, Yunlong; Xu, Menghui

2018-04-01

Considering that multi-source uncertainties from inherent nature as well as the external environment are unavoidable and severely affect the controller performance, the dynamic safety assessment with high confidence is of great significance for scientists and engineers. In view of this, the uncertainty quantification analysis and time-variant reliability estimation corresponding to the closed-loop control problems are conducted in this study under a mixture of random, interval, and convex uncertainties. By combining the state-space transformation and the natural set expansion, the boundary laws of controlled response histories are first confirmed with specific implementation of random items. For nonlinear cases, the collocation set methodology and fourth Rounge-Kutta algorithm are introduced as well. Enlightened by the first-passage model in random process theory as well as by the static probabilistic reliability ideas, a new definition of the hybrid time-variant reliability measurement is provided for the vibration control systems and the related solution details are further expounded. Two engineering examples are eventually presented to demonstrate the validity and applicability of the methodology developed.
Structural hybrid reliability index and its convergent solving method based on random–fuzzy–interval reliability model

Directory of Open Access Journals (Sweden)

Hai An

2016-08-01

Full Text Available Aiming to resolve the problems of a variety of uncertainty variables that coexist in the engineering structure reliability analysis, a new hybrid reliability index to evaluate structural hybrid reliability, based on the random–fuzzy–interval model, is proposed in this article. The convergent solving method is also presented. First, the truncated probability reliability model, the fuzzy random reliability model, and the non-probabilistic interval reliability model are introduced. Then, the new hybrid reliability index definition is presented based on the random–fuzzy–interval model. Furthermore, the calculation flowchart of the hybrid reliability index is presented and it is solved using the modified limit-step length iterative algorithm, which ensures convergence. And the validity of convergent algorithm for the hybrid reliability model is verified through the calculation examples in literature. In the end, a numerical example is demonstrated to show that the hybrid reliability index is applicable for the wear reliability assessment of mechanisms, where truncated random variables, fuzzy random variables, and interval variables coexist. The demonstration also shows the good convergence of the iterative algorithm proposed in this article.
Reliability assessment of complex electromechanical systems under epistemic uncertainty

International Nuclear Information System (INIS)

Mi, Jinhua; Li, Yan-Feng; Yang, Yuan-Jian; Peng, Weiwen; Huang, Hong-Zhong

2016-01-01

The appearance of macro-engineering and mega-project have led to the increasing complexity of modern electromechanical systems (EMSs). The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems. Uncertainty, dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments, lack of data and random interference. This paper presents a comprehensive study on the reliability assessment of complex systems. In view of the dynamic characteristics within the system, it makes use of the advantages of the dynamic fault tree (DFT) for characterizing system behaviors. The lifetime of system units can be expressed as bounded closed intervals by incorporating field failures, test data and design expertize. Then the coefficient of variation (COV) method is employed to estimate the parameters of life distributions. An extended probability-box (P-Box) is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data. By mapping the DFT into an equivalent Bayesian network (BN), relevant reliability parameters and indexes have been calculated. Furthermore, the Monte Carlo (MC) simulation method is utilized to compute the DFT model with consideration of system replacement policy. The results show that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems. - Highlights: • A comprehensive study on the reliability assessment of complex system is presented. • An extended probability-box is proposed to convey the present of epistemic uncertainty. • The dynamic fault tree model is built. • Bayesian network and Monte Carlo simulation methods are used. • The reliability assessment of a complex electromechanical system is performed.
Approach to uncertainty evaluation for safety analysis

International Nuclear Information System (INIS)

Ogura, Katsunori

2005-01-01

Nuclear power plant safety used to be verified and confirmed through accident simulations using computer codes generally because it is very difficult to perform integrated experiments or tests for the verification and validation of the plant safety due to radioactive consequence, cost, and scaling to the actual plant. Traditionally the plant safety had been secured owing to the sufficient safety margin through the conservative assumptions and models to be applied to those simulations. Meanwhile the best-estimate analysis based on the realistic assumptions and models in support of the accumulated insights could be performed recently, inducing the reduction of safety margin in the analysis results and the increase of necessity to evaluate the reliability or uncertainty of the analysis results. This paper introduces an approach to evaluate the uncertainty of accident simulation and its results. (Note: This research had been done not in the Japan Nuclear Energy Safety Organization but in the Tokyo Institute of Technology.) (author)
Nuclear data sensitivity/uncertainty analysis for XT-ADS

International Nuclear Information System (INIS)

Sugawara, Takanori; Sarotto, Massimo; Stankovskiy, Alexey; Van den Eynde, Gert

2011-01-01

Highlights: → The sensitivity and uncertainty analyses were performed to comprehend the reliability of the XT-ADS neutronic design. → The uncertainties deduced from the covariance data for the XT-ADS criticality were 0.94%, 1.9% and 1.1% by the SCALE 44-group, TENDL-2009 and JENDL-3.3 data, respectively. → When the target accuracy of 0.3%Δk for the criticality was considered, the uncertainties did not satisfy it. → To achieve this accuracy, the uncertainties should be improved by experiments under an adequate condition. - Abstract: The XT-ADS, an accelerator-driven system for an experimental demonstration, has been investigated in the framework of IP EUROTRANS FP6 project. In this study, the sensitivity and uncertainty analyses were performed to comprehend the reliability of the XT-ADS neutronic design. For the sensitivity analysis, it was found that the sensitivity coefficients were significantly different by changing the geometry models and calculation codes. For the uncertainty analysis, it was confirmed that the uncertainties deduced from the covariance data varied significantly by changing them. The uncertainties deduced from the covariance data for the XT-ADS criticality were 0.94%, 1.9% and 1.1% by the SCALE 44-group, TENDL-2009 and JENDL-3.3 data, respectively. When the target accuracy of 0.3%Δk for the criticality was considered, the uncertainties did not satisfy it. To achieve this accuracy, the uncertainties should be improved by experiments under an adequate condition.
Reliability-Based Topology Optimization Using Stochastic Response Surface Method with Sparse Grid Design

Directory of Open Access Journals (Sweden)

Qinghai Zhao

2015-01-01

Full Text Available A mathematical framework is developed which integrates the reliability concept into topology optimization to solve reliability-based topology optimization (RBTO problems under uncertainty. Two typical methodologies have been presented and implemented, including the performance measure approach (PMA and the sequential optimization and reliability assessment (SORA. To enhance the computational efficiency of reliability analysis, stochastic response surface method (SRSM is applied to approximate the true limit state function with respect to the normalized random variables, combined with the reasonable design of experiments generated by sparse grid design, which was proven to be an effective and special discretization technique. The uncertainties such as material property and external loads are considered on three numerical examples: a cantilever beam, a loaded knee structure, and a heat conduction problem. Monte-Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach. Based on the results, it is demonstrated that application of SRSM with SGD can produce an efficient reliability analysis in RBTO which enables a more reliable design than that obtained by DTO. It is also found that, under identical accuracy, SORA is superior to PMA in view of computational efficiency.
Managing the uncertainty aspect of reliability in an iterative product development process

NARCIS (Netherlands)

Ganesh, N.

2009-01-01

This study identifies the design criteria for a method that can be used to manage the risk and uncertainty aspects of product reliability of Really New Innovations (RNI) in an Iterative Product Development Process (IPDP). It is based on 7 years of longitudinal research exploring more than 10
Reliability analysis of steel-containment strength

International Nuclear Information System (INIS)

Greimann, L.G.; Fanous, F.; Wold-Tinsae, A.; Ketalaar, D.; Lin, T.; Bluhm, D.

1982-06-01

A best estimate and uncertainty assessment of the resistance of the St. Lucie, Cherokee, Perry, WPPSS and Browns Ferry containment vessels was performed. The Monte Carlo simulation technique and second moment approach were compared as a means of calculating the probability distribution of the containment resistance. A uniform static internal pressure was used and strain ductility was taken as the failure criterion. Approximate methods were developed and calibrated with finite element analysis. Both approximate and finite element analyses were performed on the axisymmetric containment structure. An uncertainty assessment of the containment strength was then performed by the second moment reliability method. Based upon the approximate methods, the cumulative distribution for the resistance of each of the five containments (shell modes only) is presented
System Reliability Analysis Considering Correlation of Performances

Energy Technology Data Exchange (ETDEWEB)

Kim, Saekyeol; Lee, Tae Hee [Hanyang Univ., Seoul (Korea, Republic of); Lim, Woochul [Mando Corporation, Seongnam (Korea, Republic of)

2017-04-15

Reliability analysis of a mechanical system has been developed in order to consider the uncertainties in the product design that may occur from the tolerance of design variables, uncertainties of noise, environmental factors, and material properties. In most of the previous studies, the reliability was calculated independently for each performance of the system. However, the conventional methods cannot consider the correlation between the performances of the system that may lead to a difference between the reliability of the entire system and the reliability of the individual performance. In this paper, the joint probability density function (PDF) of the performances is modeled using a copula which takes into account the correlation between performances of the system. The system reliability is proposed as the integral of joint PDF of performances and is compared with the individual reliability of each performance by mathematical examples and two-bar truss example.
System Reliability Analysis Considering Correlation of Performances

International Nuclear Information System (INIS)

Kim, Saekyeol; Lee, Tae Hee; Lim, Woochul

2017-01-01

Reliability analysis of a mechanical system has been developed in order to consider the uncertainties in the product design that may occur from the tolerance of design variables, uncertainties of noise, environmental factors, and material properties. In most of the previous studies, the reliability was calculated independently for each performance of the system. However, the conventional methods cannot consider the correlation between the performances of the system that may lead to a difference between the reliability of the entire system and the reliability of the individual performance. In this paper, the joint probability density function (PDF) of the performances is modeled using a copula which takes into account the correlation between performances of the system. The system reliability is proposed as the integral of joint PDF of performances and is compared with the individual reliability of each performance by mathematical examples and two-bar truss example.
Reliability analysis of prestressed concrete containment structures

International Nuclear Information System (INIS)

Jiang, J.; Zhao, Y.; Sun, J.

1993-01-01

The reliability analysis of prestressed concrete containment structures subjected to combinations of static and dynamic loads with consideration of uncertainties of structural and load parameters is presented. Limit state probabilities for given parameters are calculated using the procedure developed at BNL, while that with consideration of parameter uncertainties are calculated by a fast integration for time variant structural reliability. The limit state surface of the prestressed concrete containment is constructed directly incorporating the prestress. The sensitivities of the Choleskey decomposition matrix and the natural vibration character are calculated by simplified procedures. (author)
Practical reliability and uncertainty quantification in complex systems : final report.

Energy Technology Data Exchange (ETDEWEB)

Grace, Matthew D.; Ringland, James T.; Marzouk, Youssef M. (Massachusetts Institute of Technology, Cambridge, MA); Boggs, Paul T.; Zurn, Rena M.; Diegert, Kathleen V. (Sandia National Laboratories, Albuquerque, NM); Pebay, Philippe Pierre; Red-Horse, John Robert (Sandia National Laboratories, Albuquerque, NM)

2009-09-01

The purpose of this project was to investigate the use of Bayesian methods for the estimation of the reliability of complex systems. The goals were to find methods for dealing with continuous data, rather than simple pass/fail data; to avoid assumptions of specific probability distributions, especially Gaussian, or normal, distributions; to compute not only an estimate of the reliability of the system, but also a measure of the confidence in that estimate; to develop procedures to address time-dependent or aging aspects in such systems, and to use these models and results to derive optimal testing strategies. The system is assumed to be a system of systems, i.e., a system with discrete components that are themselves systems. Furthermore, the system is 'engineered' in the sense that each node is designed to do something and that we have a mathematical description of that process. In the time-dependent case, the assumption is that we have a general, nonlinear, time-dependent function describing the process. The major results of the project are described in this report. In summary, we developed a sophisticated mathematical framework based on modern probability theory and Bayesian analysis. This framework encompasses all aspects of epistemic uncertainty and easily incorporates steady-state and time-dependent systems. Based on Markov chain, Monte Carlo methods, we devised a computational strategy for general probability density estimation in the steady-state case. This enabled us to compute a distribution of the reliability from which many questions, including confidence, could be addressed. We then extended this to the time domain and implemented procedures to estimate the reliability over time, including the use of the method to predict the reliability at a future time. Finally, we used certain aspects of Bayesian decision analysis to create a novel method for determining an optimal testing strategy, e.g., we can estimate the 'best' location to
Reliability-Based Robustness Analysis for a Croatian Sports Hall

DEFF Research Database (Denmark)

Čizmar, Dean; Kirkegaard, Poul Henning; Sørensen, John Dalsgaard

2011-01-01

This paper presents a probabilistic approach for structural robustness assessment for a timber structure built a few years ago. The robustness analysis is based on a structural reliability based framework for robustness and a simplified mechanical system modelling of a timber truss system....... A complex timber structure with a large number of failure modes is modelled with only a few dominant failure modes. First, a component based robustness analysis is performed based on the reliability indices of the remaining elements after the removal of selected critical elements. The robustness...... is expressed and evaluated by a robustness index. Next, the robustness is assessed using system reliability indices where the probabilistic failure model is modelled by a series system of parallel systems....
A Preliminary Study on Sensitivity and Uncertainty Analysis with Statistic Method: Uncertainty Analysis with Cross Section Sampling from Lognormal Distribution

Energy Technology Data Exchange (ETDEWEB)

Song, Myung Sub; Kim, Song Hyun; Kim, Jong Kyung [Hanyang Univ., Seoul (Korea, Republic of); Noh, Jae Man [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2013-10-15

The uncertainty evaluation with statistical method is performed by repetition of transport calculation with sampling the directly perturbed nuclear data. Hence, the reliable uncertainty result can be obtained by analyzing the results of the numerous transport calculations. One of the problems in the uncertainty analysis with the statistical approach is known as that the cross section sampling from the normal (Gaussian) distribution with relatively large standard deviation leads to the sampling error of the cross sections such as the sampling of the negative cross section. Some collection methods are noted; however, the methods can distort the distribution of the sampled cross sections. In this study, a sampling method of the nuclear data is proposed by using lognormal distribution. After that, the criticality calculations with sampled nuclear data are performed and the results are compared with that from the normal distribution which is conventionally used in the previous studies. In this study, the statistical sampling method of the cross section with the lognormal distribution was proposed to increase the sampling accuracy without negative sampling error. Also, a stochastic cross section sampling and writing program was developed. For the sensitivity and uncertainty analysis, the cross section sampling was pursued with the normal and lognormal distribution. The uncertainties, which are caused by covariance of (n,.) cross sections, were evaluated by solving GODIVA problem. The results show that the sampling method with lognormal distribution can efficiently solve the negative sampling problem referred in the previous studies. It is expected that this study will contribute to increase the accuracy of the sampling-based uncertainty analysis.
A Preliminary Study on Sensitivity and Uncertainty Analysis with Statistic Method: Uncertainty Analysis with Cross Section Sampling from Lognormal Distribution

International Nuclear Information System (INIS)

Song, Myung Sub; Kim, Song Hyun; Kim, Jong Kyung; Noh, Jae Man

2013-01-01

The uncertainty evaluation with statistical method is performed by repetition of transport calculation with sampling the directly perturbed nuclear data. Hence, the reliable uncertainty result can be obtained by analyzing the results of the numerous transport calculations. One of the problems in the uncertainty analysis with the statistical approach is known as that the cross section sampling from the normal (Gaussian) distribution with relatively large standard deviation leads to the sampling error of the cross sections such as the sampling of the negative cross section. Some collection methods are noted; however, the methods can distort the distribution of the sampled cross sections. In this study, a sampling method of the nuclear data is proposed by using lognormal distribution. After that, the criticality calculations with sampled nuclear data are performed and the results are compared with that from the normal distribution which is conventionally used in the previous studies. In this study, the statistical sampling method of the cross section with the lognormal distribution was proposed to increase the sampling accuracy without negative sampling error. Also, a stochastic cross section sampling and writing program was developed. For the sensitivity and uncertainty analysis, the cross section sampling was pursued with the normal and lognormal distribution. The uncertainties, which are caused by covariance of (n,.) cross sections, were evaluated by solving GODIVA problem. The results show that the sampling method with lognormal distribution can efficiently solve the negative sampling problem referred in the previous studies. It is expected that this study will contribute to increase the accuracy of the sampling-based uncertainty analysis
Uncertainties in model-based outcome predictions for treatment planning

International Nuclear Information System (INIS)

Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

2001-01-01

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

Developing safety performance functions incorporating reliability-based risk measures.

Science.gov (United States)

Ibrahim, Shewkar El-Bassiouni; Sayed, Tarek

2011-11-01

Current geometric design guides provide deterministic standards where the safety margin of the design output is generally unknown and there is little knowledge of the safety implications of deviating from these standards. Several studies have advocated probabilistic geometric design where reliability analysis can be used to account for the uncertainty in the design parameters and to provide a risk measure of the implication of deviation from design standards. However, there is currently no link between measures of design reliability and the quantification of safety using collision frequency. The analysis presented in this paper attempts to bridge this gap by incorporating a reliability-based quantitative risk measure such as the probability of non-compliance (P(nc)) in safety performance functions (SPFs). Establishing this link will allow admitting reliability-based design into traditional benefit-cost analysis and should lead to a wider application of the reliability technique in road design. The present application is concerned with the design of horizontal curves, where the limit state function is defined in terms of the available (supply) and stopping (demand) sight distances. A comprehensive collision and geometric design database of two-lane rural highways is used to investigate the effect of the probability of non-compliance on safety. The reliability analysis was carried out using the First Order Reliability Method (FORM). Two Negative Binomial (NB) SPFs were developed to compare models with and without the reliability-based risk measures. It was found that models incorporating the P(nc) provided a better fit to the data set than the traditional (without risk) NB SPFs for total, injury and fatality (I+F) and property damage only (PDO) collisions. Copyright © 2011 Elsevier Ltd. All rights reserved.
Time-dependent reliability sensitivity analysis of motion mechanisms

International Nuclear Information System (INIS)

Wei, Pengfei; Song, Jingwen; Lu, Zhenzhou; Yue, Zhufeng

2016-01-01

Reliability sensitivity analysis aims at identifying the source of structure/mechanism failure, and quantifying the effects of each random source or their distribution parameters on failure probability or reliability. In this paper, the time-dependent parametric reliability sensitivity (PRS) analysis as well as the global reliability sensitivity (GRS) analysis is introduced for the motion mechanisms. The PRS indices are defined as the partial derivatives of the time-dependent reliability w.r.t. the distribution parameters of each random input variable, and they quantify the effect of the small change of each distribution parameter on the time-dependent reliability. The GRS indices are defined for quantifying the individual, interaction and total contributions of the uncertainty in each random input variable to the time-dependent reliability. The envelope function method combined with the first order approximation of the motion error function is introduced for efficiently estimating the time-dependent PRS and GRS indices. Both the time-dependent PRS and GRS analysis techniques can be especially useful for reliability-based design. This significance of the proposed methods as well as the effectiveness of the envelope function method for estimating the time-dependent PRS and GRS indices are demonstrated with a four-bar mechanism and a car rack-and-pinion steering linkage. - Highlights: • Time-dependent parametric reliability sensitivity analysis is presented. • Time-dependent global reliability sensitivity analysis is presented for mechanisms. • The proposed method is especially useful for enhancing the kinematic reliability. • An envelope method is introduced for efficiently implementing the proposed methods. • The proposed method is demonstrated by two real planar mechanisms.
Enhancing product robustness in reliability-based design optimization

International Nuclear Information System (INIS)

Zhuang, Xiaotian; Pan, Rong; Du, Xiaoping

2015-01-01

Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example. - Highlights: • A unifying framework for integrating RBDO and RPD is proposed. • Implicit product performance function is considered. • The design problem is solved by sequential optimization and reliability assessment. • A sequential sampling technique is developed for improving design optimization. • The comparison with traditional RBDO is provided
Thermal-Hydraulic Analysis for SBLOCA in OPR1000 and Evaluation of Uncertainty for PSA

International Nuclear Information System (INIS)

Kim, Tae Jin; Park, Goon Cherl

2012-01-01

Probabilistic Safety assessment (PSA) is a mathematical tool to evaluate numerical estimates of risk for nuclear power plants (NPPs). But PSA has the problems about quality and reliability since the quantification of uncertainties from thermal hydraulic (TH) analysis has not been included in the quantification of overall uncertainties in PSA. From the former research, it is proved that the quantification of uncertainties from best-estimate LBLOCA analysis can improve the PSA quality by modifying the core damage frequency (CDF) from the existing PSA report. Basing on the similar concept, this study considers the quantification of SBLOCA analysis results. In this study, however, operator error parameters are also included in addition to the phenomenon parameters which are considered in LBLOCA analysis
A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

Science.gov (United States)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.
Reliability analysis of water distribution systems under uncertainty

International Nuclear Information System (INIS)

Kansal, M.L.; Kumar, Arun; Sharma, P.B.

1995-01-01

In most of the developing countries, the Water Distribution Networks (WDN) are of intermittent type because of the shortage of safe drinking water. Failure of a pipeline(s) in such cases will cause not only the fall in one or more nodal heads but also the poor connectivity of source with various demand nodes of the system. Most of the previous works have used the two-step algorithm based on pathset or cutset approach for connectivity analysis. The computations become more cumbersome when connectivity of all demand nodes taken together with that of supply is carried out. In the present paper, network connectivity based on the concept of Appended Spanning Tree (AST) is suggested to compute global network connectivity which is defined as the probability of the source node being connected with all the demand nodes simultaneously. The concept of AST has distinct advantages as it attacks the problem directly rather than in an indirect way as most of the studies so far have done. Since the water distribution system is a repairable one, a general expression for pipeline avialability using the failure/repair rate is considered. Furthermore, the sensitivity of global reliability estimates due to the likely error in the estimation of failure/repair rates of various pipelines is also studied
Effect of Uncertainties in Physical Property Estimates on Process Design - Sensitivity Analysis

DEFF Research Database (Denmark)

Hukkerikar, Amol; Jones, Mark Nicholas; Sin, Gürkan

for performing sensitivity of process design subject to uncertainties in the property estimates. To this end, first uncertainty analysis of the property models of pure components and their mixtures was performed in order to obtain the uncertainties in the estimated property values. As a next step, sensitivity......Chemical process design calculations require accurate and reliable physical and thermodynamic property data and property models of pure components and their mixtures in order to obtain reliable design parameters which help to achieve desired specifications. The uncertainties in the property values...... can arise from the experiments itself or from the property models employed. It is important to consider the effect of these uncertainties on the process design in order to assess the quality and reliability of the final design. The main objective of this work is to develop a systematic methodology...
A Proposal on the Advanced Sampling Based Sensitivity and Uncertainty Analysis Method for the Eigenvalue Uncertainty Analysis

International Nuclear Information System (INIS)

Kim, Song Hyun; Song, Myung Sub; Shin, Chang Ho; Noh, Jae Man

2014-01-01

In using the perturbation theory, the uncertainty of the response can be estimated by a single transport simulation, and therefore it requires small computational load. However, it has a disadvantage that the computation methodology must be modified whenever estimating different response type such as multiplication factor, flux, or power distribution. Hence, it is suitable for analyzing few responses with lots of perturbed parameters. Statistical approach is a sampling based method which uses randomly sampled cross sections from covariance data for analyzing the uncertainty of the response. XSUSA is a code based on the statistical approach. The cross sections are only modified with the sampling based method; thus, general transport codes can be directly utilized for the S/U analysis without any code modifications. However, to calculate the uncertainty distribution from the result, code simulation should be enough repeated with randomly sampled cross sections. Therefore, this inefficiency is known as a disadvantage of the stochastic method. In this study, an advanced sampling method of the cross sections is proposed and verified to increase the estimation efficiency of the sampling based method. In this study, to increase the estimation efficiency of the sampling based S/U method, an advanced sampling and estimation method was proposed. The main feature of the proposed method is that the cross section averaged from each single sampled cross section is used. For the use of the proposed method, the validation was performed using the perturbation theory
Uncertainty Analysis of Seebeck Coefficient and Electrical Resistivity Characterization

Science.gov (United States)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

In order to provide a complete description of a materials thermoelectric power factor, in addition to the measured nominal value, an uncertainty interval is required. The uncertainty may contain sources of measurement error including systematic bias error and precision error of a statistical nature. The work focuses specifically on the popular ZEM-3 (Ulvac Technologies) measurement system, but the methods apply to any measurement system. The analysis accounts for sources of systematic error including sample preparation tolerance, measurement probe placement, thermocouple cold-finger effect, and measurement parameters; in addition to including uncertainty of a statistical nature. Complete uncertainty analysis of a measurement system allows for more reliable comparison of measurement data between laboratories.
Deterministic uncertainty analysis

International Nuclear Information System (INIS)

Worley, B.A.

1987-12-01

This paper presents a deterministic uncertainty analysis (DUA) method for calculating uncertainties that has the potential to significantly reduce the number of computer runs compared to conventional statistical analysis. The method is based upon the availability of derivative and sensitivity data such as that calculated using the well known direct or adjoint sensitivity analysis techniques. Formation of response surfaces using derivative data and the propagation of input probability distributions are discussed relative to their role in the DUA method. A sample problem that models the flow of water through a borehole is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. Propogation of uncertainties by the DUA method is compared for ten cases in which the number of reference model runs was varied from one to ten. The DUA method gives a more accurate representation of the true cumulative distribution of the flow rate based upon as few as two model executions compared to fifty model executions using a statistical approach. 16 refs., 4 figs., 5 tabs
Reliability analysis of digital based I and C system

Energy Technology Data Exchange (ETDEWEB)

Kang, I. S.; Cho, B. S.; Choi, M. J. [KOPEC, Yongin (Korea, Republic of)

1999-10-01

Rapidly, digital technology is being widely applied in replacing analog component installed in existing plant and designing new nuclear power plant for control and monitoring system in Korea as well as in foreign countries. Even though many merits of digital technology, it is being faced with a new problem of reliability assurance. The studies for solving this problem are being performed vigorously in foreign countries. The reliability of KNGR Engineered Safety Features Component Control System (ESF-CCS), digital based I and C system, was analyzed to verify fulfillment of the ALWR EPRI-URD requirement for reliability analysis and eliminate hazards in design applied new technology. The qualitative analysis using FMEA and quantitative analysis using reliability block diagram were performed. The results of analyses are shown in this paper.
Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

Science.gov (United States)

Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh

2018-01-01

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor 0.91, NSE>0.89, and 0.18analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.
Reliability-Based Stability Analysis of Rock Slopes Using Numerical Analysis and Response Surface Method

Science.gov (United States)

Dadashzadeh, N.; Duzgun, H. S. B.; Yesiloglu-Gultekin, N.

2017-08-01

While advanced numerical techniques in slope stability analysis are successfully used in deterministic studies, they have so far found limited use in probabilistic analyses due to their high computation cost. The first-order reliability method (FORM) is one of the most efficient probabilistic techniques to perform probabilistic stability analysis by considering the associated uncertainties in the analysis parameters. However, it is not possible to directly use FORM in numerical slope stability evaluations as it requires definition of a limit state performance function. In this study, an integrated methodology for probabilistic numerical modeling of rock slope stability is proposed. The methodology is based on response surface method, where FORM is used to develop an explicit performance function from the results of numerical simulations. The implementation of the proposed methodology is performed by considering a large potential rock wedge in Sumela Monastery, Turkey. The accuracy of the developed performance function to truly represent the limit state surface is evaluated by monitoring the slope behavior. The calculated probability of failure is compared with Monte Carlo simulation (MCS) method. The proposed methodology is found to be 72% more efficient than MCS, while the accuracy is decreased with an error of 24%.
Status of XSUSA for sampling based nuclear data uncertainty and sensitivity analysis

International Nuclear Information System (INIS)

Zwermann, W.; Gallner, L.; Klein, M.; Krzydacz-Hausmann; Pasichnyk, I.; Pautz, A.; Velkov, K.

2013-01-01

In the present contribution, an overview of the sampling based XSUSA method for sensitivity and uncertainty analysis with respect to nuclear data is given. The focus is on recent developments and applications of XSUSA. These applications include calculations for critical assemblies, fuel assembly depletion calculations, and steady state as well as transient reactor core calculations. The analyses are partially performed in the framework of international benchmark working groups (UACSA - Uncertainty Analyses for Criticality Safety Assessment, UAM - Uncertainty Analysis in Modelling). It is demonstrated that particularly for full-scale reactor calculations the influence of the nuclear data uncertainties on the results can be substantial. For instance, for the radial fission rate distributions of mixed UO 2 /MOX light water reactor cores, the 2σ uncertainties in the core centre and periphery can reach values exceeding 10%. For a fast transient, the resulting time behaviour of the reactor power was covered by a wide uncertainty band. Overall, the results confirm the necessity of adding systematic uncertainty analyses to best-estimate reactor calculations. (authors)
Reliability analysis of cluster-based ad-hoc networks

International Nuclear Information System (INIS)

Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel

2008-01-01

The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks
Fatigue Reliability Analysis of Wind Turbine Cast Components

DEFF Research Database (Denmark)

Rafsanjani, Hesam Mirzaei; Sørensen, John Dalsgaard; Fæster, Søren

2017-01-01

.) and to quantify the relevant uncertainties using available fatigue tests. Illustrative results are presented as obtained by statistical analysis of a large set of fatigue data for casted test components typically used for wind turbines. Furthermore, the SN curves (fatigue life curves based on applied stress......The fatigue life of wind turbine cast components, such as the main shaft in a drivetrain, is generally determined by defects from the casting process. These defects may reduce the fatigue life and they are generally distributed randomly in components. The foundries, cutting facilities and test...... facilities can affect the verification of properties by testing. Hence, it is important to have a tool to identify which foundry, cutting and/or test facility produces components which, based on the relevant uncertainties, have the largest expected fatigue life or, alternatively, have the largest reliability...
Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis

Directory of Open Access Journals (Sweden)

Wenjun Bai

2018-02-01

Full Text Available Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners’ emotional stability in jail, can be directly benefited from our framework. Abstract: To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e.,Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty—which can be quantified in Uncertainty Flow—is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our
Reliability-based design optimization via high order response surface method

International Nuclear Information System (INIS)

Li, Hong Shuang

2013-01-01

To reduce the computational effort of reliability-based design optimization (RBDO), the response surface method (RSM) has been widely used to evaluate reliability constraints. We propose an efficient methodology for solving RBDO problems based on an improved high order response surface method (HORSM) that takes advantage of an efficient sampling method, Hermite polynomials and uncertainty contribution concept to construct a high order response surface function with cross terms for reliability analysis. The sampling method generates supporting points from Gauss-Hermite quadrature points, which can be used to approximate response surface function without cross terms, to identify the highest order of each random variable and to determine the significant variables connected with point estimate method. The cross terms between two significant random variables are added to the response surface function to improve the approximation accuracy. Integrating the nested strategy, the improved HORSM is explored in solving RBDO problems. Additionally, a sampling based reliability sensitivity analysis method is employed to reduce the computational effort further when design variables are distributional parameters of input random variables. The proposed methodology is applied on two test problems to validate its accuracy and efficiency. The proposed methodology is more efficient than first order reliability method based RBDO and Monte Carlo simulation based RBDO, and enables the use of RBDO as a practical design tool.
A new measure of uncertainty importance based on distributional sensitivity analysis for PSA

International Nuclear Information System (INIS)

Han, Seok Jung; Tak, Nam Il; Chun, Moon Hyun

1996-01-01

The main objective of the present study is to propose a new measure of uncertainty importance based on distributional sensitivity analysis. The new measure is developed to utilize a metric distance obtained from cumulative distribution functions (cdfs). The measure is evaluated for two cases: one is a cdf given by a known analytical distribution and the other given by an empirical distribution generated by a crude Monte Carlo simulation. To study its applicability, the present measure has been applied to two different cases. The results are compared with those of existing three methods. The present approach is a useful measure of uncertainty importance which is based on cdfs. This method is simple and easy to calculate uncertainty importance without any complex process. On the basis of the results obtained in the present work, the present method is recommended to be used as a tool for the analysis of uncertainty importance
Uncertainty Assessment of Hydrological Frequency Analysis Using Bootstrap Method

Directory of Open Access Journals (Sweden)

Yi-Ming Hu

2013-01-01

Full Text Available The hydrological frequency analysis (HFA is the foundation for the hydraulic engineering design and water resources management. Hydrological extreme observations or samples are the basis for HFA; the representativeness of a sample series to the population distribution is extremely important for the estimation reliability of the hydrological design value or quantile. However, for most of hydrological extreme data obtained in practical application, the size of the samples is usually small, for example, in China about 40~50 years. Generally, samples with small size cannot completely display the statistical properties of the population distribution, thus leading to uncertainties in the estimation of hydrological design values. In this paper, a new method based on bootstrap is put forward to analyze the impact of sampling uncertainty on the design value. By bootstrap resampling technique, a large number of bootstrap samples are constructed from the original flood extreme observations; the corresponding design value or quantile is estimated for each bootstrap sample, so that the sampling distribution of design value is constructed; based on the sampling distribution, the uncertainty of quantile estimation can be quantified. Compared with the conventional approach, this method provides not only the point estimation of a design value but also quantitative evaluation on uncertainties of the estimation.

effect of uncertainty on the fatigue reliability of reinforced concrete

African Journals Online (AJOL)

user

2016-07-03

Jul 3, 2016 ... Keywords: Fatigue, cracks, structural reliability, uncertainties, high stress loads. 1. INTRODUCTION ... infrastructure system, are extremely vulnerable to this action of fatigue. .... Shear in the deck beam, G(x3) is the equation for.
Model uncertainty in safety assessment

International Nuclear Information System (INIS)

Pulkkinen, U.; Huovinen, T.

1996-01-01

The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.)
Model uncertainty in safety assessment

Energy Technology Data Exchange (ETDEWEB)

Pulkkinen, U; Huovinen, T [VTT Automation, Espoo (Finland). Industrial Automation

1996-01-01

The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.).
Structural reliability analysis based on the cokriging technique

International Nuclear Information System (INIS)

Zhao Wei; Wang Wei; Dai Hongzhe; Xue Guofeng

2010-01-01

Approximation methods are widely used in structural reliability analysis because they are simple to create and provide explicit functional relationships between the responses and variables in stead of the implicit limit state function. Recently, the kriging method which is a semi-parameter interpolation technique that can be used for deterministic optimization and structural reliability has gained popularity. However, to fully exploit the kriging method, especially in high-dimensional problems, a large number of sample points should be generated to fill the design space and this can be very expensive and even impractical in practical engineering analysis. Therefore, in this paper, a new method-the cokriging method, which is an extension of kriging, is proposed to calculate the structural reliability. cokriging approximation incorporates secondary information such as the values of the gradients of the function being approximated. This paper explores the use of the cokriging method for structural reliability problems by comparing it with the Kriging method based on some numerical examples. The results indicate that the cokriging procedure described in this work can generate approximation models to improve on the accuracy and efficiency for structural reliability problems and is a viable alternative to the kriging.
Validation and assessment of uncertainty of chemical tests as a tool for the reliability analysis of wastewater IPEN

International Nuclear Information System (INIS)

Silva, Renan A.; Martins, Elaine A.J.; Furusawa, Helio A.

2011-01-01

The validation of analytical methods has become an indispensable tool for the analysis in chemical laboratories, including being required for such accreditation. However, even if a laboratory using validated methods of analysis there is the possibility that these methods generate results discrepant with reality by making necessary the addition of a quantitative attribute (a value) which indicates the degree of certainty the extent or the analytical method used. This measure assigned to the result of measurement is called measurement uncertainty. We estimate this uncertainty with a level of confidence both direction, an analytical result has limited significance if not carried out proper assessment of its uncertainty. One of the activities of this work was to elaborate a program to help the validation and evaluation of uncertainty in chemical analysis. The program was developed with Visual Basic programming language and method of evaluation of uncertainty introduced the following concepts based on the GUM (Guide to the Expression of Uncertainty in Measurement). This evaluation program uncertainty measurement will be applied to chemical analysis in support of the characterization of the Nuclear Fuel Cycle developed by IPEN and the study of organic substances in wastewater associated with professional activities of the Institute. In the first case, primarily for the determination of total uranium and the second case for substances that were generated by human activities and that are contained in resolution 357/2005. As strategy for development of this work was considered the PDCA cycle to improve the efficiency of each step and minimize errors while performing the experimental part. The program should be validated to meet requirements of standards such as, for example, the standard ISO/IEC 17025. The application, it is projected to use in other analytical procedures of both the Nuclear Fuel Cycle and in the control program and chemical waste management of IPEN
Validation and assessment of uncertainty of chemical tests as a tool for the reliability analysis of wastewater IPEN

Energy Technology Data Exchange (ETDEWEB)

Silva, Renan A.; Martins, Elaine A.J.; Furusawa, Helio A., E-mail: elaine@ipen.br, E-mail: helioaf@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2011-07-01

The validation of analytical methods has become an indispensable tool for the analysis in chemical laboratories, including being required for such accreditation. However, even if a laboratory using validated methods of analysis there is the possibility that these methods generate results discrepant with reality by making necessary the addition of a quantitative attribute (a value) which indicates the degree of certainty the extent or the analytical method used. This measure assigned to the result of measurement is called measurement uncertainty. We estimate this uncertainty with a level of confidence both direction, an analytical result has limited significance if not carried out proper assessment of its uncertainty. One of the activities of this work was to elaborate a program to help the validation and evaluation of uncertainty in chemical analysis. The program was developed with Visual Basic programming language and method of evaluation of uncertainty introduced the following concepts based on the GUM (Guide to the Expression of Uncertainty in Measurement). This evaluation program uncertainty measurement will be applied to chemical analysis in support of the characterization of the Nuclear Fuel Cycle developed by IPEN and the study of organic substances in wastewater associated with professional activities of the Institute. In the first case, primarily for the determination of total uranium and the second case for substances that were generated by human activities and that are contained in resolution 357/2005. As strategy for development of this work was considered the PDCA cycle to improve the efficiency of each step and minimize errors while performing the experimental part. The program should be validated to meet requirements of standards such as, for example, the standard ISO/IEC 17025. The application, it is projected to use in other analytical procedures of both the Nuclear Fuel Cycle and in the control program and chemical waste management of IPEN
An Energy-Based Limit State Function for Estimation of Structural Reliability in Shock Environments

Directory of Open Access Journals (Sweden)

Michael A. Guthrie

2013-01-01

Full Text Available limit state function is developed for the estimation of structural reliability in shock environments. This limit state function uses peak modal strain energies to characterize environmental severity and modal strain energies at failure to characterize the structural capacity. The Hasofer-Lind reliability index is briefly reviewed and its computation for the energy-based limit state function is discussed. Applications to two degree of freedom mass-spring systems and to a simple finite element model are considered. For these examples, computation of the reliability index requires little effort beyond a modal analysis, but still accounts for relevant uncertainties in both the structure and environment. For both examples, the reliability index is observed to agree well with the results of Monte Carlo analysis. In situations where fast, qualitative comparison of several candidate designs is required, the reliability index based on the proposed limit state function provides an attractive metric which can be used to compare and control reliability.
Sensitivity analysis in a structural reliability context

International Nuclear Information System (INIS)

Lemaitre, Paul

2014-01-01

This thesis' subject is sensitivity analysis in a structural reliability context. The general framework is the study of a deterministic numerical model that allows to reproduce a complex physical phenomenon. The aim of a reliability study is to estimate the failure probability of the system from the numerical model and the uncertainties of the inputs. In this context, the quantification of the impact of the uncertainty of each input parameter on the output might be of interest. This step is called sensitivity analysis. Many scientific works deal with this topic but not in the reliability scope. This thesis' aim is to test existing sensitivity analysis methods, and to propose more efficient original methods. A bibliographical step on sensitivity analysis on one hand and on the estimation of small failure probabilities on the other hand is first proposed. This step raises the need to develop appropriate techniques. Two variables ranking methods are then explored. The first one proposes to make use of binary classifiers (random forests). The second one measures the departure, at each step of a subset method, between each input original density and the density given the subset reached. A more general and original methodology reflecting the impact of the input density modification on the failure probability is then explored. The proposed methods are then applied on the CWNR case, which motivates this thesis. (author)
Sampling based uncertainty analysis of 10% hot leg break LOCA in large scale test facility

International Nuclear Information System (INIS)

Sengupta, Samiran; Kraina, V.; Dubey, S. K.; Rao, R. S.; Gupta, S. K.

2010-01-01

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between 5 th and 95 th percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure
Human reliability analysis of performing tasks in plants based on fuzzy integral

International Nuclear Information System (INIS)

Washio, Takashi; Kitamura, Yutaka; Takahashi, Hideaki

1991-01-01

The effective improvement of the human working conditions in nuclear power plants might be a solution for the enhancement of the operation safety. The human reliability analysis (HRA) gives a methodological basis of the improvement based on the evaluation of human reliability under various working conditions. This study investigates some difficulties of the human reliability analysis using conventional linear models and recent fuzzy integral models, and provides some solutions to the difficulties. The following practical features of the provided methods are confirmed in comparison with the conventional methods: (1) Applicability to various types of tasks (2) Capability of evaluating complicated dependencies among working condition factors (3) A priori human reliability evaluation based on a systematic task analysis of human action processes (4) A conversion scheme to probability from indices representing human reliability. (author)
Statistically based uncertainty assessments in nuclear risk analysis

International Nuclear Information System (INIS)

Spencer, F.W.; Diegert, K.V.; Easterling, R.G.

1987-01-01

Over the last decade, the problems of estimation and uncertainty assessment in probabilistics risk assessment (PRAs) have been addressed in a variety of NRC and industry-sponsored projects. These problems have received attention because of a recognition that major uncertainties in risk estimation exist, which can be reduced by collecting more and better data and other information, and because of a recognition that better methods for assessing these uncertainties are needed. In particular, a clear understanding of the nature and magnitude of various sources of uncertainty is needed to facilitate descision-making on possible plant changes and research options. Recent PRAs have employed methods of probability propagation, sometimes involving the use of Bayes Theorem, and intended to formalize the use of ''engineering judgment'' or ''expert opinion.'' All sources, or feelings, of uncertainty are expressed probabilistically, so that uncertainty analysis becomes simply a matter of probability propagation. Alternatives to forcing a probabilistic framework at all stages of a PRA are a major concern in this paper, however
Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

Science.gov (United States)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived
Crashworthiness uncertainty analysis of typical civil aircraft based on Box–Behnken method

OpenAIRE

Ren Yiru; Xiang Jinwu

2014-01-01

The crashworthiness is an important design factor of civil aircraft related with the safety of occupant during impact accident. It is a highly nonlinear transient dynamic problem and may be greatly influenced by the uncertainty factors. Crashworthiness uncertainty analysis is conducted to investigate the effects of initial conditions, structural dimensions and material properties. Simplified finite element model is built based on the geometrical model and basic physics phenomenon. Box–Behnken...
The Uncertainty estimation of Alanine/ESR dosimetry

International Nuclear Information System (INIS)

Kim, Bo Rum; An, Jin Hee; Choi, Hoon; Kim, Young Ki

2008-01-01

Machinery, tools and cable etc are in the nuclear power plant which environment is very severe. By measuring actual dose, it needs for extending life expectancy of the machinery and tools and the cable. Therefore, we estimated on dose (gamma ray) of Wolsong nuclear power division 1 by dose estimation technology for three years. The dose estimation technology was secured by ESR(Electron Spin Resonance) dose estimation using regression analysis. We estimate uncertainty for secure a reliability of results. The uncertainty estimation will be able to judge the reliability of measurement results. The estimation of uncertainty referred the international unified guide in order; GUM(Guide to the Expression of Uncertainty in Measurement). It was published by International Standardization for Organization (ISO) in 1993. In this study the uncertainty of e-scan and EMX those are ESR equipment were evaluated and compared. Base on these results, it will improve the reliability of measurement
Probabilistic confidence for decisions based on uncertain reliability estimates

Science.gov (United States)

Reid, Stuart G.

2013-05-01

Reliability assessments are commonly carried out to provide a rational basis for risk-informed decisions concerning the design or maintenance of engineering systems and structures. However, calculated reliabilities and associated probabilities of failure often have significant uncertainties associated with the possible estimation errors relative to the 'true' failure probabilities. For uncertain probabilities of failure, a measure of 'probabilistic confidence' has been proposed to reflect the concern that uncertainty about the true probability of failure could result in a system or structure that is unsafe and could subsequently fail. The paper describes how the concept of probabilistic confidence can be applied to evaluate and appropriately limit the probabilities of failure attributable to particular uncertainties such as design errors that may critically affect the dependability of risk-acceptance decisions. This approach is illustrated with regard to the dependability of structural design processes based on prototype testing with uncertainties attributable to sampling variability.
Reducing Reliability Uncertainties for Marine Renewable Energy

Directory of Open Access Journals (Sweden)

Sam D. Weller

2015-11-01

Full Text Available Technology Readiness Levels (TRLs are a widely used metric of technology maturity and risk for marine renewable energy (MRE devices. To-date, a large number of device concepts have been proposed which have reached the early validation stages of development (TRLs 1–3. Only a handful of mature designs have attained pre-commercial development status following prototype sea trials (TRLs 7–8. In order to navigate through the aptly named “valley of death” (TRLs 4–6 towards commercial realisation, it is necessary for new technologies to be de-risked in terms of component durability and reliability. In this paper the scope of the reliability assessment module of the DTOcean Design Tool is outlined including aspects of Tool integration, data provision and how prediction uncertainties are accounted for. In addition, two case studies are reported of mooring component fatigue testing providing insight into long-term component use and system design for MRE devices. The case studies are used to highlight how test data could be utilised to improve the prediction capabilities of statistical reliability assessment approaches, such as the bottom–up statistical method.
Uncertainty analysis guide

International Nuclear Information System (INIS)

Andres, T.H.

2002-05-01

This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)
Uncertainty analysis guide

Energy Technology Data Exchange (ETDEWEB)

Andres, T.H

2002-05-01

This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)
Accounting for Model Uncertainties Using Reliability Methods - Application to Carbon Dioxide Geologic Sequestration System. Final Report

International Nuclear Information System (INIS)

Mok, Chin Man; Doughty, Christine; Zhang, Keni; Pruess, Karsten; Kiureghian, Armen; Zhang, Miao; Kaback, Dawn

2010-01-01

A new computer code, CALRELTOUGH, which uses reliability methods to incorporate parameter sensitivity and uncertainty analysis into subsurface flow and transport models, was developed by Geomatrix Consultants, Inc. in collaboration with Lawrence Berkeley National Laboratory and University of California at Berkeley. The CALREL reliability code was developed at the University of California at Berkely for geotechnical applications and the TOUGH family of codes was developed at Lawrence Berkeley National Laboratory for subsurface flow and tranport applications. The integration of the two codes provides provides a new approach to deal with uncertainties in flow and transport modeling of the subsurface, such as those uncertainties associated with hydrogeology parameters, boundary conditions, and initial conditions of subsurface flow and transport using data from site characterization and monitoring for conditioning. The new code enables computation of the reliability of a system and the components that make up the system, instead of calculating the complete probability distributions of model predictions at all locations at all times. The new CALRELTOUGH code has tremendous potential to advance subsurface understanding for a variety of applications including subsurface energy storage, nuclear waste disposal, carbon sequestration, extraction of natural resources, and environmental remediation. The new code was tested on a carbon sequestration problem as part of the Phase I project. Phase iI was not awarded.
DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia (Sandia National Laboratories, Livermore, CA); Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Eddy, John P.

2011-12-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.

Reliability Evaluation of Machine Center Components Based on Cascading Failure Analysis

Science.gov (United States)

Zhang, Ying-Zhi; Liu, Jin-Tong; Shen, Gui-Xiang; Long, Zhe; Sun, Shu-Guang

2017-07-01

In order to rectify the problems that the component reliability model exhibits deviation, and the evaluation result is low due to the overlook of failure propagation in traditional reliability evaluation of machine center components, a new reliability evaluation method based on cascading failure analysis and the failure influenced degree assessment is proposed. A direct graph model of cascading failure among components is established according to cascading failure mechanism analysis and graph theory. The failure influenced degrees of the system components are assessed by the adjacency matrix and its transposition, combined with the Pagerank algorithm. Based on the comprehensive failure probability function and total probability formula, the inherent failure probability function is determined to realize the reliability evaluation of the system components. Finally, the method is applied to a machine center, it shows the following: 1) The reliability evaluation values of the proposed method are at least 2.5% higher than those of the traditional method; 2) The difference between the comprehensive and inherent reliability of the system component presents a positive correlation with the failure influenced degree of the system component, which provides a theoretical basis for reliability allocation of machine center system.
Application of a Novel Dose-Uncertainty Model for Dose-Uncertainty Analysis in Prostate Intensity-Modulated Radiotherapy

International Nuclear Information System (INIS)

Jin Hosang; Palta, Jatinder R.; Kim, You-Hyun; Kim, Siyong

2010-01-01

Purpose: To analyze dose uncertainty using a previously published dose-uncertainty model, and to assess potential dosimetric risks existing in prostate intensity-modulated radiotherapy (IMRT). Methods and Materials: The dose-uncertainty model provides a three-dimensional (3D) dose-uncertainty distribution in a given confidence level. For 8 retrospectively selected patients, dose-uncertainty maps were constructed using the dose-uncertainty model at the 95% CL. In addition to uncertainties inherent to the radiation treatment planning system, four scenarios of spatial errors were considered: machine only (S1), S1 + intrafraction, S1 + interfraction, and S1 + both intrafraction and interfraction errors. To evaluate the potential risks of the IMRT plans, three dose-uncertainty-based plan evaluation tools were introduced: confidence-weighted dose-volume histogram, confidence-weighted dose distribution, and dose-uncertainty-volume histogram. Results: Dose uncertainty caused by interfraction setup error was more significant than that of intrafraction motion error. The maximum dose uncertainty (95% confidence) of the clinical target volume (CTV) was smaller than 5% of the prescribed dose in all but two cases (13.9% and 10.2%). The dose uncertainty for 95% of the CTV volume ranged from 1.3% to 2.9% of the prescribed dose. Conclusions: The dose uncertainty in prostate IMRT could be evaluated using the dose-uncertainty model. Prostate IMRT plans satisfying the same plan objectives could generate a significantly different dose uncertainty because a complex interplay of many uncertainty sources. The uncertainty-based plan evaluation contributes to generating reliable and error-resistant treatment plans.
Reliable gene expression analysis by reverse transcription-quantitative PCR: reporting and minimizing the uncertainty in data accuracy.

Science.gov (United States)

Remans, Tony; Keunen, Els; Bex, Geert Jan; Smeets, Karen; Vangronsveld, Jaco; Cuypers, Ann

2014-10-01

Reverse transcription-quantitative PCR (RT-qPCR) has been widely adopted to measure differences in mRNA levels; however, biological and technical variation strongly affects the accuracy of the reported differences. RT-qPCR specialists have warned that, unless researchers minimize this variability, they may report inaccurate differences and draw incorrect biological conclusions. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines describe procedures for conducting and reporting RT-qPCR experiments. The MIQE guidelines enable others to judge the reliability of reported results; however, a recent literature survey found low adherence to these guidelines. Additionally, even experiments that use appropriate procedures remain subject to individual variation that statistical methods cannot correct. For example, since ideal reference genes do not exist, the widely used method of normalizing RT-qPCR data to reference genes generates background noise that affects the accuracy of measured changes in mRNA levels. However, current RT-qPCR data reporting styles ignore this source of variation. In this commentary, we direct researchers to appropriate procedures, outline a method to present the remaining uncertainty in data accuracy, and propose an intuitive way to select reference genes to minimize uncertainty. Reporting the uncertainty in data accuracy also serves for quality assessment, enabling researchers and peer reviewers to confidently evaluate the reliability of gene expression data. © 2014 American Society of Plant Biologists. All rights reserved.
Approach to uncertainty in risk analysis

Energy Technology Data Exchange (ETDEWEB)

Rish, W.R.

1988-08-01

In the Fall of 1985 EPA's Office of Radiation Programs (ORP) initiated a project to develop a formal approach to dealing with uncertainties encountered when estimating and evaluating risks to human health and the environment. Based on a literature review of modeling uncertainty, interviews with ORP technical and management staff, and input from experts on uncertainty analysis, a comprehensive approach was developed. This approach recognizes by design the constraints on budget, time, manpower, expertise, and availability of information often encountered in ''real world'' modeling. It is based on the observation that in practice risk modeling is usually done to support a decision process. As such, the approach focuses on how to frame a given risk modeling problem, how to use that framing to select an appropriate mixture of uncertainty analyses techniques, and how to integrate the techniques into an uncertainty assessment that effectively communicates important information and insight to decision-makers. The approach is presented in this report. Practical guidance on characterizing and analyzing uncertainties about model form and quantities and on effectively communicating uncertainty analysis results is included. Examples from actual applications are presented.
Approach to uncertainty in risk analysis

International Nuclear Information System (INIS)

Rish, W.R.

1988-08-01

In the Fall of 1985 EPA's Office of Radiation Programs (ORP) initiated a project to develop a formal approach to dealing with uncertainties encountered when estimating and evaluating risks to human health and the environment. Based on a literature review of modeling uncertainty, interviews with ORP technical and management staff, and input from experts on uncertainty analysis, a comprehensive approach was developed. This approach recognizes by design the constraints on budget, time, manpower, expertise, and availability of information often encountered in ''real world'' modeling. It is based on the observation that in practice risk modeling is usually done to support a decision process. As such, the approach focuses on how to frame a given risk modeling problem, how to use that framing to select an appropriate mixture of uncertainty analyses techniques, and how to integrate the techniques into an uncertainty assessment that effectively communicates important information and insight to decision-makers. The approach is presented in this report. Practical guidance on characterizing and analyzing uncertainties about model form and quantities and on effectively communicating uncertainty analysis results is included. Examples from actual applications are presented
A task specific uncertainty analysis method for least-squares-based form characterization of ultra-precision freeform surfaces

International Nuclear Information System (INIS)

Ren, M J; Cheung, C F; Kong, L B

2012-01-01

In the measurement of ultra-precision freeform surfaces, least-squares-based form characterization methods are widely used to evaluate the form error of the measured surfaces. Although many methodologies have been proposed in recent years to improve the efficiency of the characterization process, relatively little research has been conducted on the analysis of associated uncertainty in the characterization results which may result from those characterization methods being used. As a result, this paper presents a task specific uncertainty analysis method with application in the least-squares-based form characterization of ultra-precision freeform surfaces. That is, the associated uncertainty in the form characterization results is estimated when the measured data are extracted from a specific surface with specific sampling strategy. Three factors are considered in this study which include measurement error, surface form error and sample size. The task specific uncertainty analysis method has been evaluated through a series of experiments. The results show that the task specific uncertainty analysis method can effectively estimate the uncertainty of the form characterization results for a specific freeform surface measurement
Scenario-based approach for flexible resource loading under uncertainty

NARCIS (Netherlands)

Wullink, G.; Gademann, A.J.R.M.; Hans, E.W.; Harten, van A.

2004-01-01

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To quote reliable due dates in order processing, manage resource capacity adequately and take into account uncertainty, the paper presents and analyses models and tools for
Code development for eigenvalue total sensitivity analysis and total uncertainty analysis

International Nuclear Information System (INIS)

Wan, Chenghui; Cao, Liangzhi; Wu, Hongchun; Zu, Tiejun; Shen, Wei

2015-01-01

Highlights: • We develop a new code for total sensitivity and uncertainty analysis. • The implicit effects of cross sections can be considered. • The results of our code agree well with TSUNAMI-1D. • Detailed analysis for origins of implicit effects is performed. - Abstract: The uncertainties of multigroup cross sections notably impact eigenvalue of neutron-transport equation. We report on a total sensitivity analysis and total uncertainty analysis code named UNICORN that has been developed by applying the direct numerical perturbation method and statistical sampling method. In order to consider the contributions of various basic cross sections and the implicit effects which are indirect results of multigroup cross sections through resonance self-shielding calculation, an improved multigroup cross-section perturbation model is developed. The DRAGON 4.0 code, with application of WIMSD-4 format library, is used by UNICORN to carry out the resonance self-shielding and neutron-transport calculations. In addition, the bootstrap technique has been applied to the statistical sampling method in UNICORN to obtain much steadier and more reliable uncertainty results. The UNICORN code has been verified against TSUNAMI-1D by analyzing the case of TMI-1 pin-cell. The numerical results show that the total uncertainty of eigenvalue caused by cross sections can reach up to be about 0.72%. Therefore the contributions of the basic cross sections and their implicit effects are not negligible
Methodology for reliability allocation based on fault tree analysis and dualistic contrast

Institute of Scientific and Technical Information of China (English)

TONG Lili; CAO Xuewu

2008-01-01

Reliability allocation is a difficult multi-objective optimization problem.This paper presents a methodology for reliability allocation that can be applied to determine the reliability characteristics of reactor systems or subsystems.The dualistic contrast,known as one of the most powerful tools for optimization problems,is applied to the reliability allocation model of a typical system in this article.And the fault tree analysis,deemed to be one of the effective methods of reliability analysis,is also adopted.Thus a failure rate allocation model based on the fault tree analysis and dualistic contrast is achieved.An application on the emergency diesel generator in the nuclear power plant is given to illustrate the proposed method.
Aleatoric and epistemic uncertainties in sampling based nuclear data uncertainty and sensitivity analyses

International Nuclear Information System (INIS)

Zwermann, W.; Krzykacz-Hausmann, B.; Gallner, L.; Klein, M.; Pautz, A.; Velkov, K.

2012-01-01

Sampling based uncertainty and sensitivity analyses due to epistemic input uncertainties, i.e. to an incomplete knowledge of uncertain input parameters, can be performed with arbitrary application programs to solve the physical problem under consideration. For the description of steady-state particle transport, direct simulations of the microscopic processes with Monte Carlo codes are often used. This introduces an additional source of uncertainty, the aleatoric sampling uncertainty, which is due to the randomness of the simulation process performed by sampling, and which adds to the total combined output sampling uncertainty. So far, this aleatoric part of uncertainty is minimized by running a sufficiently large number of Monte Carlo histories for each sample calculation, thus making its impact negligible as compared to the impact from sampling the epistemic uncertainties. Obviously, this process may cause high computational costs. The present paper shows that in many applications reliable epistemic uncertainty results can also be obtained with substantially lower computational effort by performing and analyzing two appropriately generated series of samples with much smaller number of Monte Carlo histories each. The method is applied along with the nuclear data uncertainty and sensitivity code package XSUSA in combination with the Monte Carlo transport code KENO-Va to various critical assemblies and a full scale reactor calculation. It is shown that the proposed method yields output uncertainties and sensitivities equivalent to the traditional approach, with a high reduction of computing time by factors of the magnitude of 100. (authors)
Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

Science.gov (United States)

Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

2018-05-01

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.
Reliability analysis of hydrologic containment of liquefied petroleum gas within unlined rock caverns.

Science.gov (United States)

Gao, X.; Yan, E. C.; Yeh, T. C. J.; Wang, Y.; Liang, Y.; Hao, Y.

2017-12-01

Notice that most of the underground liquefied petroleum gas (LPG) storage caverns are constructed in unlined rock caverns (URCs), where the variability of hydraulic properties (in particular, hydraulic conductivity) has significant impacts on hydrologic containment performance. However, it is practically impossible to characterize the spatial distribution of these properties in detail at the site of URCs. This dilemma forces us to cope with uncertainty in our evaluations of gas containment. As a consequence, the uncertainty-based analysis is deemed more appropriate than the traditional deterministic analysis. The objectives of this paper are 1) to introduce a numerical first order method to calculate the gas containment reliability within a heterogeneous, two-dimensional unlined rock caverns, and 2) to suggest a strategy for improving the gas containment reliability. In order to achieve these goals, we first introduced the stochastic continuum representation of saturated hydraulic conductivity (Ks) of fractured rock and analyzed the spatial variability of Ks at a field site. We then conducted deterministic simulations to demonstrate the importance of heterogeneity of Ks in the analysis of gas tightness performance of URCs. Considering the uncertainty of the heterogeneity in the real world situations, we subsequently developed a numerical first order method (NFOM) to determine the gas tightness reliability at crucial locations of URCs. Using the NFOM, the effect of spatial variability of Ks on gas tightness reliability was investigated. Results show that as variance or spatial structure anisotropy of Ks increases, most of the gas tightness reliability at crucial locations reduces. Meanwhile, we compare the results of NFOM with those of Monte Carlo simulation, and we find the accuracy of NFOM is mainly affected by the magnitude of the variance of Ks. At last, for improving gas containment reliability at crucial locations at this study site, we suggest that vertical
A methodology for strain-based fatigue reliability analysis

International Nuclear Information System (INIS)

Zhao, Y.X.

2000-01-01

A significant scatter of the cyclic stress-strain (CSS) responses should be noted for a nuclear reactor material, 1Cr18Ni9Ti pipe-weld metal. Existence of the scatter implies that a random cyclic strain applied history will be introduced under any of the loading modes even a deterministic loading history. A non-conservative evaluation might be given in the practice without considering the scatter. A methodology for strain-based fatigue reliability analysis, which has taken into account the scatter, is developed. The responses are approximately modeled by probability-based CSS curves of Ramberg-Osgood relation. The strain-life data are modeled, similarly, by probability-based strain-life curves of Coffin-Manson law. The reliability assessment is constructed by considering interference of the random fatigue strain applied and capacity histories. Probability density functions of the applied and capacity histories are analytically given. The methodology could be conveniently extrapolated to the case of deterministic CSS relation as the existent methods did. Non-conservative evaluation of the deterministic CSS relation and availability of present methodology have been indicated by an analysis of the material test results
Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model

KAUST Repository

Wang, Shitao

2016-05-27

Polynomial Chaos expansions are used to analyze uncertainties in an integral oil-gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet-size distribution, and the flow rate—that impact the model\\'s estimates of the plume\\'s trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos-based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate\\'s contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.
Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model

KAUST Repository

Wang, Shitao; Iskandarani, Mohamed; Srinivasan, Ashwanth; Thacker, W. Carlisle; Winokur, Justin; Knio, Omar

2016-01-01

Polynomial Chaos expansions are used to analyze uncertainties in an integral oil-gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet-size distribution, and the flow rate—that impact the model's estimates of the plume's trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos-based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate's contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.
Evidential analytic hierarchy process dependence assessment methodology in human reliability analysis

International Nuclear Information System (INIS)

Chen, Lu Yuan; Zhou, Xinyi; Xiao, Fuyuan; Deng, Yong; Mahadevan, Sankaran

2017-01-01

In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster-Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective
Evidential Analytic Hierarchy Process Dependence Assessment Methodology in Human Reliability Analysis

Directory of Open Access Journals (Sweden)

Luyuan Chen

2017-02-01

Full Text Available In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster–Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective.
Evidential analytic hierarchy process dependence assessment methodology in human reliability analysis

Energy Technology Data Exchange (ETDEWEB)

Chen, Lu Yuan; Zhou, Xinyi; Xiao, Fuyuan; Deng, Yong [School of Computer and Information Science, Southwest University, Chongqing (China); Mahadevan, Sankaran [School of Engineering, Vanderbilt University, Nashville (United States)

2017-02-15

In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster-Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective.
On Bayesian System Reliability Analysis

Energy Technology Data Exchange (ETDEWEB)

Soerensen Ringi, M

1995-05-01

The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.
On Bayesian System Reliability Analysis

International Nuclear Information System (INIS)

Soerensen Ringi, M.

1995-01-01

The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

Science.gov (United States)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.
Application of intelligence based uncertainty analysis for HLW disposal

International Nuclear Information System (INIS)

Kato, Kazuyuki

2003-01-01

Safety assessment for geological disposal of high level radioactive waste inevitably involves factors that cannot be specified in a deterministic manner. These are namely: (1) 'variability' that arises from stochastic nature of the processes and features considered, e.g., distribution of canister corrosion times and spatial heterogeneity of a host geological formation; (2) 'ignorance' due to incomplete or imprecise knowledge of the processes and conditions expected in the future, e.g., uncertainty in the estimation of solubilities and sorption coefficients for important nuclides. In many cases, a decision in assessment, e.g., selection among model options or determination of a parameter value, is subjected to both variability and ignorance in a combined form. It is clearly important to evaluate both influences of variability and ignorance on the result of a safety assessment in a consistent manner. We developed a unified methodology to handle variability and ignorance by using probabilistic and possibilistic techniques respectively. The methodology has been applied to safety assessment of geological disposal of high level radioactive waste. Uncertainties associated with scenarios, models and parameters were defined in terms of fuzzy membership functions derived through a series of interviews to the experts while variability was formulated by means of probability density functions (pdfs) based on available data set. The exercise demonstrated applicability of the new methodology and, in particular, its advantage in quantifying uncertainties based on expert's opinion and in providing information on dependence of assessment result on the level of conservatism. In addition, it was also shown that sensitivity analysis could identify key parameters in reducing uncertainties associated with the overall assessment. The above information can be used to support the judgment process and guide the process of disposal system development in optimization of protection against
Design for a Crane Metallic Structure Based on Imperialist Competitive Algorithm and Inverse Reliability Strategy

Science.gov (United States)

Fan, Xiao-Ning; Zhi, Bo

2017-07-01

Uncertainties in parameters such as materials, loading, and geometry are inevitable in designing metallic structures for cranes. When considering these uncertainty factors, reliability-based design optimization (RBDO) offers a more reasonable design approach. However, existing RBDO methods for crane metallic structures are prone to low convergence speed and high computational cost. A unilevel RBDO method, combining a discrete imperialist competitive algorithm with an inverse reliability strategy based on the performance measure approach, is developed. Application of the imperialist competitive algorithm at the optimization level significantly improves the convergence speed of this RBDO method. At the reliability analysis level, the inverse reliability strategy is used to determine the feasibility of each probabilistic constraint at each design point by calculating its α-percentile performance, thereby avoiding convergence failure, calculation error, and disproportionate computational effort encountered using conventional moment and simulation methods. Application of the RBDO method to an actual crane structure shows that the developed RBDO realizes a design with the best tradeoff between economy and safety together with about one-third of the convergence speed and the computational cost of the existing method. This paper provides a scientific and effective design approach for the design of metallic structures of cranes.
Analysis of uncertainties of thermal hydraulic calculations

International Nuclear Information System (INIS)

Macek, J.; Vavrin, J.

2002-12-01

In 1993-1997 it was proposed, within OECD projects, that a common program should be set up for uncertainty analysis by a probabilistic method based on a non-parametric statistical approach for system computer codes such as RELAP, ATHLET and CATHARE and that a method should be developed for statistical analysis of experimental databases for the preparation of the input deck and statistical analysis of the output calculation results. Software for such statistical analyses would then have to be processed as individual tools independent of the computer codes used for the thermal hydraulic analysis and programs for uncertainty analysis. In this context, a method for estimation of a thermal hydraulic calculation is outlined and selected methods of statistical analysis of uncertainties are described, including methods for prediction accuracy assessment based on the discrete Fourier transformation principle. (author)
RELIABILITY BASED DESIGN OF FIXED FOUNDATION WIND TURBINES

Energy Technology Data Exchange (ETDEWEB)

Nichols, R.

2013-10-14

Recent analysis of offshore wind turbine foundations using both applicable API and IEC standards show that the total load demand from wind and waves is greatest in wave driven storms. Further, analysis of overturning moment loads (OTM) reveal that impact forces exerted by breaking waves are the largest contributor to OTM in big storms at wind speeds above the operating range of 25 m/s. Currently, no codes or standards for offshore wind power generators have been adopted by the Bureau of Ocean Energy Management Regulation and Enforcement (BOEMRE) for use on the Outer Continental Shelf (OCS). Current design methods based on allowable stress design (ASD) incorporate the uncertainty in the variation of loads transferred to the foundation and geotechnical capacity of the soil and rock to support the loads is incorporated into a factor of safety. Sources of uncertainty include spatial and temporal variation of engineering properties, reliability of property measurements applicability and sufficiency of sampling and testing methods, modeling errors, and variability of estimated load predictions. In ASD these sources of variability are generally given qualitative rather than quantitative consideration. The IEC 61400‐3 design standard for offshore wind turbines is based on ASD methods. Load and resistance factor design (LRFD) methods are being increasingly used in the design of structures. Uncertainties such as those listed above can be included quantitatively into the LRFD process. In LRFD load factors and resistance factors are statistically based. This type of analysis recognizes that there is always some probability of failure and enables the probability of failure to be quantified. This paper presents an integrated approach consisting of field observations and numerical simulation to establish the distribution of loads from breaking waves to support the LRFD of fixed offshore foundations.
Reliability-Based Code Calibration

DEFF Research Database (Denmark)

Faber, M.H.; Sørensen, John Dalsgaard

2003-01-01

The present paper addresses fundamental concepts of reliability based code calibration. First basic principles of structural reliability theory are introduced and it is shown how the results of FORM based reliability analysis may be related to partial safety factors and characteristic values....... Thereafter the code calibration problem is presented in its principal decision theoretical form and it is discussed how acceptable levels of failure probability (or target reliabilities) may be established. Furthermore suggested values for acceptable annual failure probabilities are given for ultimate...... and serviceability limit states. Finally the paper describes the Joint Committee on Structural Safety (JCSS) recommended procedure - CodeCal - for the practical implementation of reliability based code calibration of LRFD based design codes....
On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

Science.gov (United States)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.
Uncertainty and Sensitivity Analysis for an Ibuprofen Synthesis Model Based on Hoechst Path

DEFF Research Database (Denmark)

da Conceicao Do Carmo Montes, Frederico; Gernaey, Krist V.; Sin, Gürkan

2017-01-01

into consideration the effects of temperature, acidity, and the choice of the catalyst. Parameter estimation and uncertainty analysis were conducted on the kinetic model parameters using experimental data available in the literature. Finally, one factor at a time sensitivity analysis in the form of deviations......The pharmaceutical industry faces several challenges and barriers when implementing new or improving current pharmaceutical processes, such as competition from generic drug manufacturers and stricter regulations from the U.S. Food and Drug Administration and the European Medicine agency. The demand...... for efficient and reliable models to simulate and design/improve pharmaceutical processes is therefore increasing. For the case of ibuprofen, a well-known anti-inflammatory drug, the existing models do not include its complete synthesis path, usually referring only to one out of aset of different reactions...
Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations

Directory of Open Access Journals (Sweden)

Xin-Jia Meng

2015-01-01

Full Text Available Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO. However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most probable failure point (MPP search of inverse reliability, and then the process of searching for MPP of multidisciplinary inverse reliability is performed based on the framework of CLA-CO. This method improves the MPP searching process through two elements. One is treating the discipline analyses as the equality constraints in the subsystem optimization, and the other is using linear approximations corresponding to subsystem responses as the replacement of the consistency equality constraint in system optimization. With these two elements, the proposed method realizes the parallel analysis of each discipline, and it also has a higher computational efficiency. Additionally, there are no difficulties in applying the proposed method to problems with nonnormal distribution variables. One mathematical test problem and an electronic packaging problem are used to demonstrate the effectiveness of the proposed method.
Reliability assessment and probability based design of reinforced concrete containments and shear walls

International Nuclear Information System (INIS)

Hwang, H.; Reich, M.; Ellingwood, B.; Shinozuka, M.

1986-03-01

This report summarizes work completed under the program entitled, ''Probability-Based Load Combinations for Design of Category I Structures.'' Under this program, the probabilistic models for various static and dynamic loads were formulated. The randomness and uncertainties in material strengths and structural resistance were established. Several limit states of concrete containments and shear walls were identified and analytically formulated. Furthermore, the reliability analysis methods for estimating limit state probabilities were established. These reliability analysis methods can be used to evaluate the safety levels of nuclear structures under various combinations of static and dynamic loads. They can also be used to generate analytically the fragility data for PRA studies. In addition to the development of reliability analysis methods, probability-based design criteria for concrete containments and shear wall structures have also been developed. The proposed design criteria are in the load and resistance factor design (LRFD) format. The load and resistance factors are determined for several limit states and target limit state probabilities. Thus, the proposed design criteria are risk-consistent and have a well-established rationale. 73 refs., 18 figs., 16 tabs
Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Directory of Open Access Journals (Sweden)

Hao Zhu

2017-04-01

Full Text Available Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO, probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.
Hybrid uncertainty-based design optimization and its application to hybrid rocket motors for manned lunar landing

Institute of Scientific and Technical Information of China (English)

Zhu Hao; Tian Hui; Cai Guobiao

2017-01-01

Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters. This paper presents a hybrid uncertainty-based design optimization (UDO) method developed from probability theory and interval theory. Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory, while the others are defined as interval variables with interval theory. Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncer-tainty propagation from the design parameters to system responses. Three design optimization strategies, including deterministic design optimization (DDO), probabilistic UDO and hybrid UDO, are applied to the conceptual design of a hybrid rocket motor (HRM) used as the ascent propulsion system in Apollo lunar module. By comparison, the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.
Optimal, Reliability-Based Code Calibration

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

2002-01-01

Reliability based code calibration is considered in this paper. It is described how the results of FORM based reliability analysis may be related to the partial safety factors and characteristic values. The code calibration problem is presented in a decision theoretical form and it is discussed how...... of reliability based code calibration of LRFD based design codes....
Numerical Model based Reliability Estimation of Selective Laser Melting Process

DEFF Research Database (Denmark)

Mohanty, Sankhya; Hattel, Jesper Henri

2014-01-01

Selective laser melting is developing into a standard manufacturing technology with applications in various sectors. However, the process is still far from being at par with conventional processes such as welding and casting, the primary reason of which is the unreliability of the process. While...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...... parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....
Nuclear reactor component populations, reliability data bases, and their relationship to failure rate estimation and uncertainty analysis

International Nuclear Information System (INIS)

Martz, H.F.; Beckman, R.J.

1981-12-01

Probabilistic risk analyses are used to assess the risks inherent in the operation of existing and proposed nuclear power reactors. In performing such risk analyses the failure rates of various components which are used in a variety of reactor systems must be estimated. These failure rate estimates serve as input to fault trees and event trees used in the analyses. Component failure rate estimation is often based on relevant field failure data from different reliability data sources such as LERs, NPRDS, and the In-Plant Data Program. Various statistical data analysis and estimation methods have been proposed over the years to provide the required estimates of the component failure rates. This report discusses the basis and extent to which statistical methods can be used to obtain component failure rate estimates. The report is expository in nature and focuses on the general philosophical basis for such statistical methods. Various terms and concepts are defined and illustrated by means of numerous simple examples
Uncertainty Analysis and Expert Judgment in Seismic Hazard Analysis

Science.gov (United States)

Klügel, Jens-Uwe

2011-01-01

The large uncertainty associated with the prediction of future earthquakes is usually regarded as the main reason for increased hazard estimates which have resulted from some recent large scale probabilistic seismic hazard analysis studies (e.g. the PEGASOS study in Switzerland and the Yucca Mountain study in the USA). It is frequently overlooked that such increased hazard estimates are characteristic for a single specific method of probabilistic seismic hazard analysis (PSHA): the traditional (Cornell-McGuire) PSHA method which has found its highest level of sophistication in the SSHAC probability method. Based on a review of the SSHAC probability model and its application in the PEGASOS project, it is shown that the surprising results of recent PSHA studies can be explained to a large extent by the uncertainty model used in traditional PSHA, which deviates from the state of the art in mathematics and risk analysis. This uncertainty model, the Ang-Tang uncertainty model, mixes concepts of decision theory with probabilistic hazard assessment methods leading to an overestimation of uncertainty in comparison to empirical evidence. Although expert knowledge can be a valuable source of scientific information, its incorporation into the SSHAC probability method does not resolve the issue of inflating uncertainties in PSHA results. Other, more data driven, PSHA approaches in use in some European countries are less vulnerable to this effect. The most valuable alternative to traditional PSHA is the direct probabilistic scenario-based approach, which is closely linked with emerging neo-deterministic methods based on waveform modelling.
A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis☆

Science.gov (United States)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-01-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. PMID:25843987
Uncertainty Analysis with Considering Resonance Self-shielding Effect

Energy Technology Data Exchange (ETDEWEB)

Han, Tae Young [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

If infinitely diluted multi-group cross sections were used for the sensitivity, the covariance data from the evaluated nuclear data library (ENDL) was directly applied. However, in case of using a self-shielded multi-group cross section, the covariance data should be corrected considering self-shielding effect. Usually, implicit uncertainty can be defined as the uncertainty change by the resonance self-shielding effect as described above. MUSAD ( Modules of Uncertainty and Sensitivity Analysis for DeCART ) has been developed for a multiplication factor and cross section uncertainty based on the generalized perturbation theory and it, however, can only quantify the explicit uncertainty by the self-shielded multi-group cross sections without considering the implicit effect. Thus, this paper addresses the implementation of the implicit uncertainty analysis module into the code and the numerical results for the verification are provided. The implicit uncertainty analysis module has been implemented into MUSAD based on infinitely-diluted cross section-based consistent method. The verification calculation was performed on MHTGR 350 Ex.I-1a and the differences with McCARD result decrease from 40% to 1% in CZP case and 3% in HFP case. From this study, it is expected that MUSAD code can reasonably produce the complete uncertainty on VHTR or LWR where the resonance self-shielding effect should be significantly considered.
Uncertainty Analysis with Considering Resonance Self-shielding Effect

International Nuclear Information System (INIS)

Han, Tae Young

2016-01-01

If infinitely diluted multi-group cross sections were used for the sensitivity, the covariance data from the evaluated nuclear data library (ENDL) was directly applied. However, in case of using a self-shielded multi-group cross section, the covariance data should be corrected considering self-shielding effect. Usually, implicit uncertainty can be defined as the uncertainty change by the resonance self-shielding effect as described above. MUSAD ( Modules of Uncertainty and Sensitivity Analysis for DeCART ) has been developed for a multiplication factor and cross section uncertainty based on the generalized perturbation theory and it, however, can only quantify the explicit uncertainty by the self-shielded multi-group cross sections without considering the implicit effect. Thus, this paper addresses the implementation of the implicit uncertainty analysis module into the code and the numerical results for the verification are provided. The implicit uncertainty analysis module has been implemented into MUSAD based on infinitely-diluted cross section-based consistent method. The verification calculation was performed on MHTGR 350 Ex.I-1a and the differences with McCARD result decrease from 40% to 1% in CZP case and 3% in HFP case. From this study, it is expected that MUSAD code can reasonably produce the complete uncertainty on VHTR or LWR where the resonance self-shielding effect should be significantly considered
Development and application of objective uncertainty measures for nuclear power plant transient analysis

International Nuclear Information System (INIS)

Vinai, P.

2007-10-01

For the development, design and licensing of a nuclear power plant (NPP), a sound safety analysis is necessary to study the diverse physical phenomena involved in the system behaviour under operational and transient conditions. Such studies are based on detailed computer simulations. With the progresses achieved in computer technology and the greater availability of experimental and plant data, the use of best estimate codes for safety evaluations has gained increasing acceptance. The application of best estimate safety analysis has raised new problems that need to be addressed: it has become more crucial to assess as to how reliable code predictions are, especially when they need to be compared against safety limits that must not be crossed. It becomes necessary to identify and quantify the various possible sources of uncertainty that affect the reliability of the results. Currently, such uncertainty evaluations are generally based on experts' opinion. In the present research, a novel methodology based on a non-parametric statistical approach has been developed for objective quantification of best-estimate code uncertainties related to the physical models used in the code. The basis is an evaluation of the accuracy of a given physical model achieved by comparing its predictions with experimental data from an appropriate set of separate-effect tests. The differences between measurements and predictions can be considered stochastically distributed, and thus a statistical approach can be employed. The first step was the development of a procedure for investigating the dependence of a given physical model's accuracy on the experimental conditions. Each separate-effect test effectively provides a random sample of discrepancies between measurements and predictions, corresponding to a location in the state space defined by a certain number of independent system variables. As a consequence, the samples of 'errors', achieved from analysis of the entire database, are

LIF: A new Kriging based learning function and its application to structural reliability analysis

International Nuclear Information System (INIS)

Sun, Zhili; Wang, Jian; Li, Rui; Tong, Cao

2017-01-01

The main task of structural reliability analysis is to estimate failure probability of a studied structure taking randomness of input variables into account. To consider structural behavior practically, numerical models become more and more complicated and time-consuming, which increases the difficulty of reliability analysis. Therefore, sequential strategies of design of experiment (DoE) are raised. In this research, a new learning function, named least improvement function (LIF), is proposed to update DoE of Kriging based reliability analysis method. LIF values how much the accuracy of estimated failure probability will be improved if adding a given point into DoE. It takes both statistical information provided by the Kriging model and the joint probability density function of input variables into account, which is the most important difference from the existing learning functions. Maximum point of LIF is approximately determined with Markov Chain Monte Carlo(MCMC) simulation. A new reliability analysis method is developed based on the Kriging model, in which LIF, MCMC and Monte Carlo(MC) simulation are employed. Three examples are analyzed. Results show that LIF and the new method proposed in this research are very efficient when dealing with nonlinear performance function, small probability, complicated limit state and engineering problems with high dimension. - Highlights: • Least improvement function (LIF) is proposed for structural reliability analysis. • LIF takes both Kriging based statistical information and joint PDF into account. • A reliability analysis method is constructed based on Kriging, MCS and LIF.
Reliability and Robustness Analysis of the Masinga Dam under Uncertainty

Directory of Open Access Journals (Sweden)

Hayden Postle-Floyd

2017-02-01

Full Text Available Kenya’s water abstraction must meet the projected growth in municipal and irrigation demand by the end of 2030 in order to achieve the country’s industrial and economic development plan. The Masinga dam, on the Tana River, is the key to meeting this goal to satisfy the growing demands whilst also continuing to provide hydroelectric power generation. This study quantitatively assesses the reliability and robustness of the Masinga dam system under uncertain future supply and demand using probabilistic climate and population projections, and examines how long-term planning may improve the longevity of the dam. River flow and demand projections are used alongside each other as inputs to the dam system simulation model linked to an optimisation engine to maximise water availability. Water availability after demand satisfaction is assessed for future years, and the projected reliability of the system is calculated for selected years. The analysis shows that maximising power generation on a short-term year-by-year basis achieves 80%, 50% and 1% reliability by 2020, 2025 and 2030 onwards, respectively. Longer term optimal planning, however, has increased system reliability to up to 95% in 2020, 80% in 2025, and more than 40% in 2030 onwards. In addition, increasing the capacity of the reservoir by around 25% can significantly improve the robustness of the system for all future time periods. This study provides a platform for analysing the implication of different planning and management of Masinga dam and suggests that careful consideration should be given to account for growing municipal needs and irrigation schemes in both the immediate and the associated Tana River basin.
Reliability analysis based on the losses from failures.

Science.gov (United States)

Todinov, M T

2006-04-01

The conventional reliability analysis is based on the premise that increasing the reliability of a system will decrease the losses from failures. On the basis of counterexamples, it is demonstrated that this is valid only if all failures are associated with the same losses. In case of failures associated with different losses, a system with larger reliability is not necessarily characterized by smaller losses from failures. Consequently, a theoretical framework and models are proposed for a reliability analysis, linking reliability and the losses from failures. Equations related to the distributions of the potential losses from failure have been derived. It is argued that the classical risk equation only estimates the average value of the potential losses from failure and does not provide insight into the variability associated with the potential losses. Equations have also been derived for determining the potential and the expected losses from failures for nonrepairable and repairable systems with components arranged in series, with arbitrary life distributions. The equations are also valid for systems/components with multiple mutually exclusive failure modes. The expected losses given failure is a linear combination of the expected losses from failure associated with the separate failure modes scaled by the conditional probabilities with which the failure modes initiate failure. On this basis, an efficient method for simplifying complex reliability block diagrams has been developed. Branches of components arranged in series whose failures are mutually exclusive can be reduced to single components with equivalent hazard rate, downtime, and expected costs associated with intervention and repair. A model for estimating the expected losses from early-life failures has also been developed. For a specified time interval, the expected losses from early-life failures are a sum of the products of the expected number of failures in the specified time intervals covering the
Sensitivity of wildlife habitat models to uncertainties in GIS data

Science.gov (United States)

Stoms, David M.; Davis, Frank W.; Cogan, Christopher B.

1992-01-01

Decision makers need to know the reliability of output products from GIS analysis. For many GIS applications, it is not possible to compare these products to an independent measure of 'truth'. Sensitivity analysis offers an alternative means of estimating reliability. In this paper, we present a CIS-based statistical procedure for estimating the sensitivity of wildlife habitat models to uncertainties in input data and model assumptions. The approach is demonstrated in an analysis of habitat associations derived from a GIS database for the endangered California condor. Alternative data sets were generated to compare results over a reasonable range of assumptions about several sources of uncertainty. Sensitivity analysis indicated that condor habitat associations are relatively robust, and the results have increased our confidence in our initial findings. Uncertainties and methods described in the paper have general relevance for many GIS applications.
Effect of uncertainties on probabilistic-based design capacity of hydrosystems

Science.gov (United States)

Tung, Yeou-Koung

2018-02-01

Hydrosystems engineering designs involve analysis of hydrometric data (e.g., rainfall, floods) and use of hydrologic/hydraulic models, all of which contribute various degrees of uncertainty to the design process. Uncertainties in hydrosystem designs can be generally categorized into aleatory and epistemic types. The former arises from the natural randomness of hydrologic processes whereas the latter are due to knowledge deficiency in model formulation and model parameter specification. This study shows that the presence of epistemic uncertainties induces uncertainty in determining the design capacity. Hence, the designer needs to quantify the uncertainty features of design capacity to determine the capacity with a stipulated performance reliability under the design condition. Using detention basin design as an example, the study illustrates a methodological framework by considering aleatory uncertainty from rainfall and epistemic uncertainties from the runoff coefficient, curve number, and sampling error in design rainfall magnitude. The effects of including different items of uncertainty and performance reliability on the design detention capacity are examined. A numerical example shows that the mean value of the design capacity of the detention basin increases with the design return period and this relation is found to be practically the same regardless of the uncertainty types considered. The standard deviation associated with the design capacity, when subject to epistemic uncertainty, increases with both design frequency and items of epistemic uncertainty involved. It is found that the epistemic uncertainty due to sampling error in rainfall quantiles should not be ignored. Even with a sample size of 80 (relatively large for a hydrologic application) the inclusion of sampling error in rainfall quantiles resulted in a standard deviation about 2.5 times higher than that considering only the uncertainty of the runoff coefficient and curve number. Furthermore, the
Uncertainty analysis in vulnerability estimations for elements at risk- a review of concepts and some examples on landslides

Science.gov (United States)

Ciurean, R. L.; Glade, T.

2012-04-01

Decision under uncertainty is a constant of everyday life and an important component of risk management and governance. Recently, experts have emphasized the importance of quantifying uncertainty in all phases of landslide risk analysis. Due to its multi-dimensional and dynamic nature, (physical) vulnerability is inherently complex and the "degree of loss" estimates imprecise and to some extent even subjective. Uncertainty analysis introduces quantitative modeling approaches that allow for a more explicitly objective output, improving the risk management process as well as enhancing communication between various stakeholders for better risk governance. This study presents a review of concepts for uncertainty analysis in vulnerability of elements at risk to landslides. Different semi-quantitative and quantitative methods are compared based on their feasibility in real-world situations, hazard dependency, process stage in vulnerability assessment (i.e. input data, model, output), and applicability within an integrated landslide hazard and risk framework. The resulted observations will help to identify current gaps and future needs in vulnerability assessment, including estimation of uncertainty propagation, transferability of the methods, development of visualization tools, but also address basic questions like what is uncertainty and how uncertainty can be quantified or treated in a reliable and reproducible way.
Fuzzy uncertainty modeling applied to AP1000 nuclear power plant LOCA

International Nuclear Information System (INIS)

Ferreira Guimaraes, Antonio Cesar; Franklin Lapa, Celso Marcelo; Lamego Simoes Filho, Francisco Fernando; Cabral, Denise Cunha

2011-01-01

Research highlights: → This article presents an uncertainty modelling study using a fuzzy approach. → The AP1000 Westinghouse NPP was used and it is provided of passive safety systems. → The use of advanced passive safety systems in NPP has limited operational experience. → Failure rates and basic events probabilities used on the fault tree analysis. → Fuzzy uncertainty approach was employed to reliability of the AP1000 large LOCA. - Abstract: This article presents an uncertainty modeling study using a fuzzy approach applied to the Westinghouse advanced nuclear reactor. The AP1000 Westinghouse Nuclear Power Plant (NPP) is provided of passive safety systems, based on thermo physics phenomenon, that require no operating actions, soon after an incident has been detected. The use of advanced passive safety systems in NPP has limited operational experience. As it occurs in any reliability study, statistically non-significant events report introduces a significant uncertainty level about the failure rates and basic events probabilities used on the fault tree analysis (FTA). In order to model this uncertainty, a fuzzy approach was employed to reliability analysis of the AP1000 large break Loss of Coolant Accident (LOCA). The final results have revealed that the proposed approach may be successfully applied to modeling of uncertainties in safety studies.
Uncertainty analysis in Monte Carlo criticality computations

International Nuclear Information System (INIS)

Qi Ao

2011-01-01

Highlights: ► Two types of uncertainty methods for k eff Monte Carlo computations are examined. ► Sampling method has the least restrictions on perturbation but computing resources. ► Analytical method is limited to small perturbation on material properties. ► Practicality relies on efficiency, multiparameter applicability and data availability. - Abstract: Uncertainty analysis is imperative for nuclear criticality risk assessments when using Monte Carlo neutron transport methods to predict the effective neutron multiplication factor (k eff ) for fissionable material systems. For the validation of Monte Carlo codes for criticality computations against benchmark experiments, code accuracy and precision are measured by both the computational bias and uncertainty in the bias. The uncertainty in the bias accounts for known or quantified experimental, computational and model uncertainties. For the application of Monte Carlo codes for criticality analysis of fissionable material systems, an administrative margin of subcriticality must be imposed to provide additional assurance of subcriticality for any unknown or unquantified uncertainties. Because of a substantial impact of the administrative margin of subcriticality on economics and safety of nuclear fuel cycle operations, recently increasing interests in reducing the administrative margin of subcriticality make the uncertainty analysis in criticality safety computations more risk-significant. This paper provides an overview of two most popular k eff uncertainty analysis methods for Monte Carlo criticality computations: (1) sampling-based methods, and (2) analytical methods. Examples are given to demonstrate their usage in the k eff uncertainty analysis due to uncertainties in both neutronic and non-neutronic parameters of fissionable material systems.
α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

International Nuclear Information System (INIS)

Purba, Julwan Hendry; Sony Tjahyani, D.T.; Widodo, Surip; Tjahjono, Hendro

2017-01-01

Highlights: •FPFTA deals with epistemic uncertainty using fuzzy probability. •Criticality analysis is important for reliability improvement. •An α-cut method based importance measure is proposed for criticality analysis in FPFTA. •The α-cut method based importance measure utilises α-cut multiplication, α-cut subtraction, and area defuzzification technique. •Benchmarking confirm that the proposed method is feasible for criticality analysis in FPFTA. -- Abstract: Fuzzy probability – based fault tree analysis (FPFTA) has been recently developed and proposed to deal with the limitations of conventional fault tree analysis. In FPFTA, reliabilities of basic events, intermediate events and top event are characterized by fuzzy probabilities. Furthermore, the quantification of the FPFTA is based on fuzzy multiplication rule and fuzzy complementation rule to propagate uncertainties from basic event to the top event. Since the objective of the fault tree analysis is to improve the reliability of the system being evaluated, it is necessary to find the weakest path in the system. For this purpose, criticality analysis can be implemented. Various importance measures, which are based on conventional probabilities, have been developed and proposed for criticality analysis in fault tree analysis. However, not one of those importance measures can be applied for criticality analysis in FPFTA, which is based on fuzzy probability. To be fully applied in nuclear power plant probabilistic safety assessment, FPFTA needs to have its corresponding importance measure. The objective of this study is to develop an α-cut method based importance measure to evaluate and rank the importance of basic events for criticality analysis in FPFTA. To demonstrate the applicability of the proposed measure, a case study is performed and its results are then benchmarked to the results generated by the four well known importance measures in conventional fault tree analysis. The results
A New Method of Reliability Evaluation Based on Wavelet Information Entropy for Equipment Condition Identification

International Nuclear Information System (INIS)

He, Z J; Zhang, X L; Chen, X F

2012-01-01

Aiming at reliability evaluation of condition identification of mechanical equipment, it is necessary to analyze condition monitoring information. A new method of reliability evaluation based on wavelet information entropy extracted from vibration signals of mechanical equipment is proposed. The method is quite different from traditional reliability evaluation models that are dependent on probability statistics analysis of large number sample data. The vibration signals of mechanical equipment were analyzed by means of second generation wavelet package (SGWP). We take relative energy in each frequency band of decomposed signal that equals a percentage of the whole signal energy as probability. Normalized information entropy (IE) is obtained based on the relative energy to describe uncertainty of a system instead of probability. The reliability degree is transformed by the normalized wavelet information entropy. A successful application has been achieved to evaluate the assembled quality reliability for a kind of dismountable disk-drum aero-engine. The reliability degree indicates the assembled quality satisfactorily.
Estimation of environment-related properties of chemicals for design of sustainable processes: Development of group-contribution+ (GC+) models and uncertainty analysis

DEFF Research Database (Denmark)

Hukkerikar, Amol; Kalakul, Sawitree; Sarup, Bent

2012-01-01

The aim of this work is to develop group-3 contribution+ (GC+)method (combined group-contribution (GC) method and atom connectivity index (CI)) based 15 property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated...... property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality......, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22...
Uncertainty and sensitivity analysis on probabilistic safety assessment of an experimental facility

International Nuclear Information System (INIS)

Burgazzi, L.

2000-01-01

The aim of this work is to perform an uncertainty and sensitivity analysis on the probabilistic safety assessment of the International Fusion Materials Irradiation Facility (IFMIF), in order to assess the effect on the final risk values of the uncertainties associated with the generic data used for the initiating events and component reliability and to identify the key quantities contributing to this uncertainty. The analysis is conducted on the expected frequency calculated for the accident sequences, defined through the event tree (ET) modeling. This is in order to increment credit to the ET model quantification, to calculate frequency distributions for the occurrence of events and, consequently, to assess if sequences have been correctly selected on the probability standpoint and finally to verify the fulfillment of the safety conditions. Uncertainty and sensitivity analysis are performed using respectively Monte Carlo sampling and an importance parameter technique. (author)
Comparison between different uncertainty propagation methods in multivariate analysis: An application in the bivariate case

International Nuclear Information System (INIS)

Mullor, R.; Sanchez, A.; Martorell, S.; Martinez-Alzamora, N.

2011-01-01

Safety related systems performance optimization is classically based on quantifying the effects that testing and maintenance activities have on reliability and cost (R+C). However, R+C quantification is often incomplete in the sense that important uncertainties may not be considered. An important number of studies have been published in the last decade in the field of R+C based optimization considering uncertainties. They have demonstrated that inclusion of uncertainties in the optimization brings the decision maker insights concerning how uncertain the R+C results are and how this uncertainty does matter as it can result in differences in the outcome of the decision making process. Several methods of uncertainty propagation based on the theory of tolerance regions have been proposed in the literature depending on the particular characteristics of the variables in the output and their relations. In this context, the objective of this paper focuses on the application of non-parametric and parametric methods to analyze uncertainty propagation, which will be implemented on a multi-objective optimization problem where reliability and cost act as decision criteria and maintenance intervals act as decision variables. Finally, a comparison of results of these applications and the conclusions obtained are presented.
Comparison between different uncertainty propagation methods in multivariate analysis: An application in the bivariate case

Energy Technology Data Exchange (ETDEWEB)

Mullor, R. [Dpto. Estadistica e Investigacion Operativa, Universidad Alicante (Spain); Sanchez, A., E-mail: aisanche@eio.upv.e [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia, Camino de Vera s/n 46022 (Spain); Martorell, S. [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Martinez-Alzamora, N. [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia, Camino de Vera s/n 46022 (Spain)

2011-06-15

Safety related systems performance optimization is classically based on quantifying the effects that testing and maintenance activities have on reliability and cost (R+C). However, R+C quantification is often incomplete in the sense that important uncertainties may not be considered. An important number of studies have been published in the last decade in the field of R+C based optimization considering uncertainties. They have demonstrated that inclusion of uncertainties in the optimization brings the decision maker insights concerning how uncertain the R+C results are and how this uncertainty does matter as it can result in differences in the outcome of the decision making process. Several methods of uncertainty propagation based on the theory of tolerance regions have been proposed in the literature depending on the particular characteristics of the variables in the output and their relations. In this context, the objective of this paper focuses on the application of non-parametric and parametric methods to analyze uncertainty propagation, which will be implemented on a multi-objective optimization problem where reliability and cost act as decision criteria and maintenance intervals act as decision variables. Finally, a comparison of results of these applications and the conclusions obtained are presented.
Uncertainty in Historical Land-Use Reconstructions with Topographic Maps

Directory of Open Access Journals (Sweden)

Kaim Dominik

2014-09-01

Full Text Available The paper presents the outcomes of the uncertainty investigation of a long-term forest cover change analysis in the Polish Carpathians (nearly 20,000 km2 and Swiss Alps (nearly 10,000 km2 based on topographic maps. Following Leyk et al. (2005 all possible uncertainties are grouped into three domains - production-oriented, transformation- oriented and application-oriented. We show typical examples for each uncertainty domain, encountered during the forest cover change analysis and discuss consequences for change detection. Finally, a proposal for reliability assessment is presented.
Reliability analysis of the solar array based on Fault Tree Analysis

International Nuclear Information System (INIS)

Wu Jianing; Yan Shaoze

2011-01-01

The solar array is an important device used in the spacecraft, which influences the quality of in-orbit operation of the spacecraft and even the launches. This paper analyzes the reliability of the mechanical system and certifies the most vital subsystem of the solar array. The fault tree analysis (FTA) model is established according to the operating process of the mechanical system based on DFH-3 satellite; the logical expression of the top event is obtained by Boolean algebra and the reliability of the solar array is calculated. The conclusion shows that the hinges are the most vital links between the solar arrays. By analyzing the structure importance(SI) of the hinge's FTA model, some fatal causes, including faults of the seal, insufficient torque of the locking spring, temperature in space, and friction force, can be identified. Damage is the initial stage of the fault, so limiting damage is significant to prevent faults. Furthermore, recommendations for improving reliability associated with damage limitation are discussed, which can be used for the redesigning of the solar array and the reliability growth planning.
Reliability analysis of the solar array based on Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Wu Jianing; Yan Shaoze, E-mail: yansz@mail.tsinghua.edu.cn [State Key Laboratory of Tribology, Department of Precision Instruments and Mechanology, Tsinghua University,Beijing 100084 (China)

2011-07-19

The solar array is an important device used in the spacecraft, which influences the quality of in-orbit operation of the spacecraft and even the launches. This paper analyzes the reliability of the mechanical system and certifies the most vital subsystem of the solar array. The fault tree analysis (FTA) model is established according to the operating process of the mechanical system based on DFH-3 satellite; the logical expression of the top event is obtained by Boolean algebra and the reliability of the solar array is calculated. The conclusion shows that the hinges are the most vital links between the solar arrays. By analyzing the structure importance(SI) of the hinge's FTA model, some fatal causes, including faults of the seal, insufficient torque of the locking spring, temperature in space, and friction force, can be identified. Damage is the initial stage of the fault, so limiting damage is significant to prevent faults. Furthermore, recommendations for improving reliability associated with damage limitation are discussed, which can be used for the redesigning of the solar array and the reliability growth planning.
Uncertainty analysis of neutron transport calculation

International Nuclear Information System (INIS)

Oka, Y.; Furuta, K.; Kondo, S.

1987-01-01

A cross section sensitivity-uncertainty analysis code, SUSD was developed. The code calculates sensitivity coefficients for one and two-dimensional transport problems based on the first order perturbation theory. Variance and standard deviation of detector responses or design parameters can be obtained using cross section covariance matrix. The code is able to perform sensitivity-uncertainty analysis for secondary neutron angular distribution(SAD) and secondary neutron energy distribution(SED). Covariances of 6 Li and 7 Li neutron cross sections in JENDL-3PR1 were evaluated including SAD and SED. Covariances of Fe and Be were also evaluated. The uncertainty of tritium breeding ratio, fast neutron leakage flux and neutron heating was analysed on four types of blanket concepts for a commercial tokamak fusion reactor. The uncertainty of tritium breeding ratio was less than 6 percent. Contribution from SAD/SED uncertainties are significant for some parameters. Formulas to estimate the errors of numerical solution of the transport equation were derived based on the perturbation theory. This method enables us to deterministically estimate the numerical errors due to iterative solution, spacial discretization and Legendre polynomial expansion of transfer cross-sections. The calculational errors of the tritium breeding ratio and the fast neutron leakage flux of the fusion blankets were analysed. (author)
Reliability constrained generation expansion planning with consideration of wind farms uncertainties in deregulated electricity market

International Nuclear Information System (INIS)

Hemmati, Reza; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin

2013-01-01

Highlights: • Generation expansion planning is presented in deregulated electricity market. • Wind farm uncertainty is modeled in the problem. • The profit of each GENCO is maximized and also the safe operation of system is satisfied. • Salve sector is managed as an optimization programming and solved by using PSO technique. • Master sector is considered in pool market and Cournot model is used to simulate it. - Abstract: This paper addresses reliability constrained generation expansion planning (GEP) in the presence of wind farm uncertainty in deregulated electricity market. The proposed GEP aims at maximizing the expected profit of all generation companies (GENCOs), while considering security and reliability constraints such as reserve margin and loss of load expectation (LOLE). Wind farm uncertainty is also considered in the planning and GENCOs denote their planning in the presence of wind farm uncertainty. The uncertainty is modeled by probability distribution function (PDF) and Monte-Carlo simulation (MCS) is used to insert uncertainty into the problem. The proposed GEP is a constrained, nonlinear, mixed-integer optimization programming and solved by using particle swarm optimization (PSO) method. In this paper, Electricity market structure is modeled as a pool market. Simulation results verify the effectiveness and validity of the proposed planning for maximizing GENCOs profit in the presence of wind farms uncertainties in electricity market
Reliability Analysis of Fatigue Failure of Cast Components for Wind Turbines

Directory of Open Access Journals (Sweden)

Hesam Mirzaei Rafsanjani

2015-04-01

Full Text Available Fatigue failure is one of the main failure modes for wind turbine drivetrain components made of cast iron. The wind turbine drivetrain consists of a variety of heavily loaded components, like the main shaft, the main bearings, the gearbox and the generator. The failure of each component will lead to substantial economic losses such as cost of lost energy production and cost of repairs. During the design lifetime, the drivetrain components are exposed to variable loads from winds and waves and other sources of loads that are uncertain and have to be modeled as stochastic variables. The types of loads are different for offshore and onshore wind turbines. Moreover, uncertainties about the fatigue strength play an important role in modeling and assessment of the reliability of the components. In this paper, a generic stochastic model for fatigue failure of cast iron components based on fatigue test data and a limit state equation for fatigue failure based on the SN-curve approach and Miner’s rule is presented. The statistical analysis of the fatigue data is performed using the Maximum Likelihood Method which also gives an estimate of the statistical uncertainties. Finally, illustrative examples are presented with reliability analyses depending on various stochastic models and partial safety factors.

The IAEA Coordinated Research Program on HTGR Reactor Physics, Thermal-hydraulics and Depletion Uncertainty Analysis: Description of the Benchmark Test Cases and Phases

Energy Technology Data Exchange (ETDEWEB)

Frederik Reitsma; Gerhard Strydom; Bismark Tyobeka; Kostadin Ivanov

2012-10-01

The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The uncertainties in the HTR analysis tools are today typically assessed with sensitivity analysis and then a few important input uncertainties (typically based on a PIRT process) are varied in the analysis to find a spread in the parameter of importance. However, one wish to apply a more fundamental approach to determine the predictive capability and accuracies of coupled neutronics/thermal-hydraulics and depletion simulations used for reactor design and safety assessment. Today there is a broader acceptance of the use of uncertainty analysis even in safety studies and it has been accepted by regulators in some cases to replace the traditional conservative analysis. Finally, there is also a renewed focus in supplying reliable covariance data (nuclear data uncertainties) that can then be used in uncertainty methods. Uncertainty and sensitivity studies are therefore becoming an essential component of any significant effort in data and simulation improvement. In order to address uncertainty in analysis and methods in the HTGR community the IAEA launched a Coordinated Research Project (CRP) on the HTGR Uncertainty Analysis in Modelling early in 2012. The project is built on the experience of the OECD/NEA Light Water Reactor (LWR) Uncertainty Analysis in Best-Estimate Modelling (UAM) benchmark activity, but focuses specifically on the peculiarities of HTGR designs and its simulation requirements. Two benchmark problems were defined with the prismatic type design represented by the MHTGR-350 design from General Atomics (GA) while a 250 MW modular pebble bed design, similar to the INET (China) and indirect-cycle PBMR (South Africa) designs are also included. In the paper more detail on the benchmark cases, the different specific phases and tasks and the latest
Reliability Estimation with Uncertainties Consideration for High Power IGBTs in 2.3 MW Wind Turbine Converter System

DEFF Research Database (Denmark)

Kostandyan, Erik; Ma, Ke

2012-01-01

This paper investigates the lifetime of high power IGBTs (insulated gate bipolar transistors) used in large wind turbine applications. Since the IGBTs are critical components in a wind turbine power converter, it is of great importance to assess their reliability in the design phase of the turbine....... Minimum, maximum and average junction temperatures profiles for the grid side IGBTs are estimated at each wind speed input values. The selected failure mechanism is the crack propagation in solder joint under the silicon die. Based on junction temperature profiles and physics of failure model......, the probabilistic and determinist damage models are presented with estimated fatigue lives. Reliably levels were assessed by means of First Order Reliability Method taking into account uncertainties....
Reliability analysis on passive residual heat removal of AP1000 based on Grey model

Energy Technology Data Exchange (ETDEWEB)

Qi, Shi; Zhou, Tao; Shahzad, Muhammad Ali; Li, Yu [North China Electric Power Univ., Beijing (China). School of Nuclear Science and Engineering; Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, Beijing (China); Jiang, Guangming [Nuclear Power Institute of China, Chengdu (China). Science and Technology on Reactor System Design Technology Laboratory

2017-06-15

It is common to base the design of passive systems on the natural laws of physics, such as gravity, heat conduction, inertia. For AP1000, a generation-III reactor, such systems have an inherent safety associated with them due to the simplicity of their structures. However, there is a fairly large amount of uncertainty in the operating conditions of these passive safety systems. In some cases, a small deviation in the design or operating conditions can affect the function of the system. The reliability of the passive residual heat removal is analysed.
Uncertainty Analysis of Resistance Tests in Ata Nutku Ship Model Testing Laboratory of Istanbul Technical University

Directory of Open Access Journals (Sweden)

Cihad DELEN

2015-12-01

Full Text Available In this study, some systematical resistance tests, where were performed in Ata Nutku Ship Model Testing Laboratory of Istanbul Technical University (ITU, have been included in order to determine the uncertainties. Experiments which are conducted in the framework of mathematical and physical rules for the solution of engineering problems, measurements, calculations include uncertainty. To question the reliability of the obtained values, the existing uncertainties should be expressed as quantities. The uncertainty of a measurement system is not known if the results do not carry a universal value. On the other hand, resistance is one of the most important parameters that should be considered in the process of ship design. Ship resistance during the design phase of a ship cannot be determined precisely and reliably due to the uncertainty resources in determining the resistance value that are taken into account. This case may cause negative effects to provide the required specifications in the latter design steps. The uncertainty arising from the resistance test has been estimated and compared for a displacement type ship and high speed marine vehicles according to ITTC 2002 and ITTC 2014 regulations which are related to the uncertainty analysis methods. Also, the advantages and disadvantages of both ITTC uncertainty analysis methods have been discussed.
Sensitivity functions for uncertainty analysis: Sensitivity and uncertainty analysis of reactor performance parameters

International Nuclear Information System (INIS)

Greenspan, E.

1982-01-01

This chapter presents the mathematical basis for sensitivity functions, discusses their physical meaning and information they contain, and clarifies a number of issues concerning their application, including the definition of group sensitivities, the selection of sensitivity functions to be included in the analysis, and limitations of sensitivity theory. Examines the theoretical foundation; criticality reset sensitivities; group sensitivities and uncertainties; selection of sensitivities included in the analysis; and other uses and limitations of sensitivity functions. Gives the theoretical formulation of sensitivity functions pertaining to ''as-built'' designs for performance parameters of the form of ratios of linear flux functionals (such as reaction-rate ratios), linear adjoint functionals, bilinear functions (such as reactivity worth ratios), and for reactor reactivity. Offers a consistent procedure for reducing energy-dependent or fine-group sensitivities and uncertainties to broad group sensitivities and uncertainties. Provides illustrations of sensitivity functions as well as references to available compilations of such functions and of total sensitivities. Indicates limitations of sensitivity theory originating from the fact that this theory is based on a first-order perturbation theory
Quantification of Uncertainty in the Flood Frequency Analysis

Science.gov (United States)

Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.

2017-12-01

Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

Energy Technology Data Exchange (ETDEWEB)

Adams, Brian M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ebeida, Mohamed Salah [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jakeman, John Davis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stephens, John Adam [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vigil, Dena M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wildey, Timothy Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bohnhoff, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hu, Kenneth T. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dalbey, Keith R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauman, Lara E [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hough, Patricia Diane [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-05-01

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Uncertainties in thick-target PIXE analysis

International Nuclear Information System (INIS)

Campbell, J.L.; Cookson, J.A.; Paul, H.

1983-01-01

Thick-target PIXE analysis insolves uncertainties arising from the calculation of thick-target X-ray production in addition to the usual PIXE uncertainties. The calculation demands knowledge of ionization cross-sections, stopping powers and photon attenuation coefficients. Information on these is reviewed critically and a computational method is used to estimate the uncertainties transmitted from this data base into results of thick-target PIXE analyses with reference to particular specimen types using beams of 2-3 MeV protons. A detailed assessment of the accuracy of thick-target PIXE is presented. (orig.)
Re-assessing reliability based on survived loads

NARCIS (Netherlands)

Schweckendiek, T.

2011-01-01

The reliability of flood defenses is often dictated by large uncertainties in the hydraulic loading and the structural resistance. Additional information decreases uncertainty, however, acquiring it is often costly. One source of information, even though in many cases readily available, is hardly
Safety and reliability analysis based on nonprobabilistic methods

International Nuclear Information System (INIS)

Kozin, I.O.; Petersen, K.E.

1996-01-01

Imprecise probabilities, being developed during the last two decades, offer a considerably more general theory having many advantages which make it very promising for reliability and safety analysis. The objective of the paper is to argue that imprecise probabilities are more appropriate tool for reliability and safety analysis, that they allow to model the behavior of nuclear industry objects more comprehensively and give a possibility to solve some problems unsolved in the framework of conventional approach. Furthermore, some specific examples are given from which we can see the usefulness of the tool for solving some reliability tasks
Study on reliability analysis based on multilevel flow models and fault tree method

International Nuclear Information System (INIS)

Chen Qiang; Yang Ming

2014-01-01

Multilevel flow models (MFM) and fault tree method describe the system knowledge in different forms, so the two methods express an equivalent logic of the system reliability under the same boundary conditions and assumptions. Based on this and combined with the characteristics of MFM, a method mapping MFM to fault tree was put forward, thus providing a way to establish fault tree rapidly and realizing qualitative reliability analysis based on MFM. Taking the safety injection system of pressurized water reactor nuclear power plant as an example, its MFM was established and its reliability was analyzed qualitatively. The analysis result shows that the logic of mapping MFM to fault tree is correct. The MFM is easily understood, created and modified. Compared with the traditional fault tree analysis, the workload is greatly reduced and the modeling time is saved. (authors)
Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors: Final Scientific/Technical Report

International Nuclear Information System (INIS)

Vierow, Karen; Aldemir, Tunc

2009-01-01

The project entitled, 'Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors', was conducted as a DOE NERI project collaboration between Texas A and M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.
Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors: Final Scientific/Technical Report

Energy Technology Data Exchange (ETDEWEB)

Vierow, Karen; Aldemir, Tunc

2009-09-10

The project entitled, “Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors”, was conducted as a DOE NERI project collaboration between Texas A&M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.
Reliability Analysis and Test Planning using CAPO-Test for Existing Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Engelund, S.; Faber, Michael Havbro

2000-01-01

Evaluation of the reliability of existing concrete structures often requires that the compressive strength of the concrete is estimated on the basis of tests performed with concrete samples from the structure considered. In this paper the CAPO-test method is considered. The different sources...... of uncertainty related to this method are described. It is shown how the uncertainty in the transformation from the CAPO-test results to estimates of the concrete strength can be modeled. Further, the statistical uncertainty is modeled using Bayesian statistics. Finally, it is shown how reliability-based optimal...... planning of CAPO-tests can be performed taking into account the expected costs due to the CAPO-tests and possible repair or failure of the structure considered. An illustrative example is presented where the CAPO-test is compared with conventional concrete cylinder compression tests performed on cores...
[Reliability theory based on quality risk network analysis for Chinese medicine injection].

Science.gov (United States)

Li, Zheng; Kang, Li-Yuan; Fan, Xiao-Hui

2014-08-01

A new risk analysis method based upon reliability theory was introduced in this paper for the quality risk management of Chinese medicine injection manufacturing plants. The risk events including both cause and effect ones were derived in the framework as nodes with a Bayesian network analysis approach. It thus transforms the risk analysis results from failure mode and effect analysis (FMEA) into a Bayesian network platform. With its structure and parameters determined, the network can be used to evaluate the system reliability quantitatively with probabilistic analytical appraoches. Using network analysis tools such as GeNie and AgenaRisk, we are able to find the nodes that are most critical to influence the system reliability. The importance of each node to the system can be quantitatively evaluated by calculating the effect of the node on the overall risk, and minimization plan can be determined accordingly to reduce their influences and improve the system reliability. Using the Shengmai injection manufacturing plant of SZYY Ltd as a user case, we analyzed the quality risk with both static FMEA analysis and dynamic Bayesian Network analysis. The potential risk factors for the quality of Shengmai injection manufacturing were identified with the network analysis platform. Quality assurance actions were further defined to reduce the risk and improve the product quality.
Modelling of plasma-based dry reforming: how do uncertainties in the input data affect the calculation results?

Science.gov (United States)

Wang, Weizong; Berthelot, Antonin; Zhang, Quanzhi; Bogaerts, Annemie

2018-05-01

One of the main issues in plasma chemistry modeling is that the cross sections and rate coefficients are subject to uncertainties, which yields uncertainties in the modeling results and hence hinders the predictive capabilities. In this paper, we reveal the impact of these uncertainties on the model predictions of plasma-based dry reforming in a dielectric barrier discharge. For this purpose, we performed a detailed uncertainty analysis and sensitivity study. 2000 different combinations of rate coefficients, based on the uncertainty from a log-normal distribution, are used to predict the uncertainties in the model output. The uncertainties in the electron density and electron temperature are around 11% and 8% at the maximum of the power deposition for a 70% confidence level. Still, this can have a major effect on the electron impact rates and hence on the calculated conversions of CO2 and CH4, as well as on the selectivities of CO and H2. For the CO2 and CH4 conversion, we obtain uncertainties of 24% and 33%, respectively. For the CO and H2 selectivity, the corresponding uncertainties are 28% and 14%, respectively. We also identify which reactions contribute most to the uncertainty in the model predictions. In order to improve the accuracy and reliability of plasma chemistry models, we recommend using only verified rate coefficients, and we point out the need for dedicated verification experiments.
A retrospective dosimetry method and its uncertainty analysis

International Nuclear Information System (INIS)

Zhang, L.; Jia, D.; Dai, G.

2000-01-01

The main aim of a radiation epidemiological study is to assess the risk of the population exposed to ionizing radiation. The actual work of the assessment may be very difficult because dose information about the population is often indirect and incomplete. It is very important, therefore, to find a way of estimating reasonable and reliable doses of the population by a retrospective method from limited information. In order to provide reasonable dose information for the cohort study of Chinese medical diagnostic X-ray workers, a retrospective dosimetry method was established. In China, a cohort study of more than 27,000 medical diagnostic X-ray workers, with 25,000 controls, has been carried out for about fifteen years in order to assess the risk to an occupationally exposed population. Obviously, a key to the success of the study is to obtain reliable and reasonable results of dose estimation by the dose reconstruction method. Before 1985, there was a lack of information regarding personal dose measured directly; however, we can obtain other indirect information. Examples are information about working loads from the documents of the hospitals, information about operational conditions of the workers of different statuses by a survey of occupational history, and the exposure levels of various working conditions by some simulation methods. The information for estimating organ dose can also be obtained by simulating experiments with a phantom. Based on the information mentioned above, a mathematical model and computerizing system for dose reconstruction of this occupational population was design and developed. Uncertainty analysis very important for dose reconstruction. The sources of uncertainty of our study are coming from two fields. One is coming from the mode of dose reconstruction. Another is coming from the survey of the occupational history. In the result reported, main results of the uncertainty will be presented. In order to control the uncertainty of the
A holistic framework of degradation modeling for reliability analysis and maintenance optimization of nuclear safety systems

International Nuclear Information System (INIS)

Lin, Yanhui

2016-01-01

Components of nuclear safety systems are in general highly reliable, which leads to a difficulty in modeling their degradation and failure behaviors due to the limited amount of data available. Besides, the complexity of such modeling task is increased by the fact that these systems are often subject to multiple competing degradation processes and that these can be dependent under certain circumstances, and influenced by a number of external factors (e.g. temperature, stress, mechanical shocks, etc.). In this complicated problem setting, this PhD work aims to develop a holistic framework of models and computational methods for the reliability-based analysis and maintenance optimization of nuclear safety systems taking into account the available knowledge on the systems, degradation and failure behaviors, their dependencies, the external influencing factors and the associated uncertainties.The original scientific contributions of the work are: (1) For single components, we integrate random shocks into multi-state physics models for component reliability analysis, considering general dependencies between the degradation and two types of random shocks. (2) For multi-component systems (with a limited number of components):(a) a piecewise-deterministic Markov process modeling framework is developed to treat degradation dependency in a system whose degradation processes are modeled by physics-based models and multi-state models; (b) epistemic uncertainty due to incomplete or imprecise knowledge is considered and a finite-volume scheme is extended to assess the (fuzzy) system reliability; (c) the mean absolute deviation importance measures are extended for components with multiple dependent competing degradation processes and subject to maintenance; (d) the optimal maintenance policy considering epistemic uncertainty and degradation dependency is derived by combining finite-volume scheme, differential evolution and non-dominated sorting differential evolution; (e) the
Development and application of objective uncertainty measures for nuclear power plant transient analysis[Dissertation 3897

Energy Technology Data Exchange (ETDEWEB)

Vinai, P

2007-10-15

For the development, design and licensing of a nuclear power plant (NPP), a sound safety analysis is necessary to study the diverse physical phenomena involved in the system behaviour under operational and transient conditions. Such studies are based on detailed computer simulations. With the progresses achieved in computer technology and the greater availability of experimental and plant data, the use of best estimate codes for safety evaluations has gained increasing acceptance. The application of best estimate safety analysis has raised new problems that need to be addressed: it has become more crucial to assess as to how reliable code predictions are, especially when they need to be compared against safety limits that must not be crossed. It becomes necessary to identify and quantify the various possible sources of uncertainty that affect the reliability of the results. Currently, such uncertainty evaluations are generally based on experts' opinion. In the present research, a novel methodology based on a non-parametric statistical approach has been developed for objective quantification of best-estimate code uncertainties related to the physical models used in the code. The basis is an evaluation of the accuracy of a given physical model achieved by comparing its predictions with experimental data from an appropriate set of separate-effect tests. The differences between measurements and predictions can be considered stochastically distributed, and thus a statistical approach can be employed. The first step was the development of a procedure for investigating the dependence of a given physical model's accuracy on the experimental conditions. Each separate-effect test effectively provides a random sample of discrepancies between measurements and predictions, corresponding to a location in the state space defined by a certain number of independent system variables. As a consequence, the samples of 'errors', achieved from analysis of the entire
Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh

Science.gov (United States)

Mortuza, M. R.; Demissie, Y.; Li, H. Y.

2014-12-01

Increased frequency of extreme precipitations, especially those with multiday durations, are responsible for recent urban floods and associated significant losses of lives and infrastructures in Bangladesh. Reliable and routinely updated estimation of the frequency of occurrence of such extreme precipitation events are thus important for developing up-to-date hydraulic structures and stormwater drainage system that can effectively minimize future risk from similar events. In this study, we have updated the intensity-duration-frequency (IDF) curves for Bangladesh using daily precipitation data from 1961 to 2010 and quantified associated uncertainties. Regional frequency analysis based on L-moments is applied on 1-day, 2-day and 5-day annual maximum precipitation series due to its advantages over at-site estimation. The regional frequency approach pools the information from climatologically similar sites to make reliable estimates of quantiles given that the pooling group is homogeneous and of reasonable size. We have used Region of influence (ROI) approach along with homogeneity measure based on L-moments to identify the homogenous pooling groups for each site. Five 3-parameter distributions (i.e., Generalized Logistic, Generalized Extreme value, Generalized Normal, Pearson Type Three, and Generalized Pareto) are used for a thorough selection of appropriate models that fit the sample data. Uncertainties related to the selection of the distributions and historical data are quantified using the Bayesian Model Averaging and Balanced Bootstrap approaches respectively. The results from this study can be used to update the current design and management of hydraulic structures as well as in exploring spatio-temporal variations of extreme precipitation and associated risk.

Effect of activation cross section uncertainties in transmutation analysis of realistic low-activation steels for IFMIF

Energy Technology Data Exchange (ETDEWEB)

Cabellos, O.; Garcya-Herranz, N.; Sanz, J. [Institute of Nuclear Fusion, UPM, Madrid (Spain); Cabellos, O.; Garcya-Herranz, N.; Fernandez, P.; Fernandez, B. [Dept. of Nuclear Engineering, UPM, Madrid (Spain); Sanz, J. [Dept. of Power Engineering, UNED, Madrid (Spain); Reyes, S. [Safety, Environment and Health Group, ITER Joint Work Site, Cadarache Center (France)

2008-07-01

We address uncertainty analysis to draw conclusions on the reliability of the activation calculation in the International Fusion Materials Irradiation Facility (IFMIF) under the potential impact of activation cross section uncertainties. The Monte Carlo methodology implemented in ACAB code gives the uncertainty estimates due to the synergetic/global effect of the complete set of cross section uncertainties. An element-by-element analysis has been demonstrated as a helpful tool to easily analyse the transmutation performance of irradiated materials.The uncertainty analysis results showed that for times over about 24 h the relative error in the contact dose rate can be as large as 23 per cent. We have calculated the effect of cross section uncertainties in the IFMIF activation of all different elements. For EUROFER, uncertainties in H and He elements are 7.3% and 5.6%, respectively. We have found significant uncertainties in the transmutation response for C, P and Nb.
Uncertainty quantification and error analysis

Energy Technology Data Exchange (ETDEWEB)

Higdon, Dave M [Los Alamos National Laboratory; Anderson, Mark C [Los Alamos National Laboratory; Habib, Salman [Los Alamos National Laboratory; Klein, Richard [Los Alamos National Laboratory; Berliner, Mark [OHIO STATE UNIV.; Covey, Curt [LLNL; Ghattas, Omar [UNIV OF TEXAS; Graziani, Carlo [UNIV OF CHICAGO; Seager, Mark [LLNL; Sefcik, Joseph [LLNL; Stark, Philip [UC/BERKELEY; Stewart, James [SNL

2010-01-01

UQ studies all sources of error and uncertainty, including: systematic and stochastic measurement error; ignorance; limitations of theoretical models; limitations of numerical representations of those models; limitations on the accuracy and reliability of computations, approximations, and algorithms; and human error. A more precise definition for UQ is suggested below.
Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor

International Nuclear Information System (INIS)

Wilson, G.E.

1992-01-01

The Analytic Hierarchy Process (AHP) has been used to help determine the importance of components and phenomena in thermal-hydraulic safety analyses of nuclear reactors. The AHP results are based, in part on expert opinion. Therefore, it is prudent to evaluate the uncertainty of the AHP ranks of importance. Prior applications have addressed uncertainty with experimental data comparisons and bounding sensitivity calculations. These methods work well when a sufficient experimental data base exists to justify the comparisons. However, in the case of limited or no experimental data the size of the uncertainty is normally made conservatively large. Accordingly, the author has taken another approach, that of performing a statistically based uncertainty analysis. The new work is based on prior evaluations of the importance of components and phenomena in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor (ANSR), a new facility now in the design phase. The uncertainty during large break loss of coolant, and decay heat removal scenarios is estimated by assigning a probability distribution function (pdf) to the potential error in the initial expert estimates of pair-wise importance between the components. Using a Monte Carlo sampling technique, the error pdfs are propagated through the AHP software solutions to determine a pdf of uncertainty in the system wide importance of each component. To enhance the generality of the results, study of one other problem having different number of elements is reported, as are the effects of a larger assumed pdf error in the expert ranks. Validation of the Monte Carlo sample size and repeatability are also documented
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual

Energy Technology Data Exchange (ETDEWEB)

Adams, Brian M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ebeida, Mohamed Salah [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldred, Michael S [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jakeman, John Davis [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stephens, John Adam [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vigil, Dena M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wildey, Timothy Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bohnhoff, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hu, Kenneth T. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Dalbey, Keith R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauman, Lara E [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hough, Patricia Diane [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2014-05-01

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quanti cation, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.
Uncertainty and sensitivity analysis of electro-mechanical impedance based SHM system

International Nuclear Information System (INIS)

Rosiek, M; Martowicz, A; Uhl, T

2010-01-01

The paper deals with the application of uncertainty and sensitivity analysis performed for FE simulations for electro-mechanical impedance based SHM system. The measurement of electro-mechanical impedance allows to follow changes of mechanical properties of monitored construction. Therefore it can be effectively applied to conclude about presence of damage. Coupled FE simulations have been carried out for simultaneous consideration of both structural dynamics and piezoelectric properties of a simple beam with bonded transducer. Several indexes have been used to assess the damage growth. In the paper the results obtained with both deterministic and stochastic simulations are shown and discussed. First, the relationship between size of introduced damage and its indexes has been studied. Second, ranges of variation of selected model properties have been assumed to find relationships between them and damage indexes. The most influential parameters have been found. Finally, the overall propagation of considered uncertainty has been assessed and related histograms plotted to discuss effectiveness and robustness of tested damage indexes based on the measurement of electro-mechanical impedance.
Reliability-based design of a retaining wall

OpenAIRE

Kim, John Sang

1995-01-01

A retaining wall is subject to various limit states such as sliding, overturning and bearing capacity, and it can fail by anyone of them. Since a great deal of uncertainty is involved in the analysis of the limit states~ the use of detenninistic conventional safety factors may produce a misleading result. The main objective of this study is to develop a procedure for the optimum design of a retaining wall by using the reliability theory. Typical gravity retaining walls with fou...
Comparison of methods for dependency determination between human failure events within human reliability analysis

International Nuclear Information System (INIS)

Cepis, M.

2007-01-01

The Human Reliability Analysis (HRA) is a highly subjective evaluation of human performance, which is an input for probabilistic safety assessment, which deals with many parameters of high uncertainty. The objective of this paper is to show that subjectivism can have a large impact on human reliability results and consequently on probabilistic safety assessment results and applications. The objective is to identify the key features, which may decrease of subjectivity of human reliability analysis. Human reliability methods are compared with focus on dependency comparison between Institute Jozef Stefan - Human Reliability Analysis (IJS-HRA) and Standardized Plant Analysis Risk Human Reliability Analysis (SPAR-H). Results show large differences in the calculated human error probabilities for the same events within the same probabilistic safety assessment, which are the consequence of subjectivity. The subjectivity can be reduced by development of more detailed guidelines for human reliability analysis with many practical examples for all steps of the process of evaluation of human performance. (author)
Comparison of Methods for Dependency Determination between Human Failure Events within Human Reliability Analysis

International Nuclear Information System (INIS)

Cepin, M.

2008-01-01

The human reliability analysis (HRA) is a highly subjective evaluation of human performance, which is an input for probabilistic safety assessment, which deals with many parameters of high uncertainty. The objective of this paper is to show that subjectivism can have a large impact on human reliability results and consequently on probabilistic safety assessment results and applications. The objective is to identify the key features, which may decrease subjectivity of human reliability analysis. Human reliability methods are compared with focus on dependency comparison between Institute Jozef Stefan human reliability analysis (IJS-HRA) and standardized plant analysis risk human reliability analysis (SPAR-H). Results show large differences in the calculated human error probabilities for the same events within the same probabilistic safety assessment, which are the consequence of subjectivity. The subjectivity can be reduced by development of more detailed guidelines for human reliability analysis with many practical examples for all steps of the process of evaluation of human performance
Reliability analysis - systematic approach based on limited data

International Nuclear Information System (INIS)

Bourne, A.J.

1975-11-01

The initial approaches required for reliability analysis are outlined. These approaches highlight the system boundaries, examine the conditions under which the system is required to operate, and define the overall performance requirements. The discussion is illustrated by a simple example of an automatic protective system for a nuclear reactor. It is then shown how the initial approach leads to a method of defining the system, establishing performance parameters of interest and determining the general form of reliability models to be used. The overall system model and the availability of reliability data at the system level are next examined. An iterative process is then described whereby the reliability model and data requirements are systematically refined at progressively lower hierarchic levels of the system. At each stage, the approach is illustrated with examples from the protective system previously described. The main advantages of the approach put forward are the systematic process of analysis, the concentration of assessment effort in the critical areas and the maximum use of limited reliability data. (author)
Maintenance management of railway infrastructures based on reliability analysis

International Nuclear Information System (INIS)

Macchi, Marco; Garetti, Marco; Centrone, Domenico; Fumagalli, Luca; Piero Pavirani, Gian

2012-01-01

Railway infrastructure maintenance plays a crucial role for rail transport. It aims at guaranteeing safety of operations and availability of railway tracks and related equipment for traffic regulation. Moreover, it is one major cost for rail transport operations. Thus, the increased competition in traffic market is asking for maintenance improvement, aiming at the reduction of maintenance expenditures while keeping the safety of operations. This issue is addressed by the methodology presented in the paper. The first step of the methodology consists of a family-based approach for the equipment reliability analysis; its purpose is the identification of families of railway items which can be given the same reliability targets. The second step builds the reliability model of the railway system for identifying the most critical items, given a required service level for the transportation system. The two methods have been implemented and tested in practical case studies, in the context of Rete Ferroviaria Italiana, the Italian public limited company for railway transportation.
Consideration of vertical uncertainty in elevation-based sea-level rise assessments: Mobile Bay, Alabama case study

Science.gov (United States)

Gesch, Dean B.

2013-01-01

The accuracy with which coastal topography has been mapped directly affects the reliability and usefulness of elevationbased sea-level rise vulnerability assessments. Recent research has shown that the qualities of the elevation data must be well understood to properly model potential impacts. The cumulative vertical uncertainty has contributions from elevation data error, water level data uncertainties, and vertical datum and transformation uncertainties. The concepts of minimum sealevel rise increment and minimum planning timeline, important parameters for an elevation-based sea-level rise assessment, are used in recognition of the inherent vertical uncertainty of the underlying data. These concepts were applied to conduct a sea-level rise vulnerability assessment of the Mobile Bay, Alabama, region based on high-quality lidar-derived elevation data. The results that detail the area and associated resources (land cover, population, and infrastructure) vulnerable to a 1.18-m sea-level rise by the year 2100 are reported as a range of values (at the 95% confidence level) to account for the vertical uncertainty in the base data. Examination of the tabulated statistics about land cover, population, and infrastructure in the minimum and maximum vulnerable areas shows that these resources are not uniformly distributed throughout the overall vulnerable zone. The methods demonstrated in the Mobile Bay analysis provide an example of how to consider and properly account for vertical uncertainty in elevation-based sea-level rise vulnerability assessments, and the advantages of doing so.
Solution-verified reliability analysis and design of bistable MEMS using error estimation and adaptivity.

Energy Technology Data Exchange (ETDEWEB)

Eldred, Michael Scott; Subia, Samuel Ramirez; Neckels, David; Hopkins, Matthew Morgan; Notz, Patrick K.; Adams, Brian M.; Carnes, Brian; Wittwer, Jonathan W.; Bichon, Barron J.; Copps, Kevin D.

2006-10-01

This report documents the results for an FY06 ASC Algorithms Level 2 milestone combining error estimation and adaptivity, uncertainty quantification, and probabilistic design capabilities applied to the analysis and design of bistable MEMS. Through the use of error estimation and adaptive mesh refinement, solution verification can be performed in an automated and parameter-adaptive manner. The resulting uncertainty analysis and probabilistic design studies are shown to be more accurate, efficient, reliable, and convenient.
Reliability analysis of microcomputer boards and computer based systems important to safety of nuclear plants

International Nuclear Information System (INIS)

Shrikhande, S.V.; Patil, V.K.; Ganesh, G.; Biswas, B.; Patil, R.K.

2010-01-01

Computer Based Systems (CBS) are employed in Indian nuclear plants for protection, control and monitoring purpose. For forthcoming CBS, Reactor Control Division has designed and developed a new standardized family of microcomputer boards qualified to stringent requirements of nuclear industry. These boards form the basic building blocks of CBS. Reliability analysis of these boards is being carried out using analysis package based on MIL-STD-217Plus methodology. The estimated failure rate values of these standardized microcomputer boards will be useful for reliability assessment of these systems. The paper presents reliability analysis of microcomputer boards and case study of a CBS system built using these boards. (author)
Phenomenological uncertainty analysis of early containment failure at severe accident of nuclear power plant

Energy Technology Data Exchange (ETDEWEB)

Lee, Su Won

2011-02-15

The severe accident has inherently significant uncertainty due to wide range of conditions and performing experiments, validation and practical application are extremely difficult because of its high temperature and pressure. Although internal and external researches were put into practice, the reference used in Korean nuclear plants were foreign data of 1980s and safety analysis as the probabilistic safety assessment has not applied the newest methodology. Also, it is applied to containment pressure formed into point value as results of thermal hydraulic analysis to identify the probability of containment failure in level 2 PSA. In this paper, the uncertainty analysis methods for phenomena of severe accident influencing early containment failure were developed, the uncertainty analysis that apply Korean nuclear plants using the MELCOR code was performed and it is a point of view to present the distribution of containment pressure as a result of uncertainty analysis. Because early containment failure is important factor of Large Early Release Frequency(LERF) that is used as representative criteria of decision-making in nuclear power plants, it was selected in this paper among various modes of containment failure. Important phenomena of early containment failure at severe accident based on previous researches were comprehended and methodology of 7th steps to evaluate uncertainty was developed. The MELCOR input for analysis of the severe accident reflected natural circulation flow was developed and the accident scenario for station black out that was representative initial event of early containment failure was determined. By reviewing the internal model and correlation for MELCOR model relevant important phenomena of early containment failure, the uncertainty factors which could affect on the uncertainty were founded and the major factors were finally identified through the sensitivity analysis. In order to determine total number of MELCOR calculations which can
Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction.

Science.gov (United States)

Carleton, W Christopher; Campbell, David; Collard, Mark

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating-the most common chronometric technique in archaeological and palaeoenvironmental research-creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20-30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence.
Study on Feasibility of Applying Function Approximation Moment Method to Achieve Reliability-Based Design Optimization

International Nuclear Information System (INIS)

Huh, Jae Sung; Kwak, Byung Man

2011-01-01

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated
Reliability Evaluation of Bridges Based on Nonprobabilistic Response Surface Limit Method

OpenAIRE

Chen, Xuyong; Chen, Qian; Bian, Xiaoya; Fan, Jianping

2017-01-01

Due to many uncertainties in nonprobabilistic reliability assessment of bridges, the limit state function is generally unknown. The traditional nonprobabilistic response surface method is a lengthy and oscillating iteration process and leads to difficultly solving the nonprobabilistic reliability index. This article proposes a nonprobabilistic response surface limit method based on the interval model. The intention of this method is to solve the upper and lower limits of the nonprobabilistic ...
Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models.

Science.gov (United States)

Hazarika, Subhashis; Biswas, Ayan; Shen, Han-Wei

2018-01-01

Distributions are often used to model uncertainty in many scientific datasets. To preserve the correlation among the spatially sampled grid locations in the dataset, various standard multivariate distribution models have been proposed in visualization literature. These models treat each grid location as a univariate random variable which models the uncertainty at that location. Standard multivariate distributions (both parametric and nonparametric) assume that all the univariate marginals are of the same type/family of distribution. But in reality, different grid locations show different statistical behavior which may not be modeled best by the same type of distribution. In this paper, we propose a new multivariate uncertainty modeling strategy to address the needs of uncertainty modeling in scientific datasets. Our proposed method is based on a statistically sound multivariate technique called Copula, which makes it possible to separate the process of estimating the univariate marginals and the process of modeling dependency, unlike the standard multivariate distributions. The modeling flexibility offered by our proposed method makes it possible to design distribution fields which can have different types of distribution (Gaussian, Histogram, KDE etc.) at the grid locations, while maintaining the correlation structure at the same time. Depending on the results of various standard statistical tests, we can choose an optimal distribution representation at each location, resulting in a more cost efficient modeling without significantly sacrificing on the analysis quality. To demonstrate the efficacy of our proposed modeling strategy, we extract and visualize uncertain features like isocontours and vortices in various real world datasets. We also study various modeling criterion to help users in the task of univariate model selection.
Systematic Analysis Of Ocean Colour Uncertainties

Science.gov (United States)

Lavender, Samantha

2013-12-01

This paper reviews current research into the estimation of uncertainties as a pixel-based measure to aid non- specialist users of remote sensing products. An example MERIS image, captured on the 28 March 2012, was processed with above-water atmospheric correction code. This was initially based on both the Antoine & Morel Standard Atmospheric Correction, with Bright Pixel correction component, and Doerffer Neural Network coastal water's approach. It's showed that analysis of the atmospheric by-products yield important information about the separation of the atmospheric and in-water signals, helping to sign-post possible uncertainties in the atmospheric correction results. Further analysis has concentrated on implementing a ‘simplistic' atmospheric correction so that the impact of changing the input auxiliary data can be analysed; the influence of changing surface pressure is demonstrated. Future work will focus on automating the analysis, so that the methodology can be implemented within an operational system.
Reduction and Uncertainty Analysis of Chemical Mechanisms Based on Local and Global Sensitivities

Science.gov (United States)

Esposito, Gaetano

Numerical simulations of critical reacting flow phenomena in hypersonic propulsion devices require accurate representation of finite-rate chemical kinetics. The chemical kinetic models available for hydrocarbon fuel combustion are rather large, involving hundreds of species and thousands of reactions. As a consequence, they cannot be used in multi-dimensional computational fluid dynamic calculations in the foreseeable future due to the prohibitive computational cost. In addition to the computational difficulties, it is also known that some fundamental chemical kinetic parameters of detailed models have significant level of uncertainty due to limited experimental data available and to poor understanding of interactions among kinetic parameters. In the present investigation, local and global sensitivity analysis techniques are employed to develop a systematic approach of reducing and analyzing detailed chemical kinetic models. Unlike previous studies in which skeletal model reduction was based on the separate analysis of simple cases, in this work a novel strategy based on Principal Component Analysis of local sensitivity values is presented. This new approach is capable of simultaneously taking into account all the relevant canonical combustion configurations over different composition, temperature and pressure conditions. Moreover, the procedure developed in this work represents the first documented inclusion of non-premixed extinction phenomena, which is of great relevance in hypersonic combustors, in an automated reduction algorithm. The application of the skeletal reduction to a detailed kinetic model consisting of 111 species in 784 reactions is demonstrated. The resulting reduced skeletal model of 37--38 species showed that the global ignition/propagation/extinction phenomena of ethylene-air mixtures can be predicted within an accuracy of 2% of the full detailed model. The problems of both understanding non-linear interactions between kinetic parameters and

Uncertainty theory

CERN Document Server

Liu, Baoding

2015-01-01

When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, c...
A methodology for uncertainty analysis of reference equations of state

DEFF Research Database (Denmark)

Cheung, Howard; Frutiger, Jerome; Bell, Ian H.

We present a detailed methodology for the uncertainty analysis of reference equations of state (EOS) based on Helmholtz energy. In recent years there has been an increased interest in uncertainties of property data and process models of thermal systems. In the literature there are various...... for uncertainty analysis is suggested as a tool for EOS. The uncertainties of the EOS properties are calculated from the experimental values and the EOS model structure through the parameter covariance matrix and subsequent linear error propagation. This allows reporting the uncertainty range (95% confidence...
Identifying the effects of parameter uncertainty on the reliability of riverbank stability modelling

Science.gov (United States)

Samadi, A.; Amiri-Tokaldany, E.; Darby, S. E.

2009-05-01

Bank retreat is a key process in fluvial dynamics affecting a wide range of physical, ecological and socioeconomic issues in the fluvial environment. To predict the undesirable effects of bank retreat and to inform effective measures to prevent it, a wide range of bank stability models have been presented in the literature. These models typically express bank stability by defining a factor of safety as the ratio of driving and resisting forces acting on the incipient failure block. These forces are affected by a range of controlling factors that include such aspects as the bank profile (bank height and angle), the geotechnical properties of the bank materials, as well as the hydrological status of the riverbanks. In this paper we evaluate the extent to which uncertainties in the parameterization of these controlling factors feed through to influence the reliability of the resulting bank stability estimate. This is achieved by employing a simple model of riverbank stability with respect to planar failure (which is the most common type of bank stability model) in a series of sensitivity tests and Monte Carlo analyses to identify, for each model parameter, the range of values that induce significant changes in the simulated factor of safety. These identified parameter value ranges are compared to empirically derived parameter uncertainties to determine whether they are likely to confound the reliability of the resulting bank stability calculations. Our results show that parameter uncertainties are typically high enough that the likelihood of generating unreliable predictions is typically very high (> ˜ 80% for predictions requiring a precision of < ± 15%). Because parameter uncertainties are derived primarily from the natural variability of the parameters, rather than measurement errors, much more careful attention should be paid to field sampling strategies, such that the parameter uncertainties and consequent prediction unreliabilities can be quantified more
IAEA Coordinated Research Project on HTGR Reactor Physics, Thermal-hydraulics and Depletion Uncertainty Analysis

Energy Technology Data Exchange (ETDEWEB)

Strydom, Gerhard [Idaho National Lab. (INL), Idaho Falls, ID (United States); Bostelmann, F. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-09-01

The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of HTGR design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The predictive capability of coupled neutronics/thermal-hydraulics and depletion simulations for reactor design and safety analysis can be assessed with sensitivity analysis (SA) and uncertainty analysis (UA) methods. Uncertainty originates from errors in physical data, manufacturing uncertainties, modelling and computational algorithms. (The interested reader is referred to the large body of published SA and UA literature for a more complete overview of the various types of uncertainties, methodologies and results obtained). SA is helpful for ranking the various sources of uncertainty and error in the results of core analyses. SA and UA are required to address cost, safety, and licensing needs and should be applied to all aspects of reactor multi-physics simulation. SA and UA can guide experimental, modelling, and algorithm research and development. Current SA and UA rely either on derivative-based methods such as stochastic sampling methods or on generalized perturbation theory to obtain sensitivity coefficients. Neither approach addresses all needs. In order to benefit from recent advances in modelling and simulation and the availability of new covariance data (nuclear data uncertainties) extensive sensitivity and uncertainty studies are needed for quantification of the impact of different sources of uncertainties on the design and safety parameters of HTGRs. Only a parallel effort in advanced simulation and in nuclear data improvement will be able to provide designers with more robust and well validated calculation tools to meet design target accuracies. In February 2009, the Technical Working Group on Gas-Cooled Reactors (TWG-GCR) of the International Atomic Energy Agency (IAEA) recommended that the proposed Coordinated Research Program (CRP) on
Uncertainty analysis of LBLOCA for Advanced Heavy Water Reactor

International Nuclear Information System (INIS)

Srivastava, A.; Lele, H.G.; Ghosh, A.K.; Kushwaha, H.S.

2008-01-01

The main objective of safety analysis is to demonstrate in a robust way that all safety requirements are met, i.e. sufficient margins exist between real values of important parameters and their threshold values at which damage of the barriers against release of radioactivity would occur. As stated in the IAEA Safety Requirements for Design of NPPs 'a safety analysis of the plant design shall be conducted in which methods of both deterministic and probabilistic analysis shall be applied'. It is required that 'the computer programs, analytical methods and plant models used in the safety analysis shall be verified and validated, and adequate consideration shall be given to uncertainties'. Uncertainties are present in calculations due to the computer codes, initial and boundary conditions, plant state, fuel parameters, scaling and numerical solution algorithm. All conservative approaches, still widely used, were introduced to cover uncertainties due to limited capability for modelling and understanding of physical phenomena at the early stages of safety analysis. The results obtained by this approach are quite unrealistic and the level of conservatism is not fully known. Another approach is the use of Best Estimate (BE) codes with realistic initial and boundary conditions. If this approach is selected, it should be based on statistically combined uncertainties for plant initial and boundary conditions, assumptions and code models. The current trends are going into direction of the best estimate code with some conservative assumptions of the system with realistic input data with uncertainty analysis. The BE analysis with evaluation of uncertainties offers, in addition, a way to quantify the existing plant safety margins. Its broader use in the future is therefore envisaged, even though it is not always feasible because of the difficulty of quantifying code uncertainties with sufficiently narrow range for every phenomenon and for each accident sequence. In this paper
Reliability and risk analysis data base development: an historical perspective

International Nuclear Information System (INIS)

Fragola, Joseph R.

1996-01-01

Galvagni, R. Risk based decision analysis in design. Fourth SRA Europe Conference Proceedings, Rome, Italy, 18-20 October 1993). These factors, although they continue to be heuristically based, attempt to account for uncertainties in the design environment (e.g., the load spectra) and residual materials defects (Fragola, J.R. et al., Investigation of the risk implications of space shuttle solid rocket booster chamber pressure excursions. SAIC Document No. SAIC/NY 95-01-10, New York, NY). Although the approaches may appear different, at least at first glance, the intention in both the insurance and design arenas was to establish an 'infrastructure of confidence' to enable rational decision making for future endeavours. Maturity in the design process of conventional structures such as bridges, buildings, boilers, and highways has led to the loss of recognition of the role that robustness plays in these designs to qualify them against their normal failure environment. So routinely do we expect these designs to survive that we tend to think of the individual failures (which do occur on occasion) as isolated 'freak' accidents. Attempts to uncover potential underlying classes and document associated attributes are rare, and even when they are undertaken 'human error' or 'one-of-a-kind accidents' is often cited as the major cause which somehow seems to absolve the analyst from the responsibility of further data collection (Levy, M. and Salvadori, M., Why Buildings Fall Down, W.W. Norton and Co., New York, NY, 1992; Pecht, M., Nash, F.R. and Long, J.H., Understanding and solving the real reliability assurance problems. 1995 Proceedings of Annual RAMS Symposium, IEEE, New York, NY, 1995). The confusion has proliferated to the point where legitimate calls for scepticism regarding the scant data resources available (Evans, R.A., Bayes paradox. IEEE Trans. Reliab., R-31 (1982) 321) have given way to cries that some data sources be abandoned altogether (Cushing, M. et al., Comparison
Dynamic decision-making for reliability and maintenance analysis of manufacturing systems based on failure effects

Science.gov (United States)

Zhang, Ding; Zhang, Yingjie

2017-09-01

A framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.
Reliability Analysis of Adhesive Bonded Scarf Joints

DEFF Research Database (Denmark)

Kimiaeifar, Amin; Toft, Henrik Stensgaard; Lund, Erik

2012-01-01

element analysis (FEA). For the reliability analysis a design equation is considered which is related to a deterministic code-based design equation where reliability is secured by partial safety factors together with characteristic values for the material properties and loads. The failure criteria......A probabilistic model for the reliability analysis of adhesive bonded scarfed lap joints subjected to static loading is developed. It is representative for the main laminate in a wind turbine blade subjected to flapwise bending. The structural analysis is based on a three dimensional (3D) finite...... are formulated using a von Mises, a modified von Mises and a maximum stress failure criterion. The reliability level is estimated for the scarfed lap joint and this is compared with the target reliability level implicitly used in the wind turbine standard IEC 61400-1. A convergence study is performed to validate...
ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

Directory of Open Access Journals (Sweden)

Z.-G. Zhou

2016-06-01

Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.
Where do uncertainties reside within environmental risk assessments? Expert opinion on uncertainty distributions for pesticide risks to surface water organisms.

Science.gov (United States)

Skinner, Daniel J C; Rocks, Sophie A; Pollard, Simon J T

2016-12-01

A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimensions of uncertainty: its level (the severity of uncertainty, ranging from determinism to ignorance); nature (whether the uncertainty is epistemic or aleatory); and location (the data source or area in which the uncertainty arises). Risk characterisation contains the highest median levels of uncertainty, associated with estimating, aggregating and evaluating the magnitude of risks. Regarding the locations in which uncertainty is manifest, data uncertainty is dominant in problem formulation, exposure assessment and effects assessment. The comprehensive description of uncertainty described will enable risk analysts to prioritise the required phases, groups of tasks, or individual tasks within a risk analysis according to the highest levels of uncertainty, the potential for uncertainty to be reduced or quantified, or the types of location-based uncertainty, thus aiding uncertainty prioritisation during environmental risk assessments. In turn, it is expected to inform investment in uncertainty reduction or targeted risk management action. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
Uncertainty analysis technique of dynamic response and cumulative damage properties of piping system

International Nuclear Information System (INIS)

Suzuki, Kohei; Aoki, Shigeru; Hara, Fumio; Hanaoka, Masaaki; Yamashita, Tadashi.

1982-01-01

It is a technologically important subject to establish the method of uncertainty analysis statistically examining the variation of the earthquake response and damage properties of equipment and piping system due to the change of input load and the parameters of structural system, for evaluating the aseismatic capability and dynamic structural reliability of these systems. The uncertainty in the response and damage properties when equipment and piping system are subjected to excessive vibration load is mainly dependent on the irregularity of acting input load such as the unsteady vibration of earthquakes, and structural uncertainty in forms and dimensions. This study is the basic one to establish the method for evaluating the uncertainty in the cumulative damage property at the time of resonant vibration of piping system due to the disperse of structural parameters with a simple model. First, the piping models with simple form were broken by resonant vibration, and the uncertainty in the cumulative damage property was evaluated. Next, the response analysis using an elasto-plastic mechanics model was performed by numerical simulation. Finally, the method of uncertainty analysis for response and damage properties by the perturbation method utilizing equivalent linearization was proposed, and its propriety was proved. (Kako, I.)
Deterministic uncertainty analysis

International Nuclear Information System (INIS)

Worley, B.A.

1987-01-01

Uncertainties of computer results are of primary interest in applications such as high-level waste (HLW) repository performance assessment in which experimental validation is not possible or practical. This work presents an alternate deterministic approach for calculating uncertainties that has the potential to significantly reduce the number of computer runs required for conventional statistical analysis. 7 refs., 1 fig
Sensitivity and uncertainty analysis

CERN Document Server

Cacuci, Dan G; Navon, Ionel Michael

2005-01-01

As computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable scientific tools. Sensitivity and Uncertainty Analysis. Volume I: Theory focused on the mathematical underpinnings of two important methods for such analyses: the Adjoint Sensitivity Analysis Procedure and the Global Adjoint Sensitivity Analysis Procedure. This volume concentrates on the practical aspects of performing these analyses for large-scale systems. The applications addressed include two-phase flow problems, a radiative c
Validation of Fuel Performance Uncertainty for RIA Safety Analysis

Energy Technology Data Exchange (ETDEWEB)

Park, Nam-Gyu; Yoo, Jong-Sung; Jung, Yil-Sup [KEPCO Nuclear Fuel Co., Daejeon (Korea, Republic of)

2016-10-15

To achieve this the computer code performance has to be validated based on the experimental results. And for the uncertainty quantification, important uncertainty parameters need to be selected, and combined uncertainty has to be evaluated with an acceptable statistical treatment. And important uncertainty parameters to the rod performance such as fuel enthalpy, fission gas release, cladding hoop strain etc. were chosen through the rigorous sensitivity studies. And their validity has been assessed by utilizing the experimental results, which were tested in CABRI and NSRR. Analysis results revealed that several tested rods were not bounded within combined fuel performance uncertainty. Assessment of fuel performance with an extended fuel power uncertainty on tested rods in NSRR and CABRI has been done. Analysis results showed that several tested rods were not bounded within calculated fuel performance uncertainty. This implies that the currently considered uncertainty range of the parameters is not enough to cover the fuel performance sufficiently.
Reliability analysis of reactor pressure vessel intensity

International Nuclear Information System (INIS)

Zheng Liangang; Lu Yongbo

2012-01-01

This paper performs the reliability analysis of reactor pressure vessel (RPV) with ANSYS. The analysis method include direct Monte Carlo Simulation method, Latin Hypercube Sampling, central composite design and Box-Behnken Matrix design. The RPV integrity reliability under given input condition is proposed. The result shows that the effects on the RPV base material reliability are internal press, allowable basic stress and elasticity modulus of base material in descending order, and the effects on the bolt reliability are allowable basic stress of bolt material, preload of bolt and internal press in descending order. (authors)
A method and application study on holistic decision tree for human reliability analysis in nuclear power plant

International Nuclear Information System (INIS)

Sun Feng; Zhong Shan; Wu Zhiyu

2008-01-01

The paper introduces a human reliability analysis method mainly used in Nuclear Power Plant Safety Assessment and the Holistic Decision Tree (HDT) method and how to apply it. The focus is primarily on providing the basic framework and some background of HDT method and steps to perform it. Influence factors and quality descriptors are formed by the interview with operators in Qinshan Nuclear Power Plant and HDT analysis performed for SGTR and SLOCA based on this information. The HDT model can use a graphic tree structure to indicate that error rate is a function of influence factors. HDT method is capable of dealing with the uncertainty in HRA, and it is reliable and practical. (authors)
User's manual of a support system for human reliability analysis

International Nuclear Information System (INIS)

Yokobayashi, Masao; Tamura, Kazuo.

1995-10-01

Many kinds of human reliability analysis (HRA) methods have been developed. However, users are required to be skillful so as to use them, and also required complicated works such as drawing event tree (ET) and calculation of uncertainty bounds. Moreover, each method is not so complete that only one method of them is not enough to evaluate human reliability. Therefore, a personal computer (PC) based support system for HRA has been developed to execute HRA practically and efficiently. The system consists of two methods, namely, simple method and detailed one. The former uses ASEP that is a simplified THERP-technique, and combined method of OAT and HRA-ET/DeBDA is used for the latter. Users can select a suitable method for their purpose. Human error probability (HEP) data were collected and a database of them was built to use for the support system. This paper describes outline of the HRA methods, support functions and user's guide of the system. (author)
Choosing a heuristic and root node for edge ordering in BDD-based network reliability analysis

International Nuclear Information System (INIS)

Mo, Yuchang; Xing, Liudong; Zhong, Farong; Pan, Zhusheng; Chen, Zhongyu

2014-01-01

In the Binary Decision Diagram (BDD)-based network reliability analysis, heuristics have been widely used to obtain a reasonably good ordering of edge variables. Orderings generated using different heuristics can lead to dramatically different sizes of BDDs, and thus dramatically different running times and memory usages for the analysis of the same network. Unfortunately, due to the nature of the ordering problem (i.e., being an NP-complete problem) no formal guidelines or rules are available for choosing a good heuristic or for choosing a high-performance root node to perform edge searching using a particular heuristic. In this work, we make novel contributions by proposing heuristic and root node selection methods based on the concept of boundary sets for the BDD-based network reliability analysis. Empirical studies show that the proposed selection methods can help to generate high-performance edge ordering for most of studied cases, enabling the efficient BDD-based reliability analysis of large-scale networks. The proposed methods are demonstrated on different types of networks, including square lattice networks, torus lattice networks and de Bruijn networks
Test-retest reliability of computer-based video analysis of general movements in healthy term-born infants.

Science.gov (United States)

Valle, Susanne Collier; Støen, Ragnhild; Sæther, Rannei; Jensenius, Alexander Refsum; Adde, Lars

2015-10-01

A computer-based video analysis has recently been presented for quantitative assessment of general movements (GMs). This method's test-retest reliability, however, has not yet been evaluated. The aim of the current study was to evaluate the test-retest reliability of computer-based video analysis of GMs, and to explore the association between computer-based video analysis and the temporal organization of fidgety movements (FMs). Test-retest reliability study. 75 healthy, term-born infants were recorded twice the same day during the FMs period using a standardized video set-up. The computer-based movement variables "quantity of motion mean" (Qmean), "quantity of motion standard deviation" (QSD) and "centroid of motion standard deviation" (CSD) were analyzed, reflecting the amount of motion and the variability of the spatial center of motion of the infant, respectively. In addition, the association between the variable CSD and the temporal organization of FMs was explored. Intraclass correlation coefficients (ICC 1.1 and ICC 3.1) were calculated to assess test-retest reliability. The ICC values for the variables CSD, Qmean and QSD were 0.80, 0.80 and 0.86 for ICC (1.1), respectively; and 0.80, 0.86 and 0.90 for ICC (3.1), respectively. There were significantly lower CSD values in the recordings with continual FMs compared to the recordings with intermittent FMs (ptest-retest reliability of computer-based video analysis of GMs, and a significant association between our computer-based video analysis and the temporal organization of FMs. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Aeroelastic/Aeroservoelastic Uncertainty and Reliability of Advanced Aerospace Vehicles in Flight and Ground Operations, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — ASSURE - Aeroelastic / Aeroservoelastic (AE/ASE) Uncertainty and Reliability Engineering capability - is a set of probabilistic computer programs for isolating...

Uncertainty analysis for secondary energy distributions

International Nuclear Information System (INIS)

Gerstl, S.A.W.

1978-01-01

In many transport calculations the integral design parameter of interest (response) is determined mainly by secondary particles such as gamma rays from (n,γ) reactions or secondary neutrons from inelastic scattering events or (n,2n) reactions. Standard sensitivity analysis usually allows to calculate the sensitivities to the production cross sections of such secondaries, but an extended formalism is needed to also obtain the sensitivities to the energy distribution of the generated secondary particles. For a 30-group standard cross-section set 84% of all non-zero table positions pertain to the description of secondary energy distributions (SED's) and only 16% to the actual reaction cross sections. Therefore, any sensitivity/uncertainty analysis which does not consider the effects of SED's is incomplete and neglects most of the input data. This paper describes the methods of how sensitivity profiles for SED's are obtained and used to estimate the uncertainty of an integral response due to uncertainties in these SED's. The detailed theory is documented elsewhere and implemented in the LASL sensitivity code SENSIT. SED sensitivity profiles have proven particularly valuable in cross-section uncertainty analyses for fusion reactors. Even when the production cross sections for secondary neutrons were assumed to be without error, the uncertainties in the energy distribution of these secondaries produced appreciable uncertainties in the calculated tritium breeding rate. However, complete error files for SED's are presently nonexistent. Therefore, methods will be described that allow rough error estimates due to estimated SED uncertainties based on integral SED sensitivities
HTGR reactor physics, thermal-hydraulics and depletion uncertainty analysis: a proposed IAEA coordinated research project

International Nuclear Information System (INIS)

Tyobeka, Bismark; Reitsma, Frederik; Ivanov, Kostadin

2011-01-01

The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of HTGR design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The predictive capability of coupled neutronics/thermal hydraulics and depletion simulations for reactor design and safety analysis can be assessed with sensitivity analysis and uncertainty analysis methods. In order to benefit from recent advances in modeling and simulation and the availability of new covariance data (nuclear data uncertainties) extensive sensitivity and uncertainty studies are needed for quantification of the impact of different sources of uncertainties on the design and safety parameters of HTGRs. Uncertainty and sensitivity studies are an essential component of any significant effort in data and simulation improvement. In February 2009, the Technical Working Group on Gas-Cooled Reactors recommended that the proposed IAEA Coordinated Research Project (CRP) on the HTGR Uncertainty Analysis in Modeling be implemented. In the paper the current status and plan are presented. The CRP will also benefit from interactions with the currently ongoing OECD/NEA Light Water Reactor (LWR) UAM benchmark activity by taking into consideration the peculiarities of HTGR designs and simulation requirements. (author)
Photometric Uncertainties

Science.gov (United States)

Zou, Xiao-Duan; Li, Jian-Yang; Clark, Beth Ellen; Golish, Dathon

2018-01-01

The OSIRIS-REx spacecraft, launched in September, 2016, will study the asteroid Bennu and return a sample from its surface to Earth in 2023. Bennu is a near-Earth carbonaceous asteroid which will provide insight into the formation and evolution of the solar system. OSIRIS-REx will first approach Bennu in August 2018 and will study the asteroid for approximately two years before sampling. OSIRIS-REx will develop its photometric model (including Lommel-Seelinger, ROLO, McEwen, Minnaert and Akimov) of Bennu with OCAM and OVIRS during the Detailed Survey mission phase. The model developed during this phase will be used to photometrically correct the OCAM and OVIRS data.Here we present the analysis of the error for the photometric corrections. Based on our testing data sets, we find:1. The model uncertainties is only correct when we use the covariance matrix to calculate, because the parameters are highly correlated.2. No evidence of domination of any parameter in each model.3. And both model error and the data error contribute to the final correction error comparably.4. We tested the uncertainty module on fake and real data sets, and find that model performance depends on the data coverage and data quality. These tests gave us a better understanding of how different model behave in different case.5. L-S model is more reliable than others. Maybe because the simulated data are based on L-S model. However, the test on real data (SPDIF) does show slight advantage of L-S, too. ROLO is not reliable to use when calculating bond albedo. The uncertainty of McEwen model is big in most cases. Akimov performs unphysical on SOPIE 1 data.6. Better use L-S as our default choice, this conclusion is based mainly on our test on SOPIE data and IPDIF.
Uncertainty Analysis of Light Water Reactor Fuel Lattices

Directory of Open Access Journals (Sweden)

C. Arenas

2013-01-01

Full Text Available The study explored the calculation of uncertainty based on available cross-section covariance data and computational tool on fuel lattice levels, which included pin cell and the fuel assembly models. Uncertainty variations due to temperatures changes and different fuel compositions are the main focus of this analysis. Selected assemblies and unit pin cells were analyzed according to the OECD LWR UAM benchmark specifications. Criticality and uncertainty analysis were performed using TSUNAMI-2D sequence in SCALE 6.1. It was found that uncertainties increase with increasing temperature, while kinf decreases. This increase in the uncertainty is due to the increase in sensitivity of the largest contributing reaction of uncertainty, namely, the neutron capture reaction 238U(n, γ due to the Doppler broadening. In addition, three types (UOX, MOX, and UOX-Gd2O3 of fuel material compositions were analyzed. A remarkable increase in uncertainty in kinf was observed for the case of MOX fuel. The increase in uncertainty of kinf in MOX fuel was nearly twice the corresponding value in UOX fuel. The neutron-nuclide reaction of 238U, mainly inelastic scattering (n, n′, contributed the most to the uncertainties in the MOX fuel, shifting the neutron spectrum to higher energy compared to the UOX fuel.
Uncertainty characterization approaches for risk assessment of DBPs in drinking water: a review.

Science.gov (United States)

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2009-04-01

The management of risk from disinfection by-products (DBPs) in drinking water has become a critical issue over the last three decades. The areas of concern for risk management studies include (i) human health risk from DBPs, (ii) disinfection performance, (iii) technical feasibility (maintenance, management and operation) of treatment and disinfection approaches, and (iv) cost. Human health risk assessment is typically considered to be the most important phase of the risk-based decision-making or risk management studies. The factors associated with health risk assessment and other attributes are generally prone to considerable uncertainty. Probabilistic and non-probabilistic approaches have both been employed to characterize uncertainties associated with risk assessment. The probabilistic approaches include sampling-based methods (typically Monte Carlo simulation and stratified sampling) and asymptotic (approximate) reliability analysis (first- and second-order reliability methods). Non-probabilistic approaches include interval analysis, fuzzy set theory and possibility theory. However, it is generally accepted that no single method is suitable for the entire spectrum of problems encountered in uncertainty analyses for risk assessment. Each method has its own set of advantages and limitations. In this paper, the feasibility and limitations of different uncertainty analysis approaches are outlined for risk management studies of drinking water supply systems. The findings assist in the selection of suitable approaches for uncertainty analysis in risk management studies associated with DBPs and human health risk.
Condition-based fault tree analysis (CBFTA): A new method for improved fault tree analysis (FTA), reliability and safety calculations

International Nuclear Information System (INIS)

Shalev, Dan M.; Tiran, Joseph

2007-01-01

Condition-based maintenance methods have changed systems reliability in general and individual systems in particular. Yet, this change does not affect system reliability analysis. System fault tree analysis (FTA) is performed during the design phase. It uses components failure rates derived from available sources as handbooks, etc. Condition-based fault tree analysis (CBFTA) starts with the known FTA. Condition monitoring (CM) methods applied to systems (e.g. vibration analysis, oil analysis, electric current analysis, bearing CM, electric motor CM, and so forth) are used to determine updated failure rate values of sensitive components. The CBFTA method accepts updated failure rates and applies them to the FTA. The CBFTA recalculates periodically the top event (TE) failure rate (λ TE ) thus determining the probability of system failure and the probability of successful system operation-i.e. the system's reliability. FTA is a tool for enhancing system reliability during the design stages. But, it has disadvantages, mainly it does not relate to a specific system undergoing maintenance. CBFTA is tool for updating reliability values of a specific system and for calculating the residual life according to the system's monitored conditions. Using CBFTA, the original FTA is ameliorated to a practical tool for use during the system's field life phase, not just during system design phase. This paper describes the CBFTA method and its advantages are demonstrated by an example
Model-based human reliability analysis: prospects and requirements

International Nuclear Information System (INIS)

Mosleh, A.; Chang, Y.H.

2004-01-01

Major limitations of the conventional methods for human reliability analysis (HRA), particularly those developed for operator response analysis in probabilistic safety assessments (PSA) of nuclear power plants, are summarized as a motivation for the need and a basis for developing requirements for the next generation HRA methods. It is argued that a model-based approach that provides explicit cognitive causal links between operator behaviors and directly or indirectly measurable causal factors should be at the core of the advanced methods. An example of such causal model is briefly reviewed, where due to the model complexity and input requirements can only be currently implemented in a dynamic PSA environment. The computer simulation code developed for this purpose is also described briefly, together with current limitations in the models, data, and the computer implementation
An Intelligent Method for Structural Reliability Analysis Based on Response Surface

Institute of Scientific and Technical Information of China (English)

桂劲松; 刘红; 康海贵

2004-01-01

As water depth increases, the structural safety and reliability of a system become more and more important and challenging. Therefore, the structural reliability method must be applied in ocean engineering design such as offshore platform design. If the performance function is known in structural reliability analysis, the first-order second-moment method is often used. If the performance function could not be definitely expressed, the response surface method is always used because it has a very clear train of thought and simple programming. However, the traditional response surface method fits the response surface of quadratic polynomials where the problem of accuracy could not be solved, because the true limit state surface can be fitted well only in the area near the checking point. In this paper, an intelligent computing method based on the whole response surface is proposed, which can be used for the situation where the performance function could not be definitely expressed in structural reliability analysis. In this method, a response surface of the fuzzy neural network for the whole area should be constructed first, and then the structural reliability can be calculated by the genetic algorithm. In the proposed method, all the sample points for the training network come from the whole area, so the true limit state surface in the whole area can be fitted. Through calculational examples and comparative analysis, it can be known that the proposed method is much better than the traditional response surface method of quadratic polynomials, because, the amount of calculation of finite element analysis is largely reduced, the accuracy of calculation is improved,and the true limit state surface can be fitted very well in the whole area. So, the method proposed in this paper is suitable for engineering application.
Development of Property Models with Uncertainty Estimate for Process Design under Uncertainty

DEFF Research Database (Denmark)

Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens

more reliable predictions with a new and improved set of model parameters for GC (group contribution) based and CI (atom connectivity index) based models and to quantify the uncertainties in the estimated property values from a process design point-of-view. This includes: (i) parameter estimation using....... The comparison of model prediction uncertainties with reported range of measurement uncertainties is presented for the properties with related available data. The application of the developed methodology to quantify the effect of these uncertainties on the design of different unit operations (distillation column......, the developed methodology can be used to quantify the sensitivity of process design to uncertainties in property estimates; obtain rationally the risk/safety factors in process design; and identify additional experimentation needs in order to reduce most critical uncertainties....
Sparse grid-based polynomial chaos expansion for aerodynamics of an airfoil with uncertainties

Directory of Open Access Journals (Sweden)

Xiaojing WU

2018-05-01

Full Text Available The uncertainties can generate fluctuations with aerodynamic characteristics. Uncertainty Quantification (UQ is applied to compute its impact on the aerodynamic characteristics. In addition, the contribution of each uncertainty to aerodynamic characteristics should be computed by uncertainty sensitivity analysis. Non-Intrusive Polynomial Chaos (NIPC has been successfully applied to uncertainty quantification and uncertainty sensitivity analysis. However, the non-intrusive polynomial chaos method becomes inefficient as the number of random variables adopted to describe uncertainties increases. This deficiency becomes significant in stochastic aerodynamic analysis considering the geometric uncertainty because the description of geometric uncertainty generally needs many parameters. To solve the deficiency, a Sparse Grid-based Polynomial Chaos (SGPC expansion is used to do uncertainty quantification and sensitivity analysis for stochastic aerodynamic analysis considering geometric and operational uncertainties. It is proved that the method is more efficient than non-intrusive polynomial chaos and Monte Carlo Simulation (MSC method for the stochastic aerodynamic analysis. By uncertainty quantification, it can be learnt that the flow characteristics of shock wave and boundary layer separation are sensitive to the geometric uncertainty in transonic region. The uncertainty sensitivity analysis reveals the individual and coupled effects among the uncertainty parameters. Keywords: Non-intrusive polynomial chaos, Sparse grid, Stochastic aerodynamic analysis, Uncertainty sensitivity analysis, Uncertainty quantification
Eigenvalue sensitivity analysis and uncertainty quantification in SCALE6.2.1 using continuous-energy Monte Carlo Method

Energy Technology Data Exchange (ETDEWEB)

Labarile, A.; Barrachina, T.; Miró, R.; Verdú, G., E-mail: alabarile@iqn.upv.es, E-mail: tbarrachina@iqn.upv.es, E-mail: rmiro@iqn.upv.es, E-mail: gverdu@iqn.upv.es [Institute for Industrial, Radiophysical and Environmental Safety - ISIRYM, Valencia (Spain); Pereira, C., E-mail: claubia@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Departamento de Engenharia Nuclear

2017-07-01

The use of Best-Estimate computer codes is one of the greatest concerns in the nuclear industry especially for licensing analysis. Of paramount importance is the estimation of the uncertainties of the whole system to establish the safety margins based on highly reliable results. The estimation of these uncertainties should be performed by applying a methodology to propagate the uncertainties from the input parameters and the models implemented in the code to the output parameters. This study employs two different approaches for the Sensitivity Analysis (SA) and Uncertainty Quantification (UQ), the adjoint-based perturbation theory of TSUNAMI-3D, and the stochastic sampling technique of SAMPLER/KENO. The cases studied are two models of Light Water Reactors in the framework of the OECD/NEA UAM-LWR benchmark, a Boiling Water Reactor (BWR) and a Pressurized Water Reactor (PWR). Both of them at Hot Full Power (HFP) and Hot Zero Power (HZP) conditions, with and without control rod. This work presents the results of k{sub eff} from different simulation, and discuss the comparison of the two methods employed. In particular, a list of the major contributors to the uncertainty of k{sub eff} in terms of microscopic cross sections; their sensitivity coefficients; a comparison between the results of the two modules and with reference values; statistical information from the stochastic approach, and the probability and statistical confidence reached in the simulations. The reader will find all these information discussed in this paper. (author)
Parameter uncertainty effects on variance-based sensitivity analysis

International Nuclear Information System (INIS)

Yu, W.; Harris, T.J.

2009-01-01

In the past several years there has been considerable commercial and academic interest in methods for variance-based sensitivity analysis. The industrial focus is motivated by the importance of attributing variance contributions to input factors. A more complete understanding of these relationships enables companies to achieve goals related to quality, safety and asset utilization. In a number of applications, it is possible to distinguish between two types of input variables-regressive variables and model parameters. Regressive variables are those that can be influenced by process design or by a control strategy. With model parameters, there are typically no opportunities to directly influence their variability. In this paper, we propose a new method to perform sensitivity analysis through a partitioning of the input variables into these two groupings: regressive variables and model parameters. A sequential analysis is proposed, where first an sensitivity analysis is performed with respect to the regressive variables. In the second step, the uncertainty effects arising from the model parameters are included. This strategy can be quite useful in understanding process variability and in developing strategies to reduce overall variability. When this method is used for nonlinear models which are linear in the parameters, analytical solutions can be utilized. In the more general case of models that are nonlinear in both the regressive variables and the parameters, either first order approximations can be used, or numerically intensive methods must be used
Information Synthesis in Uncertainty Studies: Application to the Analysis of the BEMUSE Results

International Nuclear Information System (INIS)

Baccou, J.; Chojnacki, E.; Destercke, S.

2013-01-01

mathematical framework, the more time consuming the propagation should be. Therefore, the key point is here to construct a numerical treatment for uncertainty propagation which reduces the computational cost and can be applied to complex models used in practice. In nuclear safety studies, different uncertainty analyses using different codes and implying different experts are generally performed. Deriving benefits from these analyses appears to be a problem of information synthesis which is the third key issue. Indeed each uncertainty study can be viewed as an information source on quantities of interest. It is then useful to define formal methods to combine all these information sources in order to improve the reliability of the results and to detect possible conflicts (if any) between the sources. The efficiency of an uncertainty analysis requires a reliable quantification of the information associated to uncertainty sources. This quantification is addressed in the fourth key issue. It consists in exploiting the information related to available experiments and to the comparison code/experiment to infer the uncertainty attached to the code input parameters. Therefore, the crucial points stand in the choice of an experimental database sufficiently representative and exhaustive of the considered phenomenon and in the construction of an efficient treatment to perform this inference. The two first points have been deeply studied in the frame of the OECD BEMUSE Program. In particular, it came out that statistical approaches, based on Monte-Carlo techniques, are now sufficiently robust for the evaluation of uncertainty on a LB-LOCA transient. In this paper, we focus on the third issue and present some recent developments proposed by IRSN to derive formal tools in order to improve the reliability of an analysis involving different information sources. It is applied to exhibit some important conclusions from the two BEMUSE benchmarks. For sake of completeness, we recall that the last
Reliability-based condition assessment of steel containment and liners

International Nuclear Information System (INIS)

Ellingwood, B.; Bhattacharya, B.; Zheng, R.

1996-11-01

Steel containments and liners in nuclear power plants may be exposed to aggressive environments that may cause their strength and stiffness to decrease during the plant service life. Among the factors recognized as having the potential to cause structural deterioration are uniform, pitting or crevice corrosion; fatigue, including crack initiation and propagation to fracture; elevated temperature; and irradiation. The evaluation of steel containments and liners for continued service must provide assurance that they are able to withstand future extreme loads during the service period with a level of reliability that is sufficient for public safety. Rational methodologies to provide such assurances can be developed using modern structural reliability analysis principles that take uncertainties in loading, strength, and degradation resulting from environmental factors into account. The research described in this report is in support of the Steel Containments and Liners Program being conducted for the US Nuclear Regulatory Commission by the Oak Ridge National Laboratory. The research demonstrates the feasibility of using reliability analysis as a tool for performing condition assessments and service life predictions of steel containments and liners. Mathematical models that describe time-dependent changes in steel due to aggressive environmental factors are identified, and statistical data supporting the use of these models in time-dependent reliability analysis are summarized. The analysis of steel containment fragility is described, and simple illustrations of the impact on reliability of structural degradation are provided. The role of nondestructive evaluation in time-dependent reliability analysis, both in terms of defect detection and sizing, is examined. A Markov model provides a tool for accounting for time-dependent changes in damage condition of a structural component or system. 151 refs
Uncertainty Assessments in Fast Neutron Activation Analysis

International Nuclear Information System (INIS)

W. D. James; R. Zeisler

2000-01-01

Fast neutron activation analysis (FNAA) carried out with the use of small accelerator-based neutron generators is routinely used for major/minor element determinations in industry, mineral and petroleum exploration, and to some extent in research. While the method shares many of the operational procedures and therefore errors inherent to conventional thermal neutron activation analysis, its unique implementation gives rise to additional specific concerns that can result in errors or increased uncertainties of measured quantities. The authors were involved in a recent effort to evaluate irreversible incorporation of oxygen into a standard reference material (SRM) by direct measurement of oxygen by FNAA. That project required determination of oxygen in bottles of the SRM stored in varying environmental conditions and a comparison of the results. We recognized the need to accurately describe the total uncertainty of the measurements to accurately characterize any differences in the resulting average concentrations. It is our intent here to discuss the breadth of potential parameters that have the potential to contribute to the random and nonrandom errors of the method and provide estimates of the magnitude of uncertainty introduced. In addition, we will discuss the steps taken in this recent FNAA project to control quality, assess the uncertainty of the measurements, and evaluate results based on the statistical reproducibility
Reliability analysis of 2400 MWth gas-cooled fast reactor natural circulation decay heat removal system

International Nuclear Information System (INIS)

Marques, M.; Bassi, C.; Bentivoglio, F.

2012-01-01

In support to a PSA (Probability Safety Assessment) performed at the design level on the 2400 MWth Gas-cooled Fast Reactor, the functional reliability of the decay heat removal system (DHR) working in natural circulation has been estimated in two transient situations corresponding to an 'aggravated' Loss of Flow Accident (LOFA) and a Loss of Coolant Accident (LOCA). The reliability analysis was based on the RMPS methodology. Reliability and global sensitivity analyses use uncertainty propagation by Monte Carlo techniques. The DHR system consists of 1) 3 dedicated DHR loops: the choice of 3 loops (3*100% redundancy) is made in assuming that one could be lost due to the accident initiating event (break for example) and that another one must be supposed unavailable (single failure criterion); 2) a metallic guard containment enclosing the primary system (referred as close containment), not pressurized in normal operation, having a free volume such as the fast primary helium expansion gives an equilibrium pressure of 1.0 MPa, in the first part of the transient (few hours). Each dedicated DHR loop designed to work in forced circulation with blowers or in natural circulation, is composed of 1) a primary loop (cross-duct connected to the core vessel), with a driving height of 10 meters between core and DHX mid-plan; 2) a secondary circuit filled with pressurized water at 1.0 MPa (driving height of 5 meters for natural circulation DHR); 3) a ternary pool, initially at 50 C. degrees, whose volume is determined to handle one day heat extraction (after this time delay, additional measures are foreseen to fill up the pool). The results obtained on the reliability of the DHR system and on the most important input parameters are very different from one scenario to the other showing the necessity for the PSA to perform specific reliability analysis of the passive system for each considered scenario. The analysis shows that the DHR system working in natural circulation is
Risk Analysis of Reservoir Flood Routing Calculation Based on Inflow Forecast Uncertainty

Directory of Open Access Journals (Sweden)

Binquan Li

2016-10-01

Full Text Available Possible risks in reservoir flood control and regulation cannot be objectively assessed by deterministic flood forecasts, resulting in the probability of reservoir failure. We demonstrated a risk analysis of reservoir flood routing calculation accounting for inflow forecast uncertainty in a sub-basin of Huaihe River, China. The Xinanjiang model was used to provide deterministic flood forecasts, and was combined with the Hydrologic Uncertainty Processor (HUP to quantify reservoir inflow uncertainty in the probability density function (PDF form. Furthermore, the PDFs of reservoir water level (RWL and the risk rate of RWL exceeding a defined safety control level could be obtained. Results suggested that the median forecast (50th percentiles of HUP showed better agreement with observed inflows than the Xinanjiang model did in terms of the performance measures of flood process, peak, and volume. In addition, most observations (77.2% were bracketed by the uncertainty band of 90% confidence interval, with some small exceptions of high flows. Results proved that this framework of risk analysis could provide not only the deterministic forecasts of inflow and RWL, but also the fundamental uncertainty information (e.g., 90% confidence band for the reservoir flood routing calculation.
Knowledge-base for the new human reliability analysis method, A Technique for Human Error Analysis (ATHEANA)

International Nuclear Information System (INIS)

Cooper, S.E.; Wreathall, J.; Thompson, C.M., Drouin, M.; Bley, D.C.

1996-01-01

This paper describes the knowledge base for the application of the new human reliability analysis (HRA) method, a ''A Technique for Human Error Analysis'' (ATHEANA). Since application of ATHEANA requires the identification of previously unmodeled human failure events, especially errors of commission, and associated error-forcing contexts (i.e., combinations of plant conditions and performance shaping factors), this knowledge base is an essential aid for the HRA analyst
A reliability analysis tool for SpaceWire network

Science.gov (United States)

Zhou, Qiang; Zhu, Longjiang; Fei, Haidong; Wang, Xingyou

2017-04-01

A SpaceWire is a standard for on-board satellite networks as the basis for future data-handling architectures. It is becoming more and more popular in space applications due to its technical advantages, including reliability, low power and fault protection, etc. High reliability is the vital issue for spacecraft. Therefore, it is very important to analyze and improve the reliability performance of the SpaceWire network. This paper deals with the problem of reliability modeling and analysis with SpaceWire network. According to the function division of distributed network, a reliability analysis method based on a task is proposed, the reliability analysis of every task can lead to the system reliability matrix, the reliability result of the network system can be deduced by integrating these entire reliability indexes in the matrix. With the method, we develop a reliability analysis tool for SpaceWire Network based on VC, where the computation schemes for reliability matrix and the multi-path-task reliability are also implemented. By using this tool, we analyze several cases on typical architectures. And the analytic results indicate that redundancy architecture has better reliability performance than basic one. In practical, the dual redundancy scheme has been adopted for some key unit, to improve the reliability index of the system or task. Finally, this reliability analysis tool will has a directive influence on both task division and topology selection in the phase of SpaceWire network system design.
Network reliability analysis of complex systems using a non-simulation-based method

International Nuclear Information System (INIS)

Kim, Youngsuk; Kang, Won-Hee

2013-01-01

Civil infrastructures such as transportation, water supply, sewers, telecommunications, and electrical and gas networks often establish highly complex networks, due to their multiple source and distribution nodes, complex topology, and functional interdependence between network components. To understand the reliability of such complex network system under catastrophic events such as earthquakes and to provide proper emergency management actions under such situation, efficient and accurate reliability analysis methods are necessary. In this paper, a non-simulation-based network reliability analysis method is developed based on the Recursive Decomposition Algorithm (RDA) for risk assessment of generic networks whose operation is defined by the connections of multiple initial and terminal node pairs. The proposed method has two separate decomposition processes for two logical functions, intersection and union, and combinations of these processes are used for the decomposition of any general system event with multiple node pairs. The proposed method is illustrated through numerical network examples with a variety of system definitions, and is applied to a benchmark gas transmission pipe network in Memphis TN to estimate the seismic performance and functional degradation of the network under a set of earthquake scenarios.

GRAPH THEORY APPROACH TO QUANTIFY UNCERTAINTY OF PERFORMANCE MEASURES

Directory of Open Access Journals (Sweden)

Sérgio D. Sousa

2015-03-01

Full Text Available In this work, the performance measurement process is studied to quantify the uncertainty induced in the resulting performance measure (PM. To that end, the causes of uncertainty are identified, analysing the activities undertaken in the three following stages of the performance measurement process: design and implementation, data collection and record, and determination and analysis. A quantitative methodology based on graph theory and on the sources of uncertainty of the performance measurement process is used to calculate an uncertainty index to evaluate the level of uncertainty of a given PM or (key performance indicator. An application example is presented. The quantification of PM uncertainty could contribute to better represent the risk associated with a given decision and also to improve the PM to increase its precision and reliability.
Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling

Science.gov (United States)

Sutton, Jonathan E.; Guo, Wei; Katsoulakis, Markos A.; Vlachos, Dionisios G.

2016-04-01

Kinetic models based on first principles are becoming common place in heterogeneous catalysis because of their ability to interpret experimental data, identify the rate-controlling step, guide experiments and predict novel materials. To overcome the tremendous computational cost of estimating parameters of complex networks on metal catalysts, approximate quantum mechanical calculations are employed that render models potentially inaccurate. Here, by introducing correlative global sensitivity analysis and uncertainty quantification, we show that neglecting correlations in the energies of species and reactions can lead to an incorrect identification of influential parameters and key reaction intermediates and reactions. We rationalize why models often underpredict reaction rates and show that, despite the uncertainty being large, the method can, in conjunction with experimental data, identify influential missing reaction pathways and provide insights into the catalyst active site and the kinetic reliability of a model. The method is demonstrated in ethanol steam reforming for hydrogen production for fuel cells.
Projecting the potential evapotranspiration by coupling different formulations and input data reliabilities: The possible uncertainty source for climate change impacts on hydrological regime

Science.gov (United States)

Wang, Weiguang; Li, Changni; Xing, Wanqiu; Fu, Jianyu

2017-12-01

Representing atmospheric evaporating capability for a hypothetical reference surface, potential evapotranspiration (PET) determines the upper limit of actual evapotranspiration and is an important input to hydrological models. Due that present climate models do not give direct estimates of PET when simulating the hydrological response to future climate change, the PET must be estimated first and is subject to the uncertainty on account of many existing formulae and different input data reliabilities. Using four different PET estimation approaches, i.e., the more physically Penman (PN) equation with less reliable input variables, more empirical radiation-based Priestley-Taylor (PT) equation with relatively dependable downscaled data, the most simply temperature-based Hamon (HM) equation with the most reliable downscaled variable, and downscaling PET directly by the statistical downscaling model, this paper investigated the differences of runoff projection caused by the alternative PET methods by a well calibrated abcd monthly hydrological model. Three catchments, i.e., the Luanhe River Basin, the Source Region of the Yellow River and the Ganjiang River Basin, representing a large climatic diversity were chosen as examples to illustrate this issue. The results indicated that although similar monthly patterns of PET over the period 2021-2050 for each catchment were provided by the four methods, the magnitudes of PET were still slightly different, especially for spring and summer months in the Luanhe River Basin and the Source Region of the Yellow River with relatively dry climate feature. The apparent discrepancy in magnitude of change in future runoff and even the diverse change direction for summer months in the Luanhe River Basin and spring months in the Source Region of the Yellow River indicated that the PET method related uncertainty occurred, especially in the Luanhe River Basin and the Source Region of the Yellow River with smaller aridity index. Moreover, the
Deterministic sensitivity and uncertainty analysis for large-scale computer models

International Nuclear Information System (INIS)

Worley, B.A.; Pin, F.G.; Oblow, E.M.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.

1988-01-01

This paper presents a comprehensive approach to sensitivity and uncertainty analysis of large-scale computer models that is analytic (deterministic) in principle and that is firmly based on the model equations. The theory and application of two systems based upon computer calculus, GRESS and ADGEN, are discussed relative to their role in calculating model derivatives and sensitivities without a prohibitive initial manpower investment. Storage and computational requirements for these two systems are compared for a gradient-enhanced version of the PRESTO-II computer model. A Deterministic Uncertainty Analysis (DUA) method that retains the characteristics of analytically computing result uncertainties based upon parameter probability distributions is then introduced and results from recent studies are shown. 29 refs., 4 figs., 1 tab
Cellular scanning strategy for selective laser melting: Generating reliable, optimized scanning paths and processing parameters

DEFF Research Database (Denmark)

Mohanty, Sankhya; Hattel, Jesper Henri

2015-01-01

method based uncertainty and reliability analysis. The reliability of the scanning paths are established using cumulative probability distribution functions for process output criteria such as sample density, thermal homogeneity, etc. A customized genetic algorithm is used along with the simulation model...
Structural reliability analysis applied to pipeline risk analysis

Energy Technology Data Exchange (ETDEWEB)

Gardiner, M. [GL Industrial Services, Loughborough (United Kingdom); Mendes, Renato F.; Donato, Guilherme V.P. [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil)

2009-07-01

Quantitative Risk Assessment (QRA) of pipelines requires two main components to be provided. These are models of the consequences that follow from some loss of containment incident, and models for the likelihood of such incidents occurring. This paper describes how PETROBRAS have used Structural Reliability Analysis for the second of these, to provide pipeline- and location-specific predictions of failure frequency for a number of pipeline assets. This paper presents an approach to estimating failure rates for liquid and gas pipelines, using Structural Reliability Analysis (SRA) to analyze the credible basic mechanisms of failure such as corrosion and mechanical damage. SRA is a probabilistic limit state method: for a given failure mechanism it quantifies the uncertainty in parameters to mathematical models of the load-resistance state of a structure and then evaluates the probability of load exceeding resistance. SRA can be used to benefit the pipeline risk management process by optimizing in-line inspection schedules, and as part of the design process for new construction in pipeline rights of way that already contain multiple lines. A case study is presented to show how the SRA approach has recently been used on PETROBRAS pipelines and the benefits obtained from it. (author)
Analysis of information security reliability: A tutorial

International Nuclear Information System (INIS)

Kondakci, Suleyman

2015-01-01

This article presents a concise reliability analysis of network security abstracted from stochastic modeling, reliability, and queuing theories. Network security analysis is composed of threats, their impacts, and recovery of the failed systems. A unique framework with a collection of the key reliability models is presented here to guide the determination of the system reliability based on the strength of malicious acts and performance of the recovery processes. A unique model, called Attack-obstacle model, is also proposed here for analyzing systems with immunity growth features. Most computer science curricula do not contain courses in reliability modeling applicable to different areas of computer engineering. Hence, the topic of reliability analysis is often too diffuse to most computer engineers and researchers dealing with network security. This work is thus aimed at shedding some light on this issue, which can be useful in identifying models, their assumptions and practical parameters for estimating the reliability of threatened systems and for assessing the performance of recovery facilities. It can also be useful for the classification of processes and states regarding the reliability of information systems. Systems with stochastic behaviors undergoing queue operations and random state transitions can also benefit from the approaches presented here. - Highlights: • A concise survey and tutorial in model-based reliability analysis applicable to information security. • A framework of key modeling approaches for assessing reliability of networked systems. • The framework facilitates quantitative risk assessment tasks guided by stochastic modeling and queuing theory. • Evaluation of approaches and models for modeling threats, failures, impacts, and recovery analysis of information systems
A rigorous methodology for development and uncertainty analysis of group contribution based property models

DEFF Research Database (Denmark)

Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan

) weighted-least-square regression. 3) Initialization of estimation by use of linear algebra providing a first guess. 4) Sequential parameter and simultaneous GC parameter by using of 4 different minimization algorithms. 5) Thorough uncertainty analysis: a) based on asymptotic approximation of parameter...... covariance matrix b) based on boot strap method. Providing 95%-confidence intervals of parameters and predicted property. 6) Performance statistics analysis and model application. The application of the methodology is shown for a new GC model built to predict lower flammability limit (LFL) for refrigerants...... their credibility and robustness in wider industrial and scientific applications....
Power system reliability analysis using fault trees

International Nuclear Information System (INIS)

Volkanovski, A.; Cepin, M.; Mavko, B.

2006-01-01

The power system reliability analysis method is developed from the aspect of reliable delivery of electrical energy to customers. The method is developed based on the fault tree analysis, which is widely applied in the Probabilistic Safety Assessment (PSA). The method is adapted for the power system reliability analysis. The method is developed in a way that only the basic reliability parameters of the analysed power system are necessary as an input for the calculation of reliability indices of the system. The modeling and analysis was performed on an example power system consisting of eight substations. The results include the level of reliability of current power system configuration, the combinations of component failures resulting in a failed power delivery to loads, and the importance factors for components and subsystems. (author)
Quantified Risk Ranking Model for Condition-Based Risk and Reliability Centered Maintenance

Science.gov (United States)

Chattopadhyaya, Pradip Kumar; Basu, Sushil Kumar; Majumdar, Manik Chandra

2017-06-01

In the recent past, risk and reliability centered maintenance (RRCM) framework is introduced with a shift in the methodological focus from reliability and probabilities (expected values) to reliability, uncertainty and risk. In this paper authors explain a novel methodology for risk quantification and ranking the critical items for prioritizing the maintenance actions on the basis of condition-based risk and reliability centered maintenance (CBRRCM). The critical items are identified through criticality analysis of RPN values of items of a system and the maintenance significant precipitating factors (MSPF) of items are evaluated. The criticality of risk is assessed using three risk coefficients. The likelihood risk coefficient treats the probability as a fuzzy number. The abstract risk coefficient deduces risk influenced by uncertainty, sensitivity besides other factors. The third risk coefficient is called hazardous risk coefficient, which is due to anticipated hazards which may occur in the future and the risk is deduced from criteria of consequences on safety, environment, maintenance and economic risks with corresponding cost for consequences. The characteristic values of all the three risk coefficients are obtained with a particular test. With few more tests on the system, the values may change significantly within controlling range of each coefficient, hence `random number simulation' is resorted to obtain one distinctive value for each coefficient. The risk coefficients are statistically added to obtain final risk coefficient of each critical item and then the final rankings of critical items are estimated. The prioritization in ranking of critical items using the developed mathematical model for risk assessment shall be useful in optimization of financial losses and timing of maintenance actions.
Sensitivity Analysis of Uncertainty Parameter based on MARS-LMR Code on SHRT-45R of EBR II

Energy Technology Data Exchange (ETDEWEB)

Kang, Seok-Ju; Kang, Doo-Hyuk; Seo, Jae-Seung [System Engineering and Technology Co., Daejeon (Korea, Republic of); Bae, Sung-Won [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Jeong, Hae-Yong [Sejong University, Seoul (Korea, Republic of)

2016-10-15

In order to assess the uncertainty quantification of the MARS-LMR code, the code has been improved by modifying the source code to accommodate calculation process required for uncertainty quantification. In the present study, a transient of Unprotected Loss of Flow(ULOF) is selected as typical cases of as Anticipated Transient without Scram(ATWS) which belongs to DEC category. The MARS-LMR input generation for EBR II SHRT-45R and execution works are performed by using the PAPIRUS program. The sensitivity analysis is carried out with Uncertainty Parameter of the MARS-LMR code for EBR-II SHRT-45R. Based on the results of sensitivity analysis, dominant parameters with large sensitivity to FoM are picked out. Dominant parameters selected are closely related to the development process of ULOF event.
Inclusion of time uncertainty in calibration of ionizing radiations

International Nuclear Information System (INIS)

Jordao, B.O.; Quaresma, D.S.; Carvalho, R.J.; Peixoto, J.G.P.

2014-01-01

In terms of metrology, two key factors for reliability employed in the calibration process are what we call Traceability and Uncertainty. Traceability will provide confidence in measurements. Already uncertainty will provide security and quality of what this being measured. Based on the above, this article suggests the implementation time of uncertainty in the calibration of radiological instruments thus increasing the reliability and traceability of the system. (author)
Uncertainty analysis of environmental models

International Nuclear Information System (INIS)

Monte, L.

1990-01-01

In the present paper an evaluation of the output uncertainty of an environmental model for assessing the transfer of 137 Cs and 131 I in the human food chain are carried out on the basis of a statistical analysis of data reported by the literature. The uncertainty analysis offers the oppotunity of obtaining some remarkable information about the uncertainty of models predicting the migration of non radioactive substances in the environment mainly in relation to the dry and wet deposition
Development of the GO-FLOW reliability analysis methodology for nuclear reactor system

International Nuclear Information System (INIS)

Matsuoka, Takeshi; Kobayashi, Michiyuki

1994-01-01

Probabilistic Safety Assessment (PSA) is important in the safety analysis of technological systems and processes, such as, nuclear plants, chemical and petroleum facilities, aerospace systems. Event trees and fault trees are the basic analytical tools that have been most frequently used for PSAs. Several system analysis methods can be used in addition to, or in support of, the event- and fault-tree analysis. The need for more advanced methods of system reliability analysis has grown with the increased complexity of engineered systems. The Ship Research Institute has been developing a new reliability analysis methodology, GO-FLOW, which is a success-oriented system analysis technique, and is capable of evaluating a large system with complex operational sequences. The research has been supported by the special research fund for Nuclear Technology, Science and Technology Agency, from 1989 to 1994. This paper describes the concept of the Probabilistic Safety Assessment (PSA), an overview of various system analysis techniques, an overview of the GO-FLOW methodology, the GO-FLOW analysis support system, procedure of treating a phased mission problem, a function of common cause failure analysis, a function of uncertainty analysis, a function of common cause failure analysis with uncertainty, and printing out system of the results of GO-FLOW analysis in the form of figure or table. Above functions are explained by analyzing sample systems, such as PWR AFWS, BWR ECCS. In the appendices, the structure of the GO-FLOW analysis programs and the meaning of the main variables defined in the GO-FLOW programs are described. The GO-FLOW methodology is a valuable and useful tool for system reliability analysis, and has a wide range of applications. With the development of the total system of the GO-FLOW, this methodology has became a powerful tool in a living PSA. (author) 54 refs
Assessing Reliability of Cellulose Hydrolysis Models to Support Biofuel Process Design – Identifiability and Uncertainty Analysis

DEFF Research Database (Denmark)

Sin, Gürkan; Meyer, Anne S.; Gernaey, Krist

2010-01-01

The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done in the ori......The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done...
User`s manual of a support system for human reliability analysis

Energy Technology Data Exchange (ETDEWEB)

Yokobayashi, Masao [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment; Tamura, Kazuo

1995-10-01

Many kinds of human reliability analysis (HRA) methods have been developed. However, users are required to be skillful so as to use them, and also required complicated works such as drawing event tree (ET) and calculation of uncertainty bounds. Moreover, each method is not so complete that only one method of them is not enough to evaluate human reliability. Therefore, a personal computer (PC) based support system for HRA has been developed to execute HRA practically and efficiently. The system consists of two methods, namely, simple method and detailed one. The former uses ASEP that is a simplified THERP-technique, and combined method of OAT and HRA-ET/DeBDA is used for the latter. Users can select a suitable method for their purpose. Human error probability (HEP) data were collected and a database of them was built to use for the support system. This paper describes outline of the HRA methods, support functions and user`s guide of the system. (author).
Improved Monte Carlo Method for PSA Uncertainty Analysis

International Nuclear Information System (INIS)

Choi, Jongsoo

2016-01-01

The treatment of uncertainty is an important issue for regulatory decisions. Uncertainties exist from knowledge limitations. A probabilistic approach has exposed some of these limitations and provided a framework to assess their significance and assist in developing a strategy to accommodate them in the regulatory process. The uncertainty analysis (UA) is usually based on the Monte Carlo method. This paper proposes a Monte Carlo UA approach to calculate the mean risk metrics accounting for the SOKC between basic events (including CCFs) using efficient random number generators and to meet Capability Category III of the ASME/ANS PRA standard. Audit calculation is needed in PSA regulatory reviews of uncertainty analysis results submitted for licensing. The proposed Monte Carlo UA approach provides a high degree of confidence in PSA reviews. All PSA needs accounting for the SOKC between event probabilities to meet the ASME/ANS PRA standard
Improved Monte Carlo Method for PSA Uncertainty Analysis

Energy Technology Data Exchange (ETDEWEB)

Choi, Jongsoo [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

2016-10-15

The treatment of uncertainty is an important issue for regulatory decisions. Uncertainties exist from knowledge limitations. A probabilistic approach has exposed some of these limitations and provided a framework to assess their significance and assist in developing a strategy to accommodate them in the regulatory process. The uncertainty analysis (UA) is usually based on the Monte Carlo method. This paper proposes a Monte Carlo UA approach to calculate the mean risk metrics accounting for the SOKC between basic events (including CCFs) using efficient random number generators and to meet Capability Category III of the ASME/ANS PRA standard. Audit calculation is needed in PSA regulatory reviews of uncertainty analysis results submitted for licensing. The proposed Monte Carlo UA approach provides a high degree of confidence in PSA reviews. All PSA needs accounting for the SOKC between event probabilities to meet the ASME/ANS PRA standard.
Fuzzy Uncertainty Evaluation for Fault Tree Analysis

Energy Technology Data Exchange (ETDEWEB)

Kim, Ki Beom; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of); Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of)

2015-05-15

This traditional probabilistic approach can calculate relatively accurate results. However it requires a long time because of repetitive computation due to the MC method. In addition, when informative data for statistical analysis are not sufficient or some events are mainly caused by human error, the probabilistic approach may not be possible because uncertainties of these events are difficult to be expressed by probabilistic distributions. In order to reduce the computation time and quantify uncertainties of top events when basic events whose uncertainties are difficult to be expressed by probabilistic distributions exist, the fuzzy uncertainty propagation based on fuzzy set theory can be applied. In this paper, we develop a fuzzy uncertainty propagation code and apply the fault tree of the core damage accident after the large loss of coolant accident (LLOCA). The fuzzy uncertainty propagation code is implemented and tested for the fault tree of the radiation release accident. We apply this code to the fault tree of the core damage accident after the LLOCA in three cases and compare the results with those computed by the probabilistic uncertainty propagation using the MC method. The results obtained by the fuzzy uncertainty propagation can be calculated in relatively short time, covering the results obtained by the probabilistic uncertainty propagation.
Improving the reliability of POD curves in NDI methods using a Bayesian inversion approach for uncertainty quantification

Science.gov (United States)

Ben Abdessalem, A.; Jenson, F.; Calmon, P.

2016-02-01

This contribution provides an example of the possible advantages of adopting a Bayesian inversion approach to uncertainty quantification in nondestructive inspection methods. In such problem, the uncertainty associated to the random parameters is not always known and needs to be characterised from scattering signal measurements. The uncertainties may then correctly propagated in order to determine a reliable probability of detection curve. To this end, we establish a general Bayesian framework based on a non-parametric maximum likelihood function formulation and some priors from expert knowledge. However, the presented inverse problem is time-consuming and computationally intensive. To cope with this difficulty, we replace the real model by a surrogate one in order to speed-up the model evaluation and to make the problem to be computationally feasible for implementation. The least squares support vector regression is adopted as metamodelling technique due to its robustness to deal with non-linear problems. We illustrate the usefulness of this methodology through the control of tube with enclosed defect using ultrasonic inspection method.

Statistical analysis tolerance using jacobian torsor model based on uncertainty propagation method

Directory of Open Access Journals (Sweden)

W Ghie

2016-04-01

Full Text Available One risk inherent in the use of assembly components is that the behaviourof these components is discovered only at the moment an assembly isbeing carried out. The objective of our work is to enable designers to useknown component tolerances as parameters in models that can be usedto predict properties at the assembly level. In this paper we present astatistical approach to assemblability evaluation, based on tolerance andclearance propagations. This new statistical analysis method for toleranceis based on the Jacobian-Torsor model and the uncertainty measurementapproach. We show how this can be accomplished by modeling thedistribution of manufactured dimensions through applying a probabilitydensity function. By presenting an example we show how statisticaltolerance analysis should be used in the Jacobian-Torsor model. This workis supported by previous efforts aimed at developing a new generation ofcomputational tools for tolerance analysis and synthesis, using theJacobian-Torsor approach. This approach is illustrated on a simple threepartassembly, demonstrating the method’s capability in handling threedimensionalgeometry.
CEC/USDOE workshop on uncertainty analysis

International Nuclear Information System (INIS)

Elderkin, C.E.; Kelly, G.N.

1990-07-01

Any measured or assessed quantity contains uncertainty. The quantitative estimation of such uncertainty is becoming increasingly important, especially in assuring that safety requirements are met in design, regulation, and operation of nuclear installations. The CEC/USDOE Workshop on Uncertainty Analysis, held in Santa Fe, New Mexico, on November 13 through 16, 1989, was organized jointly by the Commission of European Communities (CEC's) Radiation Protection Research program, dealing with uncertainties throughout the field of consequence assessment, and DOE's Atmospheric Studies in Complex Terrain (ASCOT) program, concerned with the particular uncertainties in time and space variant transport and dispersion. The workshop brought together US and European scientists who have been developing or applying uncertainty analysis methodologies, conducted in a variety of contexts, often with incomplete knowledge of the work of others in this area. Thus, it was timely to exchange views and experience, identify limitations of approaches to uncertainty and possible improvements, and enhance the interface between developers and users of uncertainty analysis methods. Furthermore, the workshop considered the extent to which consistent, rigorous methods could be used in various applications within consequence assessment. 3 refs
Statistical uncertainties and unrecognized relationships

International Nuclear Information System (INIS)

Rankin, J.P.

1985-01-01

Hidden relationships in specific designs directly contribute to inaccuracies in reliability assessments. Uncertainty factors at the system level may sometimes be applied in attempts to compensate for the impact of such unrecognized relationships. Often uncertainty bands are used to relegate unknowns to a miscellaneous category of low-probability occurrences. However, experience and modern analytical methods indicate that perhaps the dominant, most probable and significant events are sometimes overlooked in statistical reliability assurances. The author discusses the utility of two unique methods of identifying the otherwise often unforeseeable system interdependencies for statistical evaluations. These methods are sneak circuit analysis and a checklist form of common cause failure analysis. Unless these techniques (or a suitable equivalent) are also employed along with the more widely-known assurance tools, high reliability of complex systems may not be adequately assured. This concern is indicated by specific illustrations. 8 references, 5 figures
Quantification of margins and uncertainties: Alternative representations of epistemic uncertainty

International Nuclear Information System (INIS)

Helton, Jon C.; Johnson, Jay D.

2011-01-01

In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e., Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainties (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. A previous presentation, 'Quantification of Margins and Uncertainties: Conceptual and Computational Basis,' describes the basic ideas that underlie QMU and illustrates these ideas with two notional examples that employ probability for the representation of aleatory and epistemic uncertainty. The current presentation introduces and illustrates the use of interval analysis, possibility theory and evidence theory as alternatives to the use of probability theory for the representation of epistemic uncertainty in QMU-type analyses. The following topics are considered: the mathematical structure of alternative representations of uncertainty, alternative representations of epistemic uncertainty in QMU analyses involving only epistemic uncertainty, and alternative representations of epistemic uncertainty in QMU analyses involving a separation of aleatory and epistemic uncertainty. Analyses involving interval analysis, possibility theory and evidence theory are illustrated with the same two notional examples used in the presentation indicated above to illustrate the use of probability to represent aleatory and epistemic uncertainty in QMU analyses.
Applied research in uncertainty modeling and analysis

CERN Document Server

Ayyub, Bilal

2005-01-01

Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on...
Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis.

Science.gov (United States)

Hukkerikar, Amol Shivajirao; Kalakul, Sawitree; Sarup, Bent; Young, Douglas M; Sin, Gürkan; Gani, Rafiqul

2012-11-26

The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals. For parameter estimation, large data sets of experimentally measured property values of a wide range of chemicals (hydrocarbons, oxygenated chemicals, nitrogenated chemicals, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22 environment-related properties, which include the fathead minnow 96-h LC(50), Daphnia magna 48-h LC(50), oral rat LD(50), aqueous solubility, bioconcentration factor, permissible exposure limit (OSHA-TWA), photochemical oxidation potential, global warming potential, ozone depletion potential, acidification potential, emission to urban air (carcinogenic and noncarcinogenic), emission to continental rural air (carcinogenic and noncarcinogenic), emission to continental fresh water (carcinogenic and noncarcinogenic), emission to continental seawater (carcinogenic and noncarcinogenic), emission to continental natural soil (carcinogenic and noncarcinogenic), and emission to continental agricultural soil (carcinogenic and noncarcinogenic) have been modeled and analyzed. The application
Development of a Prototype Model-Form Uncertainty Knowledge Base

Science.gov (United States)

Green, Lawrence L.

2016-01-01

Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.
Uncertainty analysis of the FRAP code

International Nuclear Information System (INIS)

Peck, S.O.

1978-01-01

A user oriented, automated uncertainty analysis capability has been built into the FRAP code (Fuel Rod Analysis Program) and applied to a PWR fuel rod undergoing a LOCA. The method of uncertainty analysis is the Response Surface Method (RSM). (author)
Uncertainty analysis in estimating Japanese ingestion of global fallout Cs-137 using health risk evaluation model

International Nuclear Information System (INIS)

Shimada, Yoko; Morisawa, Shinsuke

1998-01-01

Most of model estimation of the environmental contamination includes some uncertainty associated with the parameter uncertainty in the model. In this study, the uncertainty was analyzed in a model for evaluating the ingestion of radionuclide caused by the long-term global low-level radioactive contamination by using various uncertainty analysis methods: the percentile estimate, the robustness analysis and the fuzzy estimate. The model is mainly composed of five sub-models, which include their own uncertainty; we also analyzed the uncertainty. The major findings obtained in this study include that the possibility of the discrepancy between predicted value by the model simulation and the observed data is less than 10%; the uncertainty of the predicted value is higher before 1950 and after 1980; the uncertainty of the predicted value can be reduced by decreasing the uncertainty of some environmental parameters in the model; the reliability of the model can definitively depend on the following environmental factors: direct foliar absorption coefficient, transfer factor of radionuclide from stratosphere down to troposphere, residual rate by food processing and cooking, transfer factor of radionuclide in ocean and sedimentation in ocean. (author)
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

Science.gov (United States)

Soleimani, Hossein; Hensman, James; Saria, Suchi

2017-08-21

Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.
Uncertainty analysis for results of thermal hydraulic codes of best-estimate-type

International Nuclear Information System (INIS)

Alva N, J.

2010-01-01

In this thesis, some fundamental knowledge is presented about uncertainty analysis and about diverse methodologies applied in the study of nuclear power plant transient event analysis, particularly related to thermal hydraulics phenomena. These concepts and methodologies mentioned in this work come from a wide bibliographical research in the nuclear power subject. Methodologies for uncertainty analysis have been developed by quite diverse institutions, and they have been widely used worldwide for application to results from best-estimate-type computer codes in nuclear reactor thermal hydraulics and safety analysis. Also, the main uncertainty sources, types of uncertainties, and aspects related to best estimate modeling and methods are introduced. Once the main bases of uncertainty analysis have been set, and some of the known methodologies have been introduced, it is presented in detail the CSAU methodology, which will be applied in the analyses. The main objective of this thesis is to compare the results of an uncertainty and sensibility analysis by using the Response Surface Technique to the application of W ilks formula, apply through a loss coolant experiment and an event of rise in a BWR. Both techniques are options in the part of uncertainty and sensibility analysis of the CSAU methodology, which was developed for the analysis of transients and accidents at nuclear power plants, and it is the base of most of the methodologies used in licensing of nuclear power plants practically everywhere. Finally, the results of applying both techniques are compared and discussed. (Author)
Human reliability analysis

International Nuclear Information System (INIS)

Dougherty, E.M.; Fragola, J.R.

1988-01-01

The authors present a treatment of human reliability analysis incorporating an introduction to probabilistic risk assessment for nuclear power generating stations. They treat the subject according to the framework established for general systems theory. Draws upon reliability analysis, psychology, human factors engineering, and statistics, integrating elements of these fields within a systems framework. Provides a history of human reliability analysis, and includes examples of the application of the systems approach
Uncertainty analysis of the FRAP code

International Nuclear Information System (INIS)

Peck, S.O.

1978-01-01

A user oriented, automated uncertainty analysis capability has been built into the Fuel Rod Analysis Program (FRAP) code and has been applied to a pressurized water reactor (PWR) fuel rod undergoing a loss-of-coolant accident (LOCA). The method of uncertainty analysis is the response surface method. The automated version significantly reduced the time required to complete the analysis and, at the same time, greatly increased the problem scope. Results of the analysis showed a significant difference in the total and relative contributions to the uncertainty of the response parameters between steady state and transient conditions
Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

Science.gov (United States)

Huang, Yuechen; Li, Haiyang

2018-06-01

This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.
Reliability Impact of Stockpile Aging: Stress Voiding; TOPICAL

International Nuclear Information System (INIS)

ROBINSON, DAVID G.

1999-01-01

The objective of this research is to statistically characterize the aging of integrated circuit interconnects. This report supersedes the stress void aging characterization presented in SAND99-0975, ''Reliability Degradation Due to Stockpile Aging,'' by the same author. The physics of the stress voiding, before and after wafer processing have been recently characterized by F. G. Yost in SAND99-0601, ''Stress Voiding during Wafer Processing''. The current effort extends this research to account for uncertainties in grain size, storage temperature, void spacing and initial residual stress and their impact on interconnect failure after wafer processing. The sensitivity of the life estimates to these uncertainties is also investigated. Various methods for characterizing the probability of failure of a conductor line were investigated including: Latin hypercube sampling (LHS), quasi-Monte Carlo sampling (qMC), as well as various analytical methods such as the advanced mean value (Ah/IV) method. The comparison was aided by the use of the Cassandra uncertainty analysis library. It was found that the only viable uncertainty analysis methods were those based on either LHS or quasi-Monte Carlo sampling. Analytical methods such as AMV could not be applied due to the nature of the stress voiding problem. The qMC method was chosen since it provided smaller estimation error for a given number of samples. The preliminary results indicate that the reliability of integrated circuits due to stress voiding is very sensitive to the underlying uncertainties associated with grain size and void spacing. In particular, accurate characterization of IC reliability depends heavily on not only the frost and second moments of the uncertainty distribution, but more specifically the unique form of the underlying distribution
Reliability and validity of risk analysis

International Nuclear Information System (INIS)

Aven, Terje; Heide, Bjornar

2009-01-01

In this paper we investigate to what extent risk analysis meets the scientific quality requirements of reliability and validity. We distinguish between two types of approaches within risk analysis, relative frequency-based approaches and Bayesian approaches. The former category includes both traditional statistical inference methods and the so-called probability of frequency approach. Depending on the risk analysis approach, the aim of the analysis is different, the results are presented in different ways and consequently the meaning of the concepts reliability and validity are not the same.
Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions

Science.gov (United States)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. The methods developed herein, which are based on nonlinear constrained optimization, are applicable to requirement functions whose functional dependency on the uncertainty is arbitrary and whose explicit form may even be unknown. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the assumed uncertainty model (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.
Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

Energy Technology Data Exchange (ETDEWEB)

Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.

2015-01-15

Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.
DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 reference manual

Energy Technology Data Exchange (ETDEWEB)

Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandai National Labs, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandai National Labs, Livermore, CA); Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandai National Labs, Livermore, CA); Hough, Patricia Diane (Sandai National Labs, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Guinta, Anthony A.; Brown, Shannon L.

2006-10-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Reliability demonstration test planning using bayesian analysis

International Nuclear Information System (INIS)

Chandran, Senthil Kumar; Arul, John A.

2003-01-01

In Nuclear Power Plants, the reliability of all the safety systems is very critical from the safety viewpoint and it is very essential that the required reliability requirements be met while satisfying the design constraints. From practical experience, it is found that the reliability of complex systems such as Safety Rod Drive Mechanism is of the order of 10 -4 with an uncertainty factor of 10. To demonstrate the reliability of such systems is prohibitive in terms of cost and time as the number of tests needed is very large. The purpose of this paper is to develop a Bayesian reliability demonstrating testing procedure for exponentially distributed failure times with gamma prior distribution on the failure rate which can be easily and effectively used to demonstrate component/subsystem/system reliability conformance to stated requirements. The important questions addressed in this paper are: With zero failures, how long one should perform the tests and how many components are required to conclude with a given degree of confidence, that the component under test, meets the reliability requirement. The procedure is explained with an example. This procedure can also be extended to demonstrate with more number of failures. The approach presented is applicable for deriving test plans for demonstrating component failure rates of nuclear power plants, as the failure data for similar components are becoming available in existing plants elsewhere. The advantages of this procedure are the criterion upon which the procedure is based is simple and pertinent, the fitting of the prior distribution is an integral part of the procedure and is based on the use of information regarding two percentiles of this distribution and finally, the procedure is straightforward and easy to apply in practice. (author)

Uncertainty analysis for the assembly and core simulation of BEAVRS at the HZP conditions

Energy Technology Data Exchange (ETDEWEB)

Wan, Chenghui [School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an 710049 (China); Cao, Liangzhi, E-mail: caolz@mail.xjtu.edu.cn [School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an 710049 (China); Wu, Hongchun [School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an 710049 (China); Shen, Wei [School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an 710049 (China); Canadian Nuclear Safety Commission, Ottawa, Ontario (Canada)

2017-04-15

Highlights: • Uncertainty analysis has been completed based on the “two-step” scheme. • Uncertainty analysis has been performed to BEAVRS at HZP. • For lattice calculations, the few-group constant’s uncertainty was quantified. • For core simulation, uncertainties of k{sub eff} and power distributions were quantified. - Abstract: Based on the “two-step” scheme for the reactor-physics calculations, the capability of uncertainty analysis for the core simulations has been implemented in the UNICORN code, an in-house code for the sensitivity and uncertainty analysis of the reactor-physics calculations. Applying the statistical sampling method, the nuclear-data uncertainties can be propagated to the important predictions of the core simulations. The uncertainties of the few-group constants introduced by the uncertainties of the multigroup microscopic cross sections are quantified first for the lattice calculations; the uncertainties of the few-group constants are then propagated to the core multiplication factor and core power distributions for the core simulations. Up to now, our in-house lattice code NECP-CACTI and the neutron-diffusion solver NECP-VIOLET have been implemented in UNICORN for the steady-state core simulations based on the “two-step” scheme. With NECP-CACTI and NECP-VIOLET, the modeling and simulation of the steady-state BEAVRS benchmark problem at the HZP conditions was performed, and the results were compared with those obtained by CASMO-4E. Based on the modeling and simulation, the UNICORN code has been applied to perform the uncertainty analysis for BAEVRS at HZP. The uncertainty results of the eigenvalues and two-group constants for the lattice calculations and the multiplication factor and the power distributions for the steady-state core simulations are obtained and analyzed in detail.
Uncertainty analysis for the assembly and core simulation of BEAVRS at the HZP conditions

International Nuclear Information System (INIS)

Wan, Chenghui; Cao, Liangzhi; Wu, Hongchun; Shen, Wei

2017-01-01

Highlights: • Uncertainty analysis has been completed based on the “two-step” scheme. • Uncertainty analysis has been performed to BEAVRS at HZP. • For lattice calculations, the few-group constant’s uncertainty was quantified. • For core simulation, uncertainties of k_e_f_f and power distributions were quantified. - Abstract: Based on the “two-step” scheme for the reactor-physics calculations, the capability of uncertainty analysis for the core simulations has been implemented in the UNICORN code, an in-house code for the sensitivity and uncertainty analysis of the reactor-physics calculations. Applying the statistical sampling method, the nuclear-data uncertainties can be propagated to the important predictions of the core simulations. The uncertainties of the few-group constants introduced by the uncertainties of the multigroup microscopic cross sections are quantified first for the lattice calculations; the uncertainties of the few-group constants are then propagated to the core multiplication factor and core power distributions for the core simulations. Up to now, our in-house lattice code NECP-CACTI and the neutron-diffusion solver NECP-VIOLET have been implemented in UNICORN for the steady-state core simulations based on the “two-step” scheme. With NECP-CACTI and NECP-VIOLET, the modeling and simulation of the steady-state BEAVRS benchmark problem at the HZP conditions was performed, and the results were compared with those obtained by CASMO-4E. Based on the modeling and simulation, the UNICORN code has been applied to perform the uncertainty analysis for BAEVRS at HZP. The uncertainty results of the eigenvalues and two-group constants for the lattice calculations and the multiplication factor and the power distributions for the steady-state core simulations are obtained and analyzed in detail.
Uncertainty management in integrated modelling, the IMAGE case

International Nuclear Information System (INIS)

Van der Sluijs, J.P.

1995-01-01

Integrated assessment models of global environmental problems play an increasingly important role in decision making. This use demands a good insight regarding the reliability of these models. In this paper we analyze uncertainty management in the IMAGE-project (Integrated Model to Assess the Greenhouse Effect). We use a classification scheme comprising type and source of uncertainty. Our analysis shows reliability analysis as main area for improvement. We briefly review a recently developed methodology, NUSAP (Numerical, Unit, Spread, Assessment and Pedigree), that systematically addresses the strength of data in terms of spread, reliability and scientific status (pedigree) of information. This approach is being tested through interviews with model builders. 3 tabs., 20 refs
Dependence assessment in human reliability analysis based on D numbers and AHP

International Nuclear Information System (INIS)

Zhou, Xinyi; Deng, Xinyang; Deng, Yong; Mahadevan, Sankaran

2017-01-01

Highlights: • D numbers and AHP are combined to implement dependence assessment in HRA. • A new tool, called D numbers, is used to deal with the uncertainty in HRA. • The proposed method can well address the fuzziness and subjectivity in linguistic assessment. • The proposed method is well applicable in dependence assessment which inherently has a linguistic assessment process. - Abstract: Since human errors always cause heavy loss especially in nuclear engineering, human reliability analysis (HRA) has attracted more and more attention. Dependence assessment plays a vital role in HRA, measuring the dependence degree of human errors. Many researches have been done while still have improvement space. In this paper, a dependence assessment model based on D numbers and analytic hierarchy process (AHP) is proposed. Firstly, identify the factors used to measure the dependence level of two human operations. Besides, in terms of the suggested dependence level, determine and quantify the anchor points for each factor. Secondly, D numbers and AHP are adopted in model. Experts evaluate the dependence level of human operations for each factor. Then, the evaluation results are presented as D numbers and fused by D number’s combination rule that can obtain the dependence probability of human operations for each factor. The weights of factors can be determined by AHP. Thirdly, based on the dependence probability for each factor and its corresponding weight, the dependence probability of two human operations and its confidence can be obtained. The proposed method can well address the fuzziness and subjectivity in linguistic assessment. The proposed method is well applicable to assess the dependence degree of human errors in HRA which inherently has a linguistic assessment process.
Dependence assessment in human reliability analysis based on D numbers and AHP

Energy Technology Data Exchange (ETDEWEB)

Zhou, Xinyi; Deng, Xinyang [School of Computer and Information Science, Southwest University, Chongqing 400715 (China); Deng, Yong, E-mail: ydeng@swu.edu.cn [School of Computer and Information Science, Southwest University, Chongqing 400715 (China); Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054 (China); Mahadevan, Sankaran [School of Engineering, Vanderbilt University, Nashville, TN 37235 (United States)

2017-03-15

Highlights: • D numbers and AHP are combined to implement dependence assessment in HRA. • A new tool, called D numbers, is used to deal with the uncertainty in HRA. • The proposed method can well address the fuzziness and subjectivity in linguistic assessment. • The proposed method is well applicable in dependence assessment which inherently has a linguistic assessment process. - Abstract: Since human errors always cause heavy loss especially in nuclear engineering, human reliability analysis (HRA) has attracted more and more attention. Dependence assessment plays a vital role in HRA, measuring the dependence degree of human errors. Many researches have been done while still have improvement space. In this paper, a dependence assessment model based on D numbers and analytic hierarchy process (AHP) is proposed. Firstly, identify the factors used to measure the dependence level of two human operations. Besides, in terms of the suggested dependence level, determine and quantify the anchor points for each factor. Secondly, D numbers and AHP are adopted in model. Experts evaluate the dependence level of human operations for each factor. Then, the evaluation results are presented as D numbers and fused by D number’s combination rule that can obtain the dependence probability of human operations for each factor. The weights of factors can be determined by AHP. Thirdly, based on the dependence probability for each factor and its corresponding weight, the dependence probability of two human operations and its confidence can be obtained. The proposed method can well address the fuzziness and subjectivity in linguistic assessment. The proposed method is well applicable to assess the dependence degree of human errors in HRA which inherently has a linguistic assessment process.
Reliability and safety analyses under fuzziness

International Nuclear Information System (INIS)

Onisawa, T.; Kacprzyk, J.

1995-01-01

Fuzzy theory, for example possibility theory, is compatible with probability theory. What is shown so far is that probability theory needs not be replaced by fuzzy theory, but rather that the former works much better in applications if it is combined with the latter. In fact, it is said that there are two essential uncertainties in the field of reliability and safety analyses: One is a probabilistic uncertainty which is more relevant for mechanical systems and the natural environment, and the other is fuzziness (imprecision) caused by the existence of human beings in systems. The classical probability theory alone is therefore not sufficient to deal with uncertainties in humanistic system. In such a context this collection of works will put a milestone in the arguments of probability theory and fuzzy theory. This volume covers fault analysis, life time analysis, reliability, quality control, safety analysis and risk analysis. (orig./DG). 106 figs
Methodologies for uncertainty analysis in the level 2 PSA and their implementation procedures

International Nuclear Information System (INIS)

Ahn, Kwang Il; Yang, Joon Eun; Kim, Dong Ha

2002-04-01

Main purpose of this report to present standardized methodologies for uncertainty analysis in the Level 2 Probabilistic Safety Assessment (PSA) and their implementation procedures, based on results obtained through a critical review of the existing methodologies for the analysis of uncertainties employed in the Level 2 PSA, especially Accident Progression Event Tree (APET). Uncertainties employed in the Level 2 PSA, quantitative expressions of overall knowledge of analysts' and experts' participating in the probabilistic quantification process of phenomenological accident progressions ranging from core melt to containment failure, their numerical values are directly related to the degree of confidence that the analyst has that a given phenomenological event or accident process will or will not occur, or analyst's subjective probabilities of occurrence. These results that are obtained from Level 2 PSA uncertainty analysis, become an essential contributor to the plant risk, in addition to the Level 1 PSA and Level 3 PSA uncertainties. Uncertainty analysis methodologies and their implementation procedures presented in this report was prepared based on the following criteria: 'uncertainty quantification process must be logical, scrutable, complete, consistent and in an appropriate level of detail, as mandated by the Level 2 PSA objectives'. For the aforementioned purpose, this report deals mainly with (1) summary of general or Level 2 PSA specific uncertainty analysis methodologies, (2) selection of phenomenological branch events for uncertainty analysis in the APET, methodology for quantification of APET uncertainty inputs and its implementation procedure, (3) statistical propagation of uncertainty inputs through APET and its implementation procedure, and (4) formal procedure for quantification of APET uncertainties and source term categories (STCs) through the Level 2 PSA quantification codes
Use of knowledge based systems for rational reliability analysis based inspection and maintenance planning for offshore structures

International Nuclear Information System (INIS)

Tang, M.X.; Dharmavasan, S.; Peers, S.M.C.

1994-01-01

The structural integrity of fixed offshore platforms is ensured by periodic inspections. In the past, decisions made as to when, where and how to inspect have been made by engineers using rules-of-thumb and general planning heuristics. It is now hoped that more rational inspection and maintenance scheduling may be carried out by applying recently developed techniques based on structural reliability methods. However, one of the problems associated with a theoretical approach is that it is not always possible to incorporate all the constraints that are present in a practical situation. These constraints modify the decisions made for analysis data input and the interpretation of the analysis results. Knowledge based systems provide a mean of encapsulating several different forms of information and knowledge within a computer system and hence can overcome this problem. In this paper, a prototype system being developed for integrating reliability based analysis with other constraints for inspection scheduling will be described. In addition, the scheduling model and the algorithms to carry out the scheduling will be explained. Furthermore, implementation details are also given
Uncertainty visualization in HARDI based on ensembles of ODFs

KAUST Repository

Jiao, Fangxiang; Phillips, Jeff M.; Gur, Yaniv; Johnson, Chris R.

2012-01-01

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. © 2012 IEEE.
Uncertainty visualization in HARDI based on ensembles of ODFs

KAUST Repository

Jiao, Fangxiang

2012-02-01

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes. © 2012 IEEE.
Uncertainty as Knowledge: Constraints on Policy Choices Provided by Analysis of Uncertainty

Science.gov (United States)

Lewandowsky, S.; Risbey, J.; Smithson, M.; Newell, B. R.

2012-12-01

Uncertainty forms an integral part of climate science, and it is often cited in connection with arguments against mitigative action. We argue that an analysis of uncertainty must consider existing knowledge as well as uncertainty, and the two must be evaluated with respect to the outcomes and risks associated with possible policy options. Although risk judgments are inherently subjective, an analysis of the role of uncertainty within the climate system yields two constraints that are robust to a broad range of assumptions. Those constraints are that (a) greater uncertainty about the climate system is necessarily associated with greater expected damages from warming, and (b) greater uncertainty translates into a greater risk of the failure of mitigation efforts. These ordinal constraints are unaffected by subjective or cultural risk-perception factors, they are independent of the discount rate, and they are independent of the magnitude of the estimate for climate sensitivity. The constraints mean that any appeal to uncertainty must imply a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.
Mechanical reliability analysis of tubes intended for hydrocarbons

Energy Technology Data Exchange (ETDEWEB)

Nahal, Mourad; Khelif, Rabia [Badji Mokhtar University, Annaba (Algeria)

2013-02-15

Reliability analysis constitutes an essential phase in any study concerning reliability. Many industrialists evaluate and improve the reliability of their products during the development cycle - from design to startup (design, manufacture, and exploitation) - to develop their knowledge on cost/reliability ratio and to control sources of failure. In this study, we obtain results for hardness, tensile, and hydrostatic tests carried out on steel tubes for transporting hydrocarbons followed by statistical analysis. Results obtained allow us to conduct a reliability study based on resistance request. Thus, index of reliability is calculated and the importance of the variables related to the tube is presented. Reliability-based assessment of residual stress effects is applied to underground pipelines under a roadway, with and without active corrosion. Residual stress has been found to greatly increase probability of failure, especially in the early stages of pipe lifetime.
Uncertainty-based simulation-optimization using Gaussian process emulation: Application to coastal groundwater management

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ketabchi, Hamed

2017-12-01

Combined simulation-optimization (S/O) schemes have long been recognized as a valuable tool in coastal groundwater management (CGM). However, previous applications have mostly relied on deterministic seawater intrusion (SWI) simulations. This is a questionable simplification, knowing that SWI models are inevitably prone to epistemic and aleatory uncertainty, and hence a management strategy obtained through S/O without consideration of uncertainty may result in significantly different real-world outcomes than expected. However, two key issues have hindered the use of uncertainty-based S/O schemes in CGM, which are addressed in this paper. The first issue is how to solve the computational challenges resulting from the need to perform massive numbers of simulations. The second issue is how the management problem is formulated in presence of uncertainty. We propose the use of Gaussian process (GP) emulation as a valuable tool in solving the computational challenges of uncertainty-based S/O in CGM. We apply GP emulation to the case study of Kish Island (located in the Persian Gulf) using an uncertainty-based S/O algorithm which relies on continuous ant colony optimization and Monte Carlo simulation. In doing so, we show that GP emulation can provide an acceptable level of accuracy, with no bias and low statistical dispersion, while tremendously reducing the computational time. Moreover, five new formulations for uncertainty-based S/O are presented based on concepts such as energy distances, prediction intervals and probabilities of SWI occurrence. We analyze the proposed formulations with respect to their resulting optimized solutions, the sensitivity of the solutions to the intended reliability levels, and the variations resulting from repeated optimization runs.
A study in the reliability analysis method for nuclear power plant structures (I)

Energy Technology Data Exchange (ETDEWEB)

Oh, Byung Hwan; Choi, Seong Cheol; Shin, Ho Sang; Yang, In Hwan; Kim, Yi Sung; Yu, Young; Kim, Se Hun [Seoul, Nationl Univ., Seoul (Korea, Republic of)

1999-03-15

Nuclear power plant structures may be exposed to aggressive environmental effects that may cause their strength and stiffness to decrease over their service life. Although the physics of these damage mechanisms are reasonably well understood and quantitative evaluation of their effects on time-dependent structural behavior is possible in some instances, such evaluations are generally very difficult and remain novel. The assessment of existing steel containment in nuclear power plants for continued service must provide quantitative evidence that they are able to withstand future extreme loads during a service period with an acceptable level of reliability. Rational methodologies to perform the reliability assessment can be developed from mechanistic models of structural deterioration, using time-dependent structural reliability analysis to take loading and strength uncertainties into account. The final goal of this study is to develop the analysis method for the reliability of containment structures. The cause and mechanism of corrosion is first clarified and the reliability assessment method has been established. By introducing the equivalent normal distribution, the procedure of reliability analysis which can determine the failure probabilities has been established. The influence of design variables to reliability and the relation between the reliability and service life will be continued second year research.
Time-variant reliability assessment through equivalent stochastic process transformation

International Nuclear Information System (INIS)

Wang, Zequn; Chen, Wei

2016-01-01

Time-variant reliability measures the probability that an engineering system successfully performs intended functions over a certain period of time under various sources of uncertainty. In practice, it is computationally prohibitive to propagate uncertainty in time-variant reliability assessment based on expensive or complex numerical models. This paper presents an equivalent stochastic process transformation approach for cost-effective prediction of reliability deterioration over the life cycle of an engineering system. To reduce the high dimensionality, a time-independent reliability model is developed by translating random processes and time parameters into random parameters in order to equivalently cover all potential failures that may occur during the time interval of interest. With the time-independent reliability model, an instantaneous failure surface is attained by using a Kriging-based surrogate model to identify all potential failure events. To enhance the efficacy of failure surface identification, a maximum confidence enhancement method is utilized to update the Kriging model sequentially. Then, the time-variant reliability is approximated using Monte Carlo simulations of the Kriging model where system failures over a time interval are predicted by the instantaneous failure surface. The results of two case studies demonstrate that the proposed approach is able to accurately predict the time evolution of system reliability while requiring much less computational efforts compared with the existing analytical approach. - Highlights: • Developed a new approach for time-variant reliability analysis. • Proposed a novel stochastic process transformation procedure to reduce the dimensionality. • Employed Kriging models with confidence-based adaptive sampling scheme to enhance computational efficiency. • The approach is effective for handling random process in time-variant reliability analysis. • Two case studies are used to demonstrate the efficacy
Uncertainty analysis in WWTP model applications: a critical discussion using an example from design

DEFF Research Database (Denmark)

Sin, Gürkan; Gernaey, Krist; Neumann, Marc B.

2009-01-01

of design performance criteria differs significantly. The implication for the practical applications of uncertainty analysis in the wastewater industry is profound: (i) as the uncertainty analysis results are specific to the framing used, the results must be interpreted within the context of that framing......This study focuses on uncertainty analysis of WWTP models and analyzes the issue of framing and how it affects the interpretation of uncertainty analysis results. As a case study, the prediction of uncertainty involved in model-based design of a wastewater treatment plant is studied. The Monte...... to stoichiometric, biokinetic and influent parameters; (2) uncertainty due to hydraulic behaviour of the plant and mass transfer parameters; (3) uncertainty due to the combination of (1) and (2). The results demonstrate that depending on the way the uncertainty analysis is framed, the estimated uncertainty...
Reliability Analysis of Sealing Structure of Electromechanical System Based on Kriging Model

Science.gov (United States)

Zhang, F.; Wang, Y. M.; Chen, R. W.; Deng, W. W.; Gao, Y.

2018-05-01

The sealing performance of aircraft electromechanical system has a great influence on flight safety, and the reliability of its typical seal structure is analyzed by researcher. In this paper, we regard reciprocating seal structure as a research object to study structural reliability. Having been based on the finite element numerical simulation method, the contact stress between the rubber sealing ring and the cylinder wall is calculated, and the relationship between the contact stress and the pressure of the hydraulic medium is built, and the friction force on different working conditions are compared. Through the co-simulation, the adaptive Kriging model obtained by EFF learning mechanism is used to describe the failure probability of the seal ring, so as to evaluate the reliability of the sealing structure. This article proposes a new idea of numerical evaluation for the reliability analysis of sealing structure, and also provides a theoretical basis for the optimal design of sealing structure.
Regional inversion of CO2 ecosystem fluxes from atmospheric measurements. Reliability of the uncertainty estimates

Energy Technology Data Exchange (ETDEWEB)

Broquet, G.; Chevallier, F.; Breon, F.M.; Yver, C.; Ciais, P.; Ramonet, M.; Schmidt, M. [Laboratoire des Sciences du Climat et de l' Environnement, CEA-CNRS-UVSQ, UMR8212, IPSL, Gif-sur-Yvette (France); Alemanno, M. [Servizio Meteorologico dell' Aeronautica Militare Italiana, Centro Aeronautica Militare di Montagna, Monte Cimone/Sestola (Italy); Apadula, F. [Research on Energy Systems, RSE, Environment and Sustainable Development Department, Milano (Italy); Hammer, S. [Universitaet Heidelberg, Institut fuer Umweltphysik, Heidelberg (Germany); Haszpra, L. [Hungarian Meteorological Service, Budapest (Hungary); Meinhardt, F. [Federal Environmental Agency, Kirchzarten (Germany); Necki, J. [AGH University of Science and Technology, Krakow (Poland); Piacentino, S. [ENEA, Laboratory for Earth Observations and Analyses, Palermo (Italy); Thompson, R.L. [Max Planck Institute for Biogeochemistry, Jena (Germany); Vermeulen, A.T. [Energy research Centre of the Netherlands ECN, EEE-EA, Petten (Netherlands)

2013-07-01

The Bayesian framework of CO2 flux inversions permits estimates of the retrieved flux uncertainties. Here, the reliability of these theoretical estimates is studied through a comparison against the misfits between the inverted fluxes and independent measurements of the CO2 Net Ecosystem Exchange (NEE) made by the eddy covariance technique at local (few hectares) scale. Regional inversions at 0.5{sup 0} resolution are applied for the western European domain where {approx}50 eddy covariance sites are operated. These inversions are conducted for the period 2002-2007. They use a mesoscale atmospheric transport model, a prior estimate of the NEE from a terrestrial ecosystem model and rely on the variational assimilation of in situ continuous measurements of CO2 atmospheric mole fractions. Averaged over monthly periods and over the whole domain, the misfits are in good agreement with the theoretical uncertainties for prior and inverted NEE, and pass the chi-square test for the variance at the 30% and 5% significance levels respectively, despite the scale mismatch and the independence between the prior (respectively inverted) NEE and the flux measurements. The theoretical uncertainty reduction for the monthly NEE at the measurement sites is 53% while the inversion decreases the standard deviation of the misfits by 38 %. These results build confidence in the NEE estimates at the European/monthly scales and in their theoretical uncertainty from the regional inverse modelling system. However, the uncertainties at the monthly (respectively annual) scale remain larger than the amplitude of the inter-annual variability of monthly (respectively annual) fluxes, so that this study does not engender confidence in the inter-annual variations. The uncertainties at the monthly scale are significantly smaller than the seasonal variations. The seasonal cycle of the inverted fluxes is thus reliable. In particular, the CO2 sink period over the European continent likely ends later than
Reactor pressure vessels safety and reliability - certainty and uncertainty

International Nuclear Information System (INIS)

O'Neil, R.

1977-01-01

In the paper, it is suggested that the hazard to the population which would result from vessel failure rate of the order of 10 -6 to 10 -7 per vessel year could be acceptable to society on the basis of other natural and man-made risks. The paper considers the problems of demonstrating safety by calculation based on fracture mechanics, and indicates some of the uncertainties, and inconsistencies in the theory, particularly the effect of cracks in locally degraded volumes of material. The phenomenon of crack arrest is considered, and attention is drawn to the uncertainties as indicated at least by some tests. There is need for speedy resolution of this problem. The uncertainties in material properties, heat treatment and residual stresses are considered, and a proposed upper limit for residual defects ('original sin') is proposed. (orig.) [de
Good Modeling Practice for PAT Applications: Propagation of Input Uncertainty and Sensitivity Analysis

DEFF Research Database (Denmark)

Sin, Gürkan; Gernaey, Krist; Eliasson Lantz, Anna

2009-01-01

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input...... compared to the large uncertainty observed in the antibiotic and off-gas CO2 predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which...... promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute...

Reliability analysis for thermal cutting method based non-explosive separation device

International Nuclear Information System (INIS)

Choi, Jun Woo; Hwang, Kuk Ha; Kim, Byung Kyu

2016-01-01

In order to increase the reliability of a separation device for a small satellite, a new non-explosive separation device is invented. This device is activated using a thermal cutting method with a Ni-Cr wire. A reliability analysis is carried out for the proposed non-explosive separation device by applying the Fault tree analysis (FTA) method. In the FTA results for the separation device, only ten single-point failure modes are found. The reliability modeling and analysis for the device are performed considering failure of the power supply, the Ni-Cr wire burns failure and unwinds, the holder separation failure, the balls separation failure, and the pin release failure. Ultimately, the reliability of the proposed device is calculated as 0.999989 with five Ni-Cr wire coils
Reliability analysis for thermal cutting method based non-explosive separation device

Energy Technology Data Exchange (ETDEWEB)

Choi, Jun Woo; Hwang, Kuk Ha; Kim, Byung Kyu [Korea Aerospace University, Goyang (Korea, Republic of)

2016-12-15

In order to increase the reliability of a separation device for a small satellite, a new non-explosive separation device is invented. This device is activated using a thermal cutting method with a Ni-Cr wire. A reliability analysis is carried out for the proposed non-explosive separation device by applying the Fault tree analysis (FTA) method. In the FTA results for the separation device, only ten single-point failure modes are found. The reliability modeling and analysis for the device are performed considering failure of the power supply, the Ni-Cr wire burns failure and unwinds, the holder separation failure, the balls separation failure, and the pin release failure. Ultimately, the reliability of the proposed device is calculated as 0.999989 with five Ni-Cr wire coils.
Analysis and Application of Reliability

International Nuclear Information System (INIS)

Jeong, Hae Seong; Park, Dong Ho; Kim, Jae Ju

1999-05-01

This book tells of analysis and application of reliability, which includes definition, importance and historical background of reliability, function of reliability and failure rate, life distribution and assumption of reliability, reliability of unrepaired system, reliability of repairable system, sampling test of reliability, failure analysis like failure analysis by FEMA and FTA, and cases, accelerated life testing such as basic conception, acceleration and acceleration factor, and analysis of accelerated life testing data, maintenance policy about alternation and inspection.
Holistic uncertainty analysis in river basin modeling for climate vulnerability assessment

Science.gov (United States)

Taner, M. U.; Wi, S.; Brown, C.

2017-12-01

The challenges posed by uncertain future climate are a prominent concern for water resources managers. A number of frameworks exist for assessing the impacts of climate-related uncertainty, including internal climate variability and anthropogenic climate change, such as scenario-based approaches and vulnerability-based approaches. While in many cases climate uncertainty may be dominant, other factors such as future evolution of the river basin, hydrologic response and reservoir operations are potentially significant sources of uncertainty. While uncertainty associated with modeling hydrologic response has received attention, very little attention has focused on the range of uncertainty and possible effects of the water resources infrastructure and management. This work presents a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. The uncertainties explored include those associated with climate variability and change, hydrologic model parameters, and water system operation rules. A Bayesian framework is used to quantify and model the uncertainties at each modeling steps in integrated fashion, including prior and the likelihood information about model parameters. The framework is demonstrated in a case study for the St. Croix Basin located at border of United States and Canada.
Individual Differences in Human Reliability Analysis

Energy Technology Data Exchange (ETDEWEB)

Jeffrey C. Joe; Ronald L. Boring

2014-06-01

While human reliability analysis (HRA) methods include uncertainty in quantification, the nominal model of human error in HRA typically assumes that operator performance does not vary significantly when they are given the same initiating event, indicators, procedures, and training, and that any differences in operator performance are simply aleatory (i.e., random). While this assumption generally holds true when performing routine actions, variability in operator response has been observed in multiple studies, especially in complex situations that go beyond training and procedures. As such, complexity can lead to differences in operator performance (e.g., operator understanding and decision-making). Furthermore, psychological research has shown that there are a number of known antecedents (i.e., attributable causes) that consistently contribute to observable and systematically measurable (i.e., not random) differences in behavior. This paper reviews examples of individual differences taken from operational experience and the psychological literature. The impact of these differences in human behavior and their implications for HRA are then discussed. We propose that individual differences should not be treated as aleatory, but rather as epistemic. Ultimately, by understanding the sources of individual differences, it is possible to remove some epistemic uncertainty from analyses.
Quantitative Analysis of Variability and Uncertainty in Environmental Data and Models. Volume 1. Theory and Methodology Based Upon Bootstrap Simulation

Energy Technology Data Exchange (ETDEWEB)

Frey, H. Christopher [North Carolina State University, Raleigh, NC (United States); Rhodes, David S. [North Carolina State University, Raleigh, NC (United States)

1999-04-30

This is Volume 1 of a two-volume set of reports describing work conducted at North Carolina State University sponsored by Grant Number DE-FG05-95ER30250 by the U.S. Department of Energy. The title of the project is “Quantitative Analysis of Variability and Uncertainty in Acid Rain Assessments.” The work conducted under sponsorship of this grant pertains primarily to two main topics: (1) development of new methods for quantitative analysis of variability and uncertainty applicable to any type of model; and (2) analysis of variability and uncertainty in the performance, emissions, and cost of electric power plant combustion-based NOx control technologies. These two main topics are reported separately in Volumes 1 and 2.
How does uncertainty shape patient experience in advanced illness? A secondary analysis of qualitative data.

Science.gov (United States)

Etkind, Simon Noah; Bristowe, Katherine; Bailey, Katharine; Selman, Lucy Ellen; Murtagh, Fliss Em

2017-02-01

Uncertainty is common in advanced illness but is infrequently studied in this context. If poorly addressed, uncertainty can lead to adverse patient outcomes. We aimed to understand patient experiences of uncertainty in advanced illness and develop a typology of patients' responses and preferences to inform practice. Secondary analysis of qualitative interview transcripts. Studies were assessed for inclusion and interviews were sampled using maximum-variation sampling. Analysis used a thematic approach with 10% of coding cross-checked to enhance reliability. Qualitative interviews from six studies including patients with heart failure, chronic obstructive pulmonary disease, renal disease, cancer and liver failure. A total of 30 transcripts were analysed. Median age was 75 (range, 43-95), 12 patients were women. The impact of uncertainty was frequently discussed: the main related themes were engagement with illness, information needs, patient priorities and the period of time that patients mainly focused their attention on (temporal focus). A typology of patient responses to uncertainty was developed from these themes. Uncertainty influences patient experience in advanced illness through affecting patients' information needs, preferences and future priorities for care. Our typology aids understanding of how patients with advanced illness respond to uncertainty. Assessment of these three factors may be a useful starting point to guide clinical assessment and shared decision making.
User's manual of SECOM2: a computer code for seismic system reliability analysis

International Nuclear Information System (INIS)

Uchiyama, Tomoaki; Oikawa, Tetsukuni; Kondo, Masaaki; Tamura, Kazuo

2002-03-01

This report is the user's manual of seismic system reliability analysis code SECOM2 (Seismic Core Melt Frequency Evaluation Code Ver.2) developed at the Japan Atomic Energy Research Institute for systems reliability analysis, which is one of the tasks of seismic probabilistic safety assessment (PSA) of nuclear power plants (NPPs). The SECOM2 code has many functions such as: Calculation of component failure probabilities based on the response factor method, Extraction of minimal cut sets (MCSs), Calculation of conditional system failure probabilities for given seismic motion levels at the site of an NPP, Calculation of accident sequence frequencies and the core damage frequency (CDF) with use of the seismic hazard curve, Importance analysis using various indicators, Uncertainty analysis, Calculation of the CDF taking into account the effect of the correlations of responses and capacities of components, and Efficient sensitivity analysis by changing parameters on responses and capacities of components. These analyses require the fault tree (FT) representing the occurrence condition of the system failures and core damage, information about response and capacity of components and seismic hazard curve for the NPP site as inputs. This report presents the models and methods applied in the SECOM2 code and how to use those functions. (author)
Model Uncertainty for Bilinear Hysteretic Systems

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1984-01-01

. The statistical uncertainty -due to lack of information can e.g. be taken into account by describing the variables by predictive density functions, Veneziano [2). In general, model uncertainty is the uncertainty connected with mathematical modelling of the physical reality. When structural reliability analysis...... is related to the concept of a failure surface (or limit state surface) in the n-dimensional basic variable space then model uncertainty is at least due to the neglected variables, the modelling of the failure surface and the computational technique used. A more precise definition is given in section 2...
Uncertainty analysis methods for quantification of source terms using a large computer code

International Nuclear Information System (INIS)

Han, Seok Jung

1997-02-01

Quantification of uncertainties in the source term estimations by a large computer code, such as MELCOR and MAAP, is an essential process of the current probabilistic safety assessments (PSAs). The main objectives of the present study are (1) to investigate the applicability of a combined procedure of the response surface method (RSM) based on input determined from a statistical design and the Latin hypercube sampling (LHS) technique for the uncertainty analysis of CsI release fractions under a hypothetical severe accident sequence of a station blackout at Young-Gwang nuclear power plant using MAAP3.0B code as a benchmark problem; and (2) to propose a new measure of uncertainty importance based on the distributional sensitivity analysis. On the basis of the results obtained in the present work, the RSM is recommended to be used as a principal tool for an overall uncertainty analysis in source term quantifications, while using the LHS in the calculations of standardized regression coefficients (SRC) and standardized rank regression coefficients (SRRC) to determine the subset of the most important input parameters in the final screening step and to check the cumulative distribution functions (cdfs) obtained by RSM. Verification of the response surface model for its sufficient accuracy is a prerequisite for the reliability of the final results obtained by the combined procedure proposed in the present work. In the present study a new measure has been developed to utilize the metric distance obtained from cumulative distribution functions (cdfs). The measure has been evaluated for three different cases of distributions in order to assess the characteristics of the measure: The first case and the second are when the distribution is known as analytical distributions and the other case is when the distribution is unknown. The first case is given by symmetry analytical distributions. The second case consists of two asymmetry distributions of which the skewness is non zero
Lognormal Approximations of Fault Tree Uncertainty Distributions.

Science.gov (United States)

El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P

2018-01-26

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.
Parameter Uncertainty for Repository Thermal Analysis

Energy Technology Data Exchange (ETDEWEB)

Hardin, Ernest [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hadgu, Teklu [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Greenberg, Harris [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Dupont, Mark [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2015-10-01

This report is one follow-on to a study of reference geologic disposal design concepts (Hardin et al. 2011a). Based on an analysis of maximum temperatures, that study concluded that certain disposal concepts would require extended decay storage prior to emplacement, or the use of small waste packages, or both. The study used nominal values for thermal properties of host geologic media and engineered materials, demonstrating the need for uncertainty analysis to support the conclusions. This report is a first step that identifies the input parameters of the maximum temperature calculation, surveys published data on measured values, uses an analytical approach to determine which parameters are most important, and performs an example sensitivity analysis. Using results from this first step, temperature calculations planned for FY12 can focus on only the important parameters, and can use the uncertainty ranges reported here. The survey of published information on thermal properties of geologic media and engineered materials, is intended to be sufficient for use in generic calculations to evaluate the feasibility of reference disposal concepts. A full compendium of literature data is beyond the scope of this report. The term “uncertainty” is used here to represent both measurement uncertainty and spatial variability, or variability across host geologic units. For the most important parameters (e.g., buffer thermal conductivity) the extent of literature data surveyed samples these different forms of uncertainty and variability. Finally, this report is intended to be one chapter or section of a larger FY12 deliverable summarizing all the work on design concepts and thermal load management for geologic disposal (M3FT-12SN0804032, due 15Aug2012).
Sensitivity and uncertainty analysis of NET/ITER shielding blankets

International Nuclear Information System (INIS)

Hogenbirk, A.; Gruppelaar, H.; Verschuur, K.A.

1990-09-01

Results are presented of sensitivity and uncertainty calculations based upon the European fusion file (EFF-1). The effect of uncertainties in Fe, Cr and Ni cross sections on the nuclear heating in the coils of a NET/ITER shielding blanket has been studied. The analysis has been performed for the total cross section as well as partial cross sections. The correct expression for the sensitivity profile was used, including the gain term. The resulting uncertainty in the nuclear heating lies between 10 and 20 per cent. (author). 18 refs.; 2 figs.; 2 tabs
Micropollutants throughout an integrated urban drainage model: Sensitivity and uncertainty analysis

Science.gov (United States)

Mannina, Giorgio; Cosenza, Alida; Viviani, Gaspare

2017-11-01

The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole - SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e., SS, WWTP and RWB), have been considered applying, for the sensitivity analysis, the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results have demonstrated that sensitivity analysis is a powerful tool for increasing operator confidence in the modelling results. The approach adopted here can be used for blocking some non-identifiable factors, thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modeling in the RWB when all model factors (scenario 1) or model factors of SS (scenarios 2 and 3) are varied. If the only factors related to the WWTP are changed (scenarios 4 and 5), the SMX concentration in the RWB is mainly influenced (till to 95% influence of the total variance for SSMX,max) by the aerobic sorption coefficient. A progressive uncertainty reduction from the upstream to downstream was found for the soluble fraction of SMX in the RWB.
Parameter estimation of component reliability models in PSA model of Krsko NPP

International Nuclear Information System (INIS)

Jordan Cizelj, R.; Vrbanic, I.

2001-01-01

In the paper, the uncertainty analysis of component reliability models for independent failures is shown. The present approach for parameter estimation of component reliability models in NPP Krsko is presented. Mathematical approaches for different types of uncertainty analyses are introduced and used in accordance with some predisposed requirements. Results of the uncertainty analyses are shown in an example for time-related components. As the most appropriate uncertainty analysis proved the Bayesian estimation with the numerical estimation of a posterior, which can be approximated with some appropriate probability distribution, in this paper with lognormal distribution.(author)
Qualitative uncertainty analysis in probabilistic safety assessment context

International Nuclear Information System (INIS)

Apostol, M.; Constantin, M; Turcu, I.

2007-01-01

In Probabilistic Safety Assessment (PSA) context, an uncertainty analysis is performed either to estimate the uncertainty in the final results (the risk to public health and safety) or to estimate the uncertainty in some intermediate quantities (the core damage frequency, the radionuclide release frequency or fatality frequency). The identification and evaluation of uncertainty are important tasks because they afford credit to the results and help in the decision-making process. Uncertainty analysis can be performed qualitatively or quantitatively. This paper performs a preliminary qualitative uncertainty analysis, by identification of major uncertainty in PSA level 1- level 2 interface and in the other two major procedural steps of a level 2 PSA i.e. the analysis of accident progression and of the containment and analysis of source term for severe accidents. One should mention that a level 2 PSA for a Nuclear Power Plant (NPP) involves the evaluation and quantification of the mechanisms, amount and probabilities of subsequent radioactive material releases from the containment. According to NUREG 1150, an important task in source term analysis is fission products transport analysis. The uncertainties related to the isotopes distribution in CANDU NPP primary circuit and isotopes' masses transferred in the containment, using SOPHAEROS module from ASTEC computer code will be also presented. (authors)
Adjoint sensitivity analysis of dynamic reliability models based on Markov chains - II: Application to IFMIF reliability assessment

Energy Technology Data Exchange (ETDEWEB)

Cacuci, D. G. [Commiss Energy Atom, Direct Energy Nucl, Saclay, (France); Cacuci, D. G.; Balan, I. [Univ Karlsruhe, Inst Nucl Technol and Reactor Safetly, Karlsruhe, (Germany); Ionescu-Bujor, M. [Forschungszentrum Karlsruhe, Fus Program, D-76021 Karlsruhe, (Germany)

2008-07-01

In Part II of this work, the adjoint sensitivity analysis procedure developed in Part I is applied to perform sensitivity analysis of several dynamic reliability models of systems of increasing complexity, culminating with the consideration of the International Fusion Materials Irradiation Facility (IFMIF) accelerator system. Section II presents the main steps of a procedure for the automated generation of Markov chains for reliability analysis, including the abstraction of the physical system, construction of the Markov chain, and the generation and solution of the ensuing set of differential equations; all of these steps have been implemented in a stand-alone computer code system called QUEFT/MARKOMAG-S/MCADJSEN. This code system has been applied to sensitivity analysis of dynamic reliability measures for a paradigm '2-out-of-3' system comprising five components and also to a comprehensive dynamic reliability analysis of the IFMIF accelerator system facilities for the average availability and, respectively, the system's availability at the final mission time. The QUEFT/MARKOMAG-S/MCADJSEN has been used to efficiently compute sensitivities to 186 failure and repair rates characterizing components and subsystems of the first-level fault tree of the IFMIF accelerator system. (authors)
Adjoint sensitivity analysis of dynamic reliability models based on Markov chains - II: Application to IFMIF reliability assessment

International Nuclear Information System (INIS)

Cacuci, D. G.; Cacuci, D. G.; Balan, I.; Ionescu-Bujor, M.

2008-01-01

In Part II of this work, the adjoint sensitivity analysis procedure developed in Part I is applied to perform sensitivity analysis of several dynamic reliability models of systems of increasing complexity, culminating with the consideration of the International Fusion Materials Irradiation Facility (IFMIF) accelerator system. Section II presents the main steps of a procedure for the automated generation of Markov chains for reliability analysis, including the abstraction of the physical system, construction of the Markov chain, and the generation and solution of the ensuing set of differential equations; all of these steps have been implemented in a stand-alone computer code system called QUEFT/MARKOMAG-S/MCADJSEN. This code system has been applied to sensitivity analysis of dynamic reliability measures for a paradigm '2-out-of-3' system comprising five components and also to a comprehensive dynamic reliability analysis of the IFMIF accelerator system facilities for the average availability and, respectively, the system's availability at the final mission time. The QUEFT/MARKOMAG-S/MCADJSEN has been used to efficiently compute sensitivities to 186 failure and repair rates characterizing components and subsystems of the first-level fault tree of the IFMIF accelerator system. (authors)
Reliability analysis of digital I and C systems at KAERI

International Nuclear Information System (INIS)

Kim, Man Cheol

2013-01-01

This paper provides an overview of the ongoing research activities on a reliability analysis of digital instrumentation and control (I and C) systems of nuclear power plants (NPPs) performed by the Korea Atomic Energy Research Institute (KAERI). The research activities include the development of a new safety-critical software reliability analysis method by integrating the advantages of existing software reliability analysis methods, a fault coverage estimation method based on fault injection experiments, and a new human reliability analysis method for computer-based main control rooms (MCRs) based on human performance data from the APR-1400 full-scope simulator. The research results are expected to be used to address various issues such as the licensing issues related to digital I and C probabilistic safety assessment (PSA) for advanced digital-based NPPs. (author)
Reliable predictions of waste performance in a geologic repository

International Nuclear Information System (INIS)

Pigford, T.H.; Chambre, P.L.

1985-08-01

Establishing reliable estimates of long-term performance of a waste repository requires emphasis upon valid theories to predict performance. Predicting rates that radionuclides are released from waste packages cannot rest upon empirical extrapolations of laboratory leach data. Reliable predictions can be based on simple bounding theoretical models, such as solubility-limited bulk-flow, if the assumed parameters are reliably known or defensibly conservative. Wherever possible, performance analysis should proceed beyond simple bounding calculations to obtain more realistic - and usually more favorable - estimates of expected performance. Desire for greater realism must be balanced against increasing uncertainties in prediction and loss of reliability. Theoretical predictions of release rate based on mass-transfer analysis are bounding and the theory can be verified. Postulated repository analogues to simulate laboratory leach experiments introduce arbitrary and fictitious repository parameters and are shown not to agree with well-established theory. 34 refs., 3 figs., 2 tabs

Power electronics reliability analysis.

Energy Technology Data Exchange (ETDEWEB)

Smith, Mark A.; Atcitty, Stanley

2009-12-01

This report provides the DOE and industry with a general process for analyzing power electronics reliability. The analysis can help with understanding the main causes of failures, downtime, and cost and how to reduce them. One approach is to collect field maintenance data and use it directly to calculate reliability metrics related to each cause. Another approach is to model the functional structure of the equipment using a fault tree to derive system reliability from component reliability. Analysis of a fictitious device demonstrates the latter process. Optimization can use the resulting baseline model to decide how to improve reliability and/or lower costs. It is recommended that both electric utilities and equipment manufacturers make provisions to collect and share data in order to lay the groundwork for improving reliability into the future. Reliability analysis helps guide reliability improvements in hardware and software technology including condition monitoring and prognostics and health management.
Structural Reliability of Wind Turbine Blades

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov

turbine blades. The main purpose is to draw a clear picture of how reliability-based design of wind turbines can be done in practice. The objectives of the thesis are to create methodologies for efficient reliability assessment of composite materials and composite wind turbine blades, and to map...... the uncertainties in the processes, materials and external conditions that have an effect on the health of a composite structure. The study considers all stages in a reliability analysis, from defining models of structural components to obtaining the reliability index and calibration of partial safety factors...... by developing new models and standards or carrying out tests The following aspects are covered in detail: ⋅ The probabilistic aspects of ultimate strength of composite laminates are addressed. Laminated plates are considered as a general structural reliability system where each layer in a laminate is a separate...
A framework for reliability and risk centered maintenance

International Nuclear Information System (INIS)

Selvik, J.T.; Aven, T.

2011-01-01

Reliability centered maintenance (RCM) is a well-established analysis method for preventive maintenance planning. As its name indicates, reliability is the main point of reference for the planning, but consequences of failures are also assessed. However, uncertainties and risk are to a limited extent addressed by the RCM method, and in this paper we suggest an extension of the RCM to reliability and risk centered maintenance (RRCM) by also considering risk as the reference for the analysis in addition to reliability. A broad perspective on risk is adopted where uncertainties are the main component of risk in addition to possible events and associated consequences. A case from the offshore oil and gas industry is presented to illustrate and discuss the suggested approach.
Impact of nuclear data uncertainties on neutronics parameters of MYRRHA/XT-ADS

International Nuclear Information System (INIS)

Sugawara, T.; Stankovskiy, A.; Van den Eynde, G.; Sarotto, M.

2011-01-01

A flexible fast spectrum research reactor MYRRHA able to operate in subcritical (driven by a proton accelerator) and critical modes is being developed in SCK-CEN. In the framework of IP EUROTRANS programme the XT-ADS model has been investigated for MYRRHA. This paper reports the comparison of the sensitivity coefficients calculated for different calculation models and the uncertainties deduced from various covariance data for the discussion on the reliability of XT-ADS neutronics design. Sensitivity analysis is based on the comparison of three-dimensional heterogeneous and two-dimensional RZ calculation models. Three covariance data sets were employed to perform uncertainty analysis. The obtained sensitivity coefficients differ substantially between the 3D heterogeneous and RZ homogenized calculation models. The uncertainties deduced from the covariance data strongly depend on the covariance data variation. The covariance data of the nuclear data libraries is an open issue to discuss the reliability of the neutronics design. The uncertainties deduced from the covariance data for XT-ADS are 0.94% and 1.9% by the SCALE-6 44-group and TENDL-2009 covariance data, accordingly. The uncertainties exceed the 0.3% Δk (confidence level 1σ) target accuracy level. To achieve this target accuracy, the uncertainties should be improved by experiments under adequate conditions such as LBE or Pb moderated environment with MOX or Uranium fuel
Development of web-based reliability data analysis algorithm model and its application

International Nuclear Information System (INIS)

Hwang, Seok-Won; Oh, Ji-Yong; Moosung-Jae

2010-01-01

For this study, a database model of plant reliability was developed for the effective acquisition and management of plant-specific data that can be used in various applications of plant programs as well as in Probabilistic Safety Assessment (PSA). Through the development of a web-based reliability data analysis algorithm, this approach systematically gathers specific plant data such as component failure history, maintenance history, and shift diary. First, for the application of the developed algorithm, this study reestablished the raw data types, data deposition procedures and features of the Enterprise Resource Planning (ERP) system process. The component codes and system codes were standardized to make statistical analysis between different types of plants possible. This standardization contributes to the establishment of a flexible database model that allows the customization of reliability data for the various applications depending on component types and systems. In addition, this approach makes it possible for users to perform trend analyses and data comparisons for the significant plant components and systems. The validation of the algorithm is performed through a comparison of the importance measure value (Fussel-Vesely) of the mathematical calculation and that of the algorithm application. The development of a reliability database algorithm is one of the best approaches for providing systemic management of plant-specific reliability data with transparency and continuity. This proposed algorithm reinforces the relationships between raw data and application results so that it can provide a comprehensive database that offers everything from basic plant-related data to final customized data.
Development of web-based reliability data analysis algorithm model and its application

Energy Technology Data Exchange (ETDEWEB)

Hwang, Seok-Won, E-mail: swhwang@khnp.co.k [Korea Hydro and Nuclear Power Co. Ltd., Jang-Dong 25-1, Yuseong-Gu, 305-343 Daejeon (Korea, Republic of); Oh, Ji-Yong [Korea Hydro and Nuclear Power Co. Ltd., Jang-Dong 25-1, Yuseong-Gu, 305-343 Daejeon (Korea, Republic of); Moosung-Jae [Department of Nuclear Engineering Hanyang University 17 Haengdang, Sungdong, Seoul (Korea, Republic of)

2010-02-15

For this study, a database model of plant reliability was developed for the effective acquisition and management of plant-specific data that can be used in various applications of plant programs as well as in Probabilistic Safety Assessment (PSA). Through the development of a web-based reliability data analysis algorithm, this approach systematically gathers specific plant data such as component failure history, maintenance history, and shift diary. First, for the application of the developed algorithm, this study reestablished the raw data types, data deposition procedures and features of the Enterprise Resource Planning (ERP) system process. The component codes and system codes were standardized to make statistical analysis between different types of plants possible. This standardization contributes to the establishment of a flexible database model that allows the customization of reliability data for the various applications depending on component types and systems. In addition, this approach makes it possible for users to perform trend analyses and data comparisons for the significant plant components and systems. The validation of the algorithm is performed through a comparison of the importance measure value (Fussel-Vesely) of the mathematical calculation and that of the algorithm application. The development of a reliability database algorithm is one of the best approaches for providing systemic management of plant-specific reliability data with transparency and continuity. This proposed algorithm reinforces the relationships between raw data and application results so that it can provide a comprehensive database that offers everything from basic plant-related data to final customized data.
Evidence-based quantification of uncertainties induced via simulation-based modeling

International Nuclear Information System (INIS)

Riley, Matthew E.

2015-01-01

The quantification of uncertainties in simulation-based modeling traditionally focuses upon quantifying uncertainties in the parameters input into the model, referred to as parametric uncertainties. Often neglected in such an approach are the uncertainties induced by the modeling process itself. This deficiency is often due to a lack of information regarding the problem or the models considered, which could theoretically be reduced through the introduction of additional data. Because of the nature of this epistemic uncertainty, traditional probabilistic frameworks utilized for the quantification of uncertainties are not necessarily applicable to quantify the uncertainties induced in the modeling process itself. This work develops and utilizes a methodology – incorporating aspects of Dempster–Shafer Theory and Bayesian model averaging – to quantify uncertainties of all forms for simulation-based modeling problems. The approach expands upon classical parametric uncertainty approaches, allowing for the quantification of modeling-induced uncertainties as well, ultimately providing bounds on classical probability without the loss of epistemic generality. The approach is demonstrated on two different simulation-based modeling problems: the computation of the natural frequency of a simple two degree of freedom non-linear spring mass system and the calculation of the flutter velocity coefficient for the AGARD 445.6 wing given a subset of commercially available modeling choices. - Highlights: • Modeling-induced uncertainties are often mishandled or ignored in the literature. • Modeling-induced uncertainties are epistemic in nature. • Probabilistic representations of modeling-induced uncertainties are restrictive. • Evidence theory and Bayesian model averaging are integrated. • Developed approach is applicable for simulation-based modeling problems
Kuhn-Tucker optimization based reliability analysis for probabilistic finite elements

Science.gov (United States)

Liu, W. K.; Besterfield, G.; Lawrence, M.; Belytschko, T.

1988-01-01

The fusion of probability finite element method (PFEM) and reliability analysis for fracture mechanics is considered. Reliability analysis with specific application to fracture mechanics is presented, and computational procedures are discussed. Explicit expressions for the optimization procedure with regard to fracture mechanics are given. The results show the PFEM is a very powerful tool in determining the second-moment statistics. The method can determine the probability of failure or fracture subject to randomness in load, material properties and crack length, orientation, and location.
Reliability analysis of grid connected small wind turbine power electronics

International Nuclear Information System (INIS)

Arifujjaman, Md.; Iqbal, M.T.; Quaicoe, J.E.

2009-01-01

Grid connection of small permanent magnet generator (PMG) based wind turbines requires a power conditioning system comprising a bridge rectifier, a dc-dc converter and a grid-tie inverter. This work presents a reliability analysis and an identification of the least reliable component of the power conditioning system of such grid connection arrangements. Reliability of the configuration is analyzed for the worst case scenario of maximum conversion losses at a particular wind speed. The analysis reveals that the reliability of the power conditioning system of such PMG based wind turbines is fairly low and it reduces to 84% of initial value within one year. The investigation is further enhanced by identifying the least reliable component within the power conditioning system and found that the inverter has the dominant effect on the system reliability, while the dc-dc converter has the least significant effect. The reliability analysis demonstrates that a permanent magnet generator based wind energy conversion system is not the best option from the point of view of power conditioning system reliability. The analysis also reveals that new research is required to determine a robust power electronics configuration for small wind turbine conversion systems.
Development of the integrated system reliability analysis code MODULE

International Nuclear Information System (INIS)

Han, S.H.; Yoo, K.J.; Kim, T.W.

1987-01-01

The major components in a system reliability analysis are the determination of cut sets, importance measure, and uncertainty analysis. Various computer codes have been used for these purposes. For example, SETS and FTAP are used to determine cut sets; Importance for importance calculations; and Sample, CONINT, and MOCUP for uncertainty analysis. There have been problems when the codes run each other and the input and output are not linked, which could result in errors when preparing input for each code. The code MODULE was developed to carry out the above calculations simultaneously without linking input and outputs to other codes. MODULE can also prepare input for SETS for the case of a large fault tree that cannot be handled by MODULE. The flow diagram of the MODULE code is shown. To verify the MODULE code, two examples are selected and the results and computation times are compared with those of SETS, FTAP, CONINT, and MOCUP on both Cyber 170-875 and IBM PC/AT. Two examples are fault trees of the auxiliary feedwater system (AFWS) of Korea Nuclear Units (KNU)-1 and -2, which have 54 gates and 115 events, 39 gates and 92 events, respectively. The MODULE code has the advantage that it can calculate the cut sets, importances, and uncertainties in a single run with little increase in computing time over other codes and that it can be used in personal computers
DAKOTA, a multilevel parellel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 uers's manual.

Energy Technology Data Exchange (ETDEWEB)

Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandai National Labs, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandai National Labs, Livermore, CA); Giunta, Anthony Andrew; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandai National Labs, Livermore, CA); Hough, Patricia Diane (Sandai National Labs, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Brown, Shannon L.

2006-10-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual.

Energy Technology Data Exchange (ETDEWEB)

Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.

2010-05-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Reliability analysis based on a novel density estimation method for structures with correlations

Directory of Open Access Journals (Sweden)

Baoyu LI

2017-06-01

Full Text Available Estimating the Probability Density Function (PDF of the performance function is a direct way for structural reliability analysis, and the failure probability can be easily obtained by integration in the failure domain. However, efficiently estimating the PDF is still an urgent problem to be solved. The existing fractional moment based maximum entropy has provided a very advanced method for the PDF estimation, whereas the main shortcoming is that it limits the application of the reliability analysis method only to structures with independent inputs. While in fact, structures with correlated inputs always exist in engineering, thus this paper improves the maximum entropy method, and applies the Unscented Transformation (UT technique to compute the fractional moments of the performance function for structures with correlations, which is a very efficient moment estimation method for models with any inputs. The proposed method can precisely estimate the probability distributions of performance functions for structures with correlations. Besides, the number of function evaluations of the proposed method in reliability analysis, which is determined by UT, is really small. Several examples are employed to illustrate the accuracy and advantages of the proposed method.
Uncertainty Evaluation of the SFR Subchannel Thermal-Hydraulic Modeling Using a Hot Channel Factors Analysis

International Nuclear Information System (INIS)

Choi, Sun Rock; Cho, Chung Ho; Kim, Sang Ji

2011-01-01

In an SFR core analysis, a hot channel factors (HCF) method is most commonly used to evaluate uncertainty. It was employed to the early design such as the CRBRP and IFR. In other ways, the improved thermal design procedure (ITDP) is able to calculate the overall uncertainty based on the Root Sum Square technique and sensitivity analyses of each design parameters. The Monte Carlo method (MCM) is also employed to estimate the uncertainties. In this method, all the input uncertainties are randomly sampled according to their probability density functions and the resulting distribution for the output quantity is analyzed. Since an uncertainty analysis is basically calculated from the temperature distribution in a subassembly, the core thermal-hydraulic modeling greatly affects the resulting uncertainty. At KAERI, the SLTHEN and MATRA-LMR codes have been utilized to analyze the SFR core thermal-hydraulics. The SLTHEN (steady-state LMR core thermal hydraulics analysis code based on the ENERGY model) code is a modified version of the SUPERENERGY2 code, which conducts a multi-assembly, steady state calculation based on a simplified ENERGY model. The detailed subchannel analysis code MATRA-LMR (Multichannel Analyzer for Steady-State and Transients in Rod Arrays for Liquid Metal Reactors), an LMR version of MATRA, was also developed specifically for the SFR core thermal-hydraulic analysis. This paper describes comparative studies for core thermal-hydraulic models. The subchannel analysis and a hot channel factors based uncertainty evaluation system is established to estimate the core thermofluidic uncertainties using the MATRA-LMR code and the results are compared to those of the SLTHEN code
Probabilistic risk assessment for a loss of coolant accident in McMaster Nuclear Reactor and application of reliability physics model for modeling human reliability

Science.gov (United States)

Ha, Taesung

A probabilistic risk assessment (PRA) was conducted for a loss of coolant accident, (LOCA) in the McMaster Nuclear Reactor (MNR). A level 1 PRA was completed including event sequence modeling, system modeling, and quantification. To support the quantification of the accident sequence identified, data analysis using the Bayesian method and human reliability analysis (HRA) using the accident sequence evaluation procedure (ASEP) approach were performed. Since human performance in research reactors is significantly different from that in power reactors, a time-oriented HRA model (reliability physics model) was applied for the human error probability (HEP) estimation of the core relocation. This model is based on two competing random variables: phenomenological time and performance time. The response surface and direct Monte Carlo simulation with Latin Hypercube sampling were applied for estimating the phenomenological time, whereas the performance time was obtained from interviews with operators. An appropriate probability distribution for the phenomenological time was assigned by statistical goodness-of-fit tests. The human error probability (HEP) for the core relocation was estimated from these two competing quantities: phenomenological time and operators' performance time. The sensitivity of each probability distribution in human reliability estimation was investigated. In order to quantify the uncertainty in the predicted HEPs, a Bayesian approach was selected due to its capability of incorporating uncertainties in model itself and the parameters in that model. The HEP from the current time-oriented model was compared with that from the ASEP approach. Both results were used to evaluate the sensitivity of alternative huinan reliability modeling for the manual core relocation in the LOCA risk model. This exercise demonstrated the applicability of a reliability physics model supplemented with a. Bayesian approach for modeling human reliability and its potential
Reliability analysis for power supply system in a reprocessing facility based on GO methodology

International Nuclear Information System (INIS)

Wang Renze

2014-01-01

GO methodology was applied to analyze the reliability of power supply system in a typical reprocessing facility. Based on the fact that tie breakers are set in the system, tie breaker operator was defined. Then GO methodology modeling and quantitative analysis were performed sequently, minimal cut sets and average unavailability of the system were obtained. Parallel analysis between GO methodology and fault tree methodology was also performed. The results showed that setup of tie breakers was rational and necessary and that the modeling was much easier and the chart was much more succinct for GO methodology parallel with fault tree methodology to analyze the reliability of the power supply system. (author)
Assessing the reliability of dose coefficients for exposure to radioiodine by members of the public, accounting for dosimetric and risk model uncertainties.

Science.gov (United States)

Puncher, M; Zhang, W; Harrison, J D; Wakeford, R

2017-06-26

Assessments of risk to a specific population group resulting from internal exposure to a particular radionuclide can be used to assess the reliability of the appropriate International Commission on Radiological Protection (ICRP) dose coefficients used as a radiation protection device for the specified exposure pathway. An estimate of the uncertainty on the associated risk is important for informing judgments on reliability; a derived uncertainty factor, UF, is an estimate of the 95% probable geometric difference between the best risk estimate and the nominal risk and is a useful tool for making this assessment. This paper describes the application of parameter uncertainty analysis to quantify uncertainties resulting from internal exposures to radioiodine by members of the public, specifically 1, 10 and 20-year old females from the population of England and Wales. Best estimates of thyroid cancer incidence risk (lifetime attributable risk) are calculated for ingestion or inhalation of 129 I and 131 I, accounting for uncertainties in biokinetic model and cancer risk model parameter values. These estimates are compared with the equivalent ICRP derived nominal age-, sex- and population-averaged estimates of excess thyroid cancer incidence to obtain UFs. Derived UF values for ingestion or inhalation of 131 I for 1 year, 10-year and 20-year olds are around 28, 12 and 6, respectively, when compared with ICRP Publication 103 nominal values, and 9, 7 and 14, respectively, when compared with ICRP Publication 60 values. Broadly similar results were obtained for 129 I. The uncertainties on risk estimates are largely determined by uncertainties on risk model parameters rather than uncertainties on biokinetic model parameters. An examination of the sensitivity of the results to the risk models and populations used in the calculations show variations in the central estimates of risk of a factor of around 2-3. It is assumed that the direct proportionality of excess thyroid cancer
Integrated uncertainty analysis using RELAP/SCDAPSIM/MOD4.0

International Nuclear Information System (INIS)

Perez, M.; Reventos, F.; Wagner, R.; Allison, C.

2009-01-01

The RELAP/SCDAPSIM/MOD4.0 code, designed to predict the behavior of reactor systems during normal and accident conditions, is being developed as part of an international nuclear technology Software Development and Training Program (SDTP). RELAP/SCDAPSIM/MOD4.0, which is the first version of RELAP5 completely rewritten to FORTRAN 90/95/2000 standards, uses the publicly available RELAP5 and SCDAP models in combination with (a) advanced programming and numerical techniques, (b) advanced SDTP-member-developed models for LWR, HWR, and research reactor analysis, and (c) a variety of other member-developed computational packages. One such computational package is an integrated uncertainty analysis package being developed jointly by the Technical University of Catalunya (UPC) and Innovative Systems Software (ISS). The integrated uncertainty analysis approach used in the package uses the following steps: 1. Selection of the plant; 2. Selection of the scenario; 3. Selection of the safety criteria; 4. Identification and ranking of the relevant phenomena based on the safety criteria; 5. Selection of the appropriate code parameters to represent those phenomena; 6. Association of uncertainty by means of Probability Distribution Functions (PDFs) for each selected parameter; 7. Random sampling of the selected parameters according to its PDF and performing multiple computer runs to obtain uncertainty bands with a certain percentile and confidence level; 8. Processing the results of the multiple computer runs to estimate the uncertainty bands for the computed quantities associated with the selected safety criteria. The first four steps are performed by the user prior to the RELAP/SCDAPSIM/MOD4.0 analysis. The remaining steps are included with the MOD4.0 integrated uncertainty analysis (IUA) package. This paper briefly describes the integrated uncertainty analysis package including (a) the features of the package, (b) the implementation of the package into RELAP/SCDAPSIM/MOD4.0, and
The reliability of structural systems operating at high temperature: Replacing engineering judgement with operational experience

International Nuclear Information System (INIS)

Chevalier, M.J.; Smith, D.J.; Dean, D.W.

2012-01-01

Deterministic assessments are used to assess the integrity of structural systems operating at high temperature by providing a lower bound lifetime prediction, requiring considerable engineering judgement. However such a result may not satisfy the structural integrity assessment purpose if the results are overly conservative or conversely plant observations (such as failures) could undermine the assessment result if observed before the lower bound lifetime. This paper develops a reliability methodology for high temperature assessments and illustrates the impact and importance of managing the uncertainties within such an analysis. This is done by separating uncertainties into three classifications; aleatory uncertainty, quantifiable epistemic uncertainty and unquantifiable epistemic uncertainty. The result is a reliability model that can predict the behaviour of a structural system based upon plant observations, including failure and survival data. This can be used to reduce the over reliance upon engineering judgement which is prevalent in deterministic assessments. Highlights: ► Deterministic assessments are shown to be heavily reliant upon engineering judgment. ► Based upon the R5 procedure, a reliability model for a structural system is developed. ► Variables must be classified as either aleatory or epistemic to model their impact on reliability. ► Operation experience is then used to reduce reliance upon engineering judgment. ► This results in a model which can predict system behaviour and learn from operational experience.
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 developers manual.

Energy Technology Data Exchange (ETDEWEB)

Griffin, Joshua D. (Sandia National lababoratory, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandia National lababoratory, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandia National lababoratory, Livermore, CA); Giunta, Anthony Andrew; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandia National lababoratory, Livermore, CA); Hough, Patricia Diane (Sandia National lababoratory, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Brown, Shannon L.

2006-10-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.

DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, developers manual.

Energy Technology Data Exchange (ETDEWEB)

Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.

2010-05-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
Reliability-Based Calibration of Load Duration Factors for Timber Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Svensson, Staffan; Stang, Birgitte Friis Dela

2005-01-01

John Dalsgaard Sørensen, Staffan Svensson, Birgitte Dela Stang : Reliability-Based Calibration of Load Duration Factors for Timber Structures Abstract : The load bearing capacity of timber structures decrease with time depending on the type of load and timber. Based on representative limit...... states and stochastic models for timber structures, load duration factors are calibrated using probabilistic methods. Load duration e.ects are estimated on basis of simulation of realizations of wind, snow and imposed loads in accordance with the load models in the Danish structural codes. Three damage...... accumulation models are considered, namely Gerhards model, Barrett and Foschi _ s model and Foschi and Yao _ s model. The parameters in these models are .tted by the Maximum Likelihood Method using data relevant for Danish structural timber and the statistical uncertainty is quanti .ed. The reliability...
Using finite mixture models in thermal-hydraulics system code uncertainty analysis

Energy Technology Data Exchange (ETDEWEB)

Carlos, S., E-mail: scarlos@iqn.upv.es [Department d’Enginyeria Química i Nuclear, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain); Sánchez, A. [Department d’Estadística Aplicada i Qualitat, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain); Ginestar, D. [Department de Matemàtica Aplicada, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain); Martorell, S. [Department d’Enginyeria Química i Nuclear, Universitat Politècnica de València, Camí de Vera s.n, 46022 València (Spain)

2013-09-15

Highlights: • Best estimate codes simulation needs uncertainty quantification. • The output variables can present multimodal probability distributions. • The analysis of multimodal distribution is performed using finite mixture models. • Two methods to reconstruct output variable probability distribution are used. -- Abstract: Nuclear Power Plant safety analysis is mainly based on the use of best estimate (BE) codes that predict the plant behavior under normal or accidental conditions. As the BE codes introduce uncertainties due to uncertainty in input parameters and modeling, it is necessary to perform uncertainty assessment (UA), and eventually sensitivity analysis (SA), of the results obtained. These analyses are part of the appropriate treatment of uncertainties imposed by current regulation based on the adoption of the best estimate plus uncertainty (BEPU) approach. The most popular approach for uncertainty assessment, based on Wilks’ method, obtains a tolerance/confidence interval, but it does not completely characterize the output variable behavior, which is required for an extended UA and SA. However, the development of standard UA and SA impose high computational cost due to the large number of simulations needed. In order to obtain more information about the output variable and, at the same time, to keep computational cost as low as possible, there has been a recent shift toward developing metamodels (model of model), or surrogate models, that approximate or emulate complex computer codes. In this way, there exist different techniques to reconstruct the probability distribution using the information provided by a sample of values as, for example, the finite mixture models. In this paper, the Expectation Maximization and the k-means algorithms are used to obtain a finite mixture model that reconstructs the output variable probability distribution from data obtained with RELAP-5 simulations. Both methodologies have been applied to a separated
STARS software tool for analysis of reliability and safety

International Nuclear Information System (INIS)

Poucet, A.; Guagnini, E.

1989-01-01

This paper reports on the STARS (Software Tool for the Analysis of Reliability and Safety) project aims at developing an integrated set of Computer Aided Reliability Analysis tools for the various tasks involved in systems safety and reliability analysis including hazard identification, qualitative analysis, logic model construction and evaluation. The expert system technology offers the most promising perspective for developing a Computer Aided Reliability Analysis tool. Combined with graphics and analysis capabilities, it can provide a natural engineering oriented environment for computer assisted reliability and safety modelling and analysis. For hazard identification and fault tree construction, a frame/rule based expert system is used, in which the deductive (goal driven) reasoning and the heuristic, applied during manual fault tree construction, is modelled. Expert system can explain their reasoning so that the analyst can become aware of the why and the how results are being obtained. Hence, the learning aspect involved in manual reliability and safety analysis can be maintained and improved
Reliability Based Optimization of Structural Systems

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

1987-01-01

The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...
Managing Information Uncertainty in Wave Height Modeling for the Offshore Structural Analysis through Random Set

Directory of Open Access Journals (Sweden)

Keqin Yan

2017-01-01

Full Text Available This chapter presents a reliability study for an offshore jacket structure with emphasis on the features of nonconventional modeling. Firstly, a random set model is formulated for modeling the random waves in an ocean site. Then, a jacket structure is investigated in a pushover analysis to identify the critical wave direction and key structural elements. This is based on the ultimate base shear strength. The selected probabilistic models are adopted for the important structural members and the wave direction is specified in the weakest direction of the structure for a conservative safety analysis. The wave height model is processed in a P-box format when it is used in the numerical analysis. The models are applied to find the bounds of the failure probabilities for the jacket structure. The propagation of this wave model to the uncertainty in results is investigated in both an interval analysis and Monte Carlo simulation. The results are compared in context of information content and numerical accuracy. Further, the failure probability bounds are compared with the conventional probabilistic approach.
DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's reference manual.

Energy Technology Data Exchange (ETDEWEB)

Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.

2010-05-01

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Uncertainty Analysis of Power Grid Investment Capacity Based on Monte Carlo

Science.gov (United States)

Qin, Junsong; Liu, Bingyi; Niu, Dongxiao

By analyzing the influence factors of the investment capacity of power grid, to depreciation cost, sales price and sales quantity, net profit, financing and GDP of the second industry as the dependent variable to build the investment capacity analysis model. After carrying out Kolmogorov-Smirnov test, get the probability distribution of each influence factor. Finally, obtained the grid investment capacity uncertainty of analysis results by Monte Carlo simulation.
Analysis and Reduction of Complex Networks Under Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Knio, Omar M

2014-04-09

This is a collaborative proposal that aims at developing new methods for the analysis and reduction of complex multiscale networks under uncertainty. The approach is based on combining methods of computational singular perturbation (CSP) and probabilistic uncertainty quantification. In deterministic settings, CSP yields asymptotic approximations of reduced-dimensionality “slow manifolds” on which a multiscale dynamical system evolves. Introducing uncertainty raises fundamentally new issues, particularly concerning its impact on the topology of slow manifolds, and means to represent and quantify associated variability. To address these challenges, this project uses polynomial chaos (PC) methods to reformulate uncertain network models, and to analyze them using CSP in probabilistic terms. Specific objectives include (1) developing effective algorithms that can be used to illuminate fundamental and unexplored connections among model reduction, multiscale behavior, and uncertainty, and (2) demonstrating the performance of these algorithms through applications to model problems.
Reliability Evaluation of Bridges Based on Nonprobabilistic Response Surface Limit Method

Directory of Open Access Journals (Sweden)

Xuyong Chen

2017-01-01

Full Text Available Due to many uncertainties in nonprobabilistic reliability assessment of bridges, the limit state function is generally unknown. The traditional nonprobabilistic response surface method is a lengthy and oscillating iteration process and leads to difficultly solving the nonprobabilistic reliability index. This article proposes a nonprobabilistic response surface limit method based on the interval model. The intention of this method is to solve the upper and lower limits of the nonprobabilistic reliability index and to narrow the range of the nonprobabilistic reliability index. If the range of the reliability index reduces to an acceptable accuracy, the solution will be considered convergent, and the nonprobabilistic reliability index will be obtained. The case study indicates that using the proposed method can avoid oscillating iteration process, make iteration process stable and convergent, reduce iteration steps significantly, and improve computational efficiency and precision significantly compared with the traditional nonprobabilistic response surface method. Finally, the nonprobabilistic reliability evaluation process of bridge will be built through evaluating the reliability of one PC continuous rigid frame bridge with three spans using the proposed method, which appears to be more simple and reliable when lack of samples and parameters in the bridge nonprobabilistic reliability evaluation is present.
Automated uncertainty analysis methods in the FRAP computer codes

International Nuclear Information System (INIS)

Peck, S.O.

1980-01-01

A user oriented, automated uncertainty analysis capability has been incorporated in the Fuel Rod Analysis Program (FRAP) computer codes. The FRAP codes have been developed for the analysis of Light Water Reactor fuel rod behavior during steady state (FRAPCON) and transient (FRAP-T) conditions as part of the United States Nuclear Regulatory Commission's Water Reactor Safety Research Program. The objective of uncertainty analysis of these codes is to obtain estimates of the uncertainty in computed outputs of the codes is to obtain estimates of the uncertainty in computed outputs of the codes as a function of known uncertainties in input variables. This paper presents the methods used to generate an uncertainty analysis of a large computer code, discusses the assumptions that are made, and shows techniques for testing them. An uncertainty analysis of FRAP-T calculated fuel rod behavior during a hypothetical loss-of-coolant transient is presented as an example and carried through the discussion to illustrate the various concepts
Representation of analysis results involving aleatory and epistemic uncertainty.

Energy Technology Data Exchange (ETDEWEB)

Johnson, Jay Dean (ProStat, Mesa, AZ); Helton, Jon Craig (Arizona State University, Tempe, AZ); Oberkampf, William Louis; Sallaberry, Cedric J.

2008-08-01

Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for the representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.
Importance of independent and dependent human error to system reliability and plant safety

International Nuclear Information System (INIS)

Dach, K.

1988-08-01

Uncertainty analysis of the quantification of the unavailability for the emergency core cooling system was made. The reliability analysis of the low pressure injection system (LPIS) of the ECCS of WWER-440 reactor was also performed. Results of reliability analysis proved that LPIS reliability under normal conditions is sufficient and can be increased by two orders of magnitude. This increase in reliability can be achieved by means of simple changes such as securing an opening of the quick-acting fittings at LPIS discharge line. A method for analysis of systems uncertainty with periodic inspected components was elaborated and verified by performing an analysis of the medium size system. Refs, figs and tabs
Reliability and Sensitivity Analysis for Laminated Composite Plate Using Response Surface Method

International Nuclear Information System (INIS)

Lee, Seokje; Kim, Ingul; Jang, Moonho; Kim, Jaeki; Moon, Jungwon

2013-01-01

Advanced fiber-reinforced laminated composites are widely used in various fields of engineering to reduce weight. The material property of each ply is well known; specifically, it is known that ply is less reliable than metallic materials and very sensitive to the loading direction. Therefore, it is important to consider this uncertainty in the design of laminated composites. In this study, reliability analysis is conducted using Callosum and Meatball interactions for a laminated composite plate for the case in which the tip deflection is the design requirement and the material property is a random variable. Furthermore, the efficiency and accuracy of the approximation method is identified, and a probabilistic sensitivity analysis is conducted. As a result, we can prove the applicability of the advanced design method for the stabilizer of an underwater vehicle
Reliability and Sensitivity Analysis for Laminated Composite Plate Using Response Surface Method

Energy Technology Data Exchange (ETDEWEB)

Lee, Seokje; Kim, Ingul [Chungnam National Univ., Daejeon (Korea, Republic of); Jang, Moonho; Kim, Jaeki; Moon, Jungwon [LIG Nex1, Yongin (Korea, Republic of)

2013-04-15

Advanced fiber-reinforced laminated composites are widely used in various fields of engineering to reduce weight. The material property of each ply is well known; specifically, it is known that ply is less reliable than metallic materials and very sensitive to the loading direction. Therefore, it is important to consider this uncertainty in the design of laminated composites. In this study, reliability analysis is conducted using Callosum and Meatball interactions for a laminated composite plate for the case in which the tip deflection is the design requirement and the material property is a random variable. Furthermore, the efficiency and accuracy of the approximation method is identified, and a probabilistic sensitivity analysis is conducted. As a result, we can prove the applicability of the advanced design method for the stabilizer of an underwater vehicle.
Measurement uncertainty analysis techniques applied to PV performance measurements

International Nuclear Information System (INIS)

Wells, C.

1992-10-01

The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results
Measurement uncertainty analysis techniques applied to PV performance measurements

Energy Technology Data Exchange (ETDEWEB)

Wells, C.

1992-10-01

The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Measurement uncertainty analysis techniques applied to PV performance measurements

Energy Technology Data Exchange (ETDEWEB)

Wells, C

1992-10-01

The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment`s final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Reliability-Based Optimization of Series Systems of Parallel Systems

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

1993-01-01

Reliability-based design of structural systems is considered. In particular, systems where the reliability model is a series system of parallel systems are treated. A sensitivity analysis for this class of problems is presented. Optimization problems with series systems of parallel systems...... optimization of series systems of parallel systems, but it is also efficient in reliability-based optimization of series systems in general....
Development of A Standard Method for Human Reliability Analysis of Nuclear Power Plants

International Nuclear Information System (INIS)

Jung, Won Dea; Kang, Dae Il; Kim, Jae Whan

2005-12-01

According as the demand of risk-informed regulation and applications increase, the quality and reliability of a probabilistic safety assessment (PSA) has been more important. KAERI started a study to standardize the process and the rules of HRA (Human Reliability Analysis) which was known as a major contributor to the uncertainty of PSA. The study made progress as follows; assessing the level of quality of the HRAs in Korea and identifying the weaknesses of the HRAs, determining the requirements for developing a standard HRA method, developing the process and rules for quantifying human error probability. Since the risk-informed applications use the ASME PSA standard to ensure PSA quality, the standard HRA method was developed to meet the ASME HRA requirements with level of category II. The standard method was based on THERP and ASEP HRA that are widely used for conventional HRA. However, the method focuses on standardizing and specifying the analysis process, quantification rules and criteria to minimize the deviation of the analysis results caused by different analysts. Several HRA experts from different organizations in Korea participated in developing the standard method. Several case studies were interactively undertaken to verify the usability and applicability of the standard method

Development of A Standard Method for Human Reliability Analysis of Nuclear Power Plants

Energy Technology Data Exchange (ETDEWEB)

Jung, Won Dea; Kang, Dae Il; Kim, Jae Whan

2005-12-15

According as the demand of risk-informed regulation and applications increase, the quality and reliability of a probabilistic safety assessment (PSA) has been more important. KAERI started a study to standardize the process and the rules of HRA (Human Reliability Analysis) which was known as a major contributor to the uncertainty of PSA. The study made progress as follows; assessing the level of quality of the HRAs in Korea and identifying the weaknesses of the HRAs, determining the requirements for developing a standard HRA method, developing the process and rules for quantifying human error probability. Since the risk-informed applications use the ASME PSA standard to ensure PSA quality, the standard HRA method was developed to meet the ASME HRA requirements with level of category II. The standard method was based on THERP and ASEP HRA that are widely used for conventional HRA. However, the method focuses on standardizing and specifying the analysis process, quantification rules and criteria to minimize the deviation of the analysis results caused by different analysts. Several HRA experts from different organizations in Korea participated in developing the standard method. Several case studies were interactively undertaken to verify the usability and applicability of the standard method.
Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report

International Nuclear Information System (INIS)

Harper, F.T.; Young, M.L.; Miller, L.A.; Hora, S.C.; Lui, C.H.; Goossens, L.H.J.; Cooke, R.M.; Paesler-Sauer, J.; Helton, J.C.

1995-01-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The ultimate objective of the joint effort was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. Experts developed their distributions independently. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. To validate the distributions generated for the dispersion code input variables, samples from the distributions and propagated through the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the first of a three-volume document describing the project
One Approach to the Fire PSA Uncertainty Analysis

International Nuclear Information System (INIS)

Simic, Z.; Mikulicic, V.; Vukovic, I.

2002-01-01

Experienced practical events and findings from the number of fire probabilistic safety assessment (PSA) studies show that fire has high relative importance for nuclear power plant safety. Fire PSA is a very challenging phenomenon and a number of issues are still in the area of research and development. This has a major impact on the conservatism of fire PSA findings. One way to reduce the level of conservatism is to conduct uncertainty analysis. At the top-level, uncertainty of the fire PSA can be separated in to three segments. The first segment is related to fire initiating events frequencies. The second uncertainty segment is connected to the uncertainty of fire damage. Finally, there is uncertainty related to the PSA model, which propagates this fire-initiated damage to the core damage or other analyzed risk. This paper discusses all three segments of uncertainty. Some recent experience with fire PSA study uncertainty analysis, usage of fire analysis code COMPBRN IIIe, and uncertainty evaluation importance to the final result is presented.(author)
A scenario-based modeling approach for emergency evacuation management and risk analysis under multiple uncertainties

Energy Technology Data Exchange (ETDEWEB)

Lv, Y., E-mail: lvyying@hotmail.com [School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 (China); Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Huang, G.H., E-mail: huang@iseis.org [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Guo, L., E-mail: guoli8658@hotmail.com [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada); Li, Y.P., E-mail: yongping.li@iseis.org [MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206 (China); Dai, C., E-mail: daichao321@gmail.com [College of Environmental Sciences and Engineering, Peking University, Beijing 100871 (China); Wang, X.W., E-mail: wangxingwei0812@gamil.com [State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875 (China); Sun, W., E-mail: sunwei@iseis.org [Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada)

2013-02-15

Highlights: ► An interval-parameter joint-probabilistic integer programming method is developed. ► It is useful for nuclear emergency management practices under uncertainties. ► It can schedule optimal routes with maximizing evacuees during a finite time. ► Scenario-based analysis enhances robustness in controlling system risk. ► The method will help to improve the capability of disaster responses. -- Abstract: Nuclear emergency evacuation is important to prevent radioactive harms by hazardous materials and to limit the accidents’ consequences; however, uncertainties are involved in the components and processes of such a management system. In the study, an interval-parameter joint-probabilistic integer programming (IJIP) method is developed for emergency evacuation management under uncertainties. Optimization techniques of interval-parameter programming (IPP) and joint-probabilistic constrained (JPC) programming are incorporated into an integer linear programming framework, so that the approach can deal with uncertainties expressed as joint probability and interval values. The IJIP method can schedule the optimal routes to guarantee the maximum population evacuated away from the effected zone during a finite time. Furthermore, it can also facilitate post optimization analysis to enhance robustness in controlling system violation risk imposed on the joint-probabilistic constraints. The developed method has been applied to a case study of nuclear emergency management; meanwhile, a number of scenarios under different system conditions have been analyzed. It is indicated that the solutions are useful for evacuation management practices. The result of the IJIP method can not only help to raise the capability of disaster responses in a systematic manner, but also provide an insight into complex relationships among evacuation planning, resources utilizations, policy requirements and system risks.
Uncertainty analysis

International Nuclear Information System (INIS)

Thomas, R.E.

1982-03-01

An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software
Uncertainty analysis of pollutant build-up modelling based on a Bayesian weighted least squares approach

International Nuclear Information System (INIS)

Haddad, Khaled; Egodawatta, Prasanna; Rahman, Ataur; Goonetilleke, Ashantha

2013-01-01

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling. - Highlights: ► Water quality data spans short time scales leading to significant model uncertainty. ► Assessment of uncertainty essential for informed decision making in water
Adjoint-Based Uncertainty Quantification with MCNP

Energy Technology Data Exchange (ETDEWEB)

Seifried, Jeffrey E. [Univ. of California, Berkeley, CA (United States)

2011-09-01

This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence in the simulation is acquired.
Conceptual and computational basis for the quantification of margins and uncertainty

International Nuclear Information System (INIS)

Helton, Jon Craig

2009-01-01

In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e, Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainty (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. This presentation discusses and illustrates the conceptual and computational basis of QMU in analyses that use computational models to predict the behavior of complex systems. Topics considered include (1) the role of aleatory and epistemic uncertainty in QMU, (2) the representation of uncertainty with probability, (3) the probabilistic representation of uncertainty in QMU analyses involving only epistemic uncertainty, (4) the probabilistic representation of uncertainty in QMU analyses involving aleatory and epistemic uncertainty, (5) procedures for sampling-based uncertainty and sensitivity analysis, (6) the representation of uncertainty with alternatives to probability such as interval analysis, possibility theory and evidence theory, (7) the representation of uncertainty with alternatives to probability in QMU analyses involving only epistemic uncertainty, and (8) the representation of uncertainty with alternatives to probability in QMU analyses involving aleatory and epistemic uncertainty. Concepts and computational procedures are illustrated with both notional examples and examples from reactor safety and radioactive waste disposal.
Space Mission Human Reliability Analysis (HRA) Project

Data.gov (United States)

National Aeronautics and Space Administration — The purpose of this project is to extend current ground-based Human Reliability Analysis (HRA) techniques to a long-duration, space-based tool to more effectively...
Assessing scenario and parametric uncertainties in risk analysis: a model uncertainty audit

International Nuclear Information System (INIS)

Tarantola, S.; Saltelli, A.; Draper, D.

1999-01-01

In the present study a process of model audit is addressed on a computational model used for predicting maximum radiological doses to humans in the field of nuclear waste disposal. Global uncertainty and sensitivity analyses are employed to assess output uncertainty and to quantify the contribution of parametric and scenario uncertainties to the model output. These tools are of fundamental importance for risk analysis and decision making purposes
Cut set-based risk and reliability analysis for arbitrarily interconnected networks

Science.gov (United States)

Wyss, Gregory D.

2000-01-01

Method for computing all-terminal reliability for arbitrarily interconnected networks such as the United States public switched telephone network. The method includes an efficient search algorithm to generate minimal cut sets for nonhierarchical networks directly from the network connectivity diagram. Efficiency of the search algorithm stems in part from its basis on only link failures. The method also includes a novel quantification scheme that likewise reduces computational effort associated with assessing network reliability based on traditional risk importance measures. Vast reductions in computational effort are realized since combinatorial expansion and subsequent Boolean reduction steps are eliminated through analysis of network segmentations using a technique of assuming node failures to occur on only one side of a break in the network, and repeating the technique for all minimal cut sets generated with the search algorithm. The method functions equally well for planar and non-planar networks.
Reliability-based sensitivity of mechanical components with arbitrary distribution parameters

International Nuclear Information System (INIS)

Zhang, Yi Min; Yang, Zhou; Wen, Bang Chun; He, Xiang Dong; Liu, Qiaoling

2010-01-01

This paper presents a reliability-based sensitivity method for mechanical components with arbitrary distribution parameters. Techniques from the perturbation method, the Edgeworth series, the reliability-based design theory, and the sensitivity analysis approach were employed directly to calculate the reliability-based sensitivity of mechanical components on the condition that the first four moments of the original random variables are known. The reliability-based sensitivity information of the mechanical components can be accurately and quickly obtained using a practical computer program. The effects of the design parameters on the reliability of mechanical components were studied. The method presented in this paper provides the theoretic basis for the reliability-based design of mechanical components
Reliability Evaluation for the Surface to Air Missile Weapon Based on Cloud Model

Directory of Open Access Journals (Sweden)

Deng Jianjun

2015-01-01

Full Text Available The fuzziness and randomness is integrated by using digital characteristics, such as Expected value, Entropy and Hyper entropy. The cloud model adapted to reliability evaluation is put forward based on the concept of the surface to air missile weapon. The cloud scale of the qualitative evaluation is constructed, and the quantitative variable and the qualitative variable in the system reliability evaluation are corresponded. The practical calculation result shows that it is more effective to analyze the reliability of the surface to air missile weapon by this way. The practical calculation result also reflects the model expressed by cloud theory is more consistent with the human thinking style of uncertainty.
An introductory guide to uncertainty analysis in environmental and health risk assessment

International Nuclear Information System (INIS)

Hoffman, F.O.; Hammonds, J.S.

1992-10-01

To compensate for the potential for overly conservative estimates of risk using standard US Environmental Protection Agency methods, an uncertainty analysis should be performed as an integral part of each risk assessment. Uncertainty analyses allow one to obtain quantitative results in the form of confidence intervals that will aid in decision making and will provide guidance for the acquisition of additional data. To perform an uncertainty analysis, one must frequently rely on subjective judgment in the absence of data to estimate the range and a probability distribution describing the extent of uncertainty about a true but unknown value for each parameter of interest. This information is formulated from professional judgment based on an extensive review of literature, analysis of the data, and interviews with experts. Various analytical and numerical techniques are available to allow statistical propagation of the uncertainty in the model parameters to a statement of uncertainty in the risk to a potentially exposed individual. Although analytical methods may be straightforward for relatively simple models, they rapidly become complicated for more involved risk assessments. Because of the tedious efforts required to mathematically derive analytical approaches to propagate uncertainty in complicated risk assessments, numerical methods such as Monte Carlo simulation should be employed. The primary objective of this report is to provide an introductory guide for performing uncertainty analysis in risk assessments being performed for Superfund sites
Uncertainty analysis of time-dependent nonlinear systems: theory and application to transient thermal hydraulics

International Nuclear Information System (INIS)

Barhen, J.; Bjerke, M.A.; Cacuci, D.G.; Mullins, C.B.; Wagschal, G.G.

1982-01-01

An advanced methodology for performing systematic uncertainty analysis of time-dependent nonlinear systems is presented. This methodology includes a capability for reducing uncertainties in system parameters and responses by using Bayesian inference techniques to consistently combine prior knowledge with additional experimental information. The determination of best estimates for the system parameters, for the responses, and for their respective covariances is treated as a time-dependent constrained minimization problem. Three alternative formalisms for solving this problem are developed. The two ''off-line'' formalisms, with and without ''foresight'' characteristics, require the generation of a complete sensitivity data base prior to performing the uncertainty analysis. The ''online'' formalism, in which uncertainty analysis is performed interactively with the system analysis code, is best suited for treatment of large-scale highly nonlinear time-dependent problems. This methodology is applied to the uncertainty analysis of a transient upflow of a high pressure water heat transfer experiment. For comparison, an uncertainty analysis using sensitivities computed by standard response surface techniques is also performed. The results of the analysis indicate the following. Major reduction of the discrepancies in the calculation/experiment ratios is achieved by using the new methodology. Incorporation of in-bundle measurements in the uncertainty analysis significantly reduces system uncertainties. Accuracy of sensitivities generated by response-surface techniques should be carefully assessed prior to using them as a basis for uncertainty analyses of transient reactor safety problems
Processing of the GALILEO fuel rod code model uncertainties within the AREVA LWR realistic thermal-mechanical analysis methodology

International Nuclear Information System (INIS)

Mailhe, P.; Barbier, B.; Garnier, C.; Landskron, H.; Sedlacek, R.; Arimescu, I.; Smith, M.; Bellanger, P.

2013-01-01

The availability of reliable tools and associated methodology able to accurately predict the LWR fuel behavior in all conditions is of great importance for safe and economic fuel usage. For that purpose, AREVA has developed its new global fuel rod performance code GALILEO along with its associated realistic thermal-mechanical analysis methodology. This realistic methodology is based on a Monte Carlo type random sampling of all relevant input variables. After having outlined the AREVA realistic methodology, this paper will be focused on the GALILEO code benchmarking process, on its extended experimental database and on the GALILEO model uncertainties assessment. The propagation of these model uncertainties through the AREVA realistic methodology is also presented. This GALILEO model uncertainties processing is of the utmost importance for accurate fuel design margin evaluation as illustrated on some application examples. With the submittal of Topical Report GALILEO to the U.S. NRC in 2013, GALILEO and its methodology are on the way to be industrially used in a wide range of irradiation conditions. (authors)
Reliability-based optimization of an active vibration controller using evolutionary algorithms

Science.gov (United States)

Saraygord Afshari, Sajad; Pourtakdoust, Seid H.

2017-04-01

Many modern industrialized systems such as aircrafts, rotating turbines, satellite booms, etc. cannot perform their desired tasks accurately if their uninhibited structural vibrations are not controlled properly. Structural health monitoring and online reliability calculations are emerging new means to handle system imposed uncertainties. As stochastic forcing are unavoidable, in most engineering systems, it is often needed to take them into the account for the control design process. In this research, smart material technology is utilized for structural health monitoring and control in order to keep the system in a reliable performance range. In this regard, a reliability-based cost function is assigned for both controller gain optimization as well as sensor placement. The proposed scheme is implemented and verified for a wing section. Comparison of results for the frequency responses is considered to show potential applicability of the presented technique.
Development of reliability and probabilistic safety assessment program RiskA

International Nuclear Information System (INIS)

Wu, Yican

2015-01-01

Highlights: • There are four parts in the structure of RiskA. User input part lets users input the PSA model and some necessary data by GUI or model transformation tool. In calculation engine part, fault tree analysis, event tree analysis, uncertainty analysis, sensitivity analysis, importance analysis and failure mode and effects analysis are supplied. User output part outputs the analysis results, user customized reports and some other data. The last part includes reliability database, some other common tools and help documents. • RiskA has several advanced features. Extensible framework makes it easy to add any new functions, making RiskA to be a large platform of reliability and probabilistic safety assessment. It is very fast to analysis fault tree in RiskA because many advanced algorithm improvement were made. Many model formats can be imported and exported, which made the PSA model in the commercial software can be easily transformed to adapt RiskA platform. Web-based co-modeling let several users in different places work together whenever they are online. • The comparison between RiskA and other mature PSA codes (e.g. CAFTA, RiskSpectrum, XFTA) has demonstrated that the calculation and analysis of RiskA is correct and efficient. Based on the development of this code package, many applications of safety and reliability analysis of some research reactors and nuclear power plants were performed. The development of RiskA appears to be of realistic and potential value for academic research and practical operation safety management of nuclear power plants in China and abroad. - Abstract: PSA (probabilistic safety assessment) software, the indispensable tool in nuclear safety assessment, has been widely used. An integrated reliability and PSA program named RiskA has been developed by FDS Team. RiskA supplies several standard PSA modules including fault tree analysis, event tree analysis, uncertainty analysis, failure mode and effect analysis and reliability
Design, Analysis and Test of Logic Circuits Under Uncertainty

CERN Document Server

Krishnaswamy, Smita; Hayes, John P

2013-01-01

Integrated circuits (ICs) increasingly exhibit uncertain characteristics due to soft errors, inherently probabilistic devices, and manufacturing variability. As device technologies scale, these effects can be detrimental to the reliability of logic circuits. To improve future semiconductor designs, this book describes methods for analyzing, designing, and testing circuits subject to probabilistic effects. The authors first develop techniques to model inherently probabilistic methods in logic circuits and to test circuits for determining their reliability after they are manufactured. Then, they study error-masking mechanisms intrinsic to digital circuits and show how to leverage them to design more reliable circuits. The book describes techniques for: • Modeling and reasoning about probabilistic behavior in logic circuits, including a matrix-based reliability-analysis framework; • Accurate analysis of soft-error rate (SER) based on functional-simulation, sufficiently scalable for use in gate-l...
Reliability analysis in interdependent smart grid systems

Science.gov (United States)

Peng, Hao; Kan, Zhe; Zhao, Dandan; Han, Jianmin; Lu, Jianfeng; Hu, Zhaolong

2018-06-01

Complex network theory is a useful way to study many real complex systems. In this paper, a reliability analysis model based on complex network theory is introduced in interdependent smart grid systems. In this paper, we focus on understanding the structure of smart grid systems and studying the underlying network model, their interactions, and relationships and how cascading failures occur in the interdependent smart grid systems. We propose a practical model for interdependent smart grid systems using complex theory. Besides, based on percolation theory, we also study the effect of cascading failures effect and reveal detailed mathematical analysis of failure propagation in such systems. We analyze the reliability of our proposed model caused by random attacks or failures by calculating the size of giant functioning components in interdependent smart grid systems. Our simulation results also show that there exists a threshold for the proportion of faulty nodes, beyond which the smart grid systems collapse. Also we determine the critical values for different system parameters. In this way, the reliability analysis model based on complex network theory can be effectively utilized for anti-attack and protection purposes in interdependent smart grid systems.

Uncertainty analysis for Ulysses safety evaluation report

International Nuclear Information System (INIS)

Frank, M.V.

1991-01-01

As part of the effort to review the Ulysses Final Safety Analysis Report and to understand the risk of plutonium release from the Ulysses spacecraft General Purpose Heat Source---Radioisotope Thermal Generator (GPHS-RTG), the Interagency Nuclear Safety Review Panel (INSRP) and the author performed an integrated, quantitative analysis of the uncertainties of the calculated risk of plutonium release from Ulysses. Using state-of-art probabilistic risk assessment technology, the uncertainty analysis accounted for both variability and uncertainty of the key parameters of the risk analysis. The results show that INSRP had high confidence that risk of fatal cancers from potential plutonium release associated with calculated launch and deployment accident scenarios is low
Uncertainty Analysis via Failure Domain Characterization: Polynomial Requirement Functions

Science.gov (United States)

Crespo, Luis G.; Munoz, Cesar A.; Narkawicz, Anthony J.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. A Bernstein expansion approach is used to size hyper-rectangular subsets while a sum of squares programming approach is used to size quasi-ellipsoidal subsets. These methods are applicable to requirement functions whose functional dependency on the uncertainty is a known polynomial. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the uncertainty model assumed (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.
Development of web-based reliability data base platform

International Nuclear Information System (INIS)

Hwang, Seok Won; Lee, Chang Ju; Sung, Key Yong

2004-01-01

Probabilistic safety assessment (PSA) is a systematic technique which estimates the degree of risk impacts to the public due to an accident scenario. Estimating the occurrence frequencies and consequences of potential scenarios requires a thorough analysis of the accident details and all fundamental parameters. The robustness of PSA to check weaknesses in a design and operation will allow a better informed and balanced decision to be reached. The fundamental parameters for PSA, such as the component failure rates, should be estimated under the condition of steady collection of the evidence throughout the operational period. However, since any single plant data does not sufficiently enough to provide an adequate PSA result, in actual, the whole operating data was commonly used to estimate the reliability parameters for the same type of components. The reliability data of any component type consists of two categories; the generic that is based on the operating experiences of whole plants, and the plant-specific that is based on the operation of a specific plant of interest. The generic data is highly essential for new or recently-built nuclear power plants (NPPs). Generally, the reliability data base may be categorized into the component reliability, initiating event frequencies, human performance, and so on. Among these data, the component reliability seems a key element because it has the most abundant population. Therefore, the component reliability data is essential for taking a part in the quantification of accident sequences because it becomes an input of various basic events which consists of the fault tree
Uncertainty analysis for geologic disposal of radioactive waste

International Nuclear Information System (INIS)

Cranwell, R.M.; Helton, J.C.

1981-01-01

The incorporation and representation of uncertainty in the analysis of the consequences and risks associated with the geologic disposal of high-level radioactive waste are discussed. Such uncertainty has three primary components: process modeling uncertainty, model input data uncertainty, and scenario uncertainty. The following topics are considered in connection with the preceding components: propagation of uncertainty in the modeling of a disposal site, sampling of input data for models, and uncertainty associated with model output
Erha Uncertainty Analysis: Planning for the future

International Nuclear Information System (INIS)

Brami, T.R.; Hopkins, D.F.; Loguer, W.L.; Cornagia, D.M.; Braisted, A.W.C.

2002-01-01

The Erha field (OPL 209) was discovered in 1999 approximately 100 km off the coast of Nigeria in 1,100 m of water. The discovery well (Erha-1) encountered oil and gas in deep-water clastic reservoirs. The first appraisal well (Erha-2) drilled 1.6 km downdip to the northwest penetrated an oil-water contact and confirmed a potentially commercial discovery. However, the Erha-3 and Erha-3 ST-1 boreholes, drilled on the faulted east-side of the field in 2001, encountered shallower fluid contacts. As a result of these findings, a comprehensive field-wide uncertainty analysis was performed to better understand what we know versus what we think regarding resource size and economic viability The uncertainty analysis process applied at Erha is an integrated scenario-based probabilistic approach to model resource and reserves. Its goal is to provide quantitative results for a variety of scenarios, thus allowing identification of and focus on critical controls (the variables that are likely to impose the greatest influence).The initial focus at Erha was to incorporate the observed fluid contacts and to develop potential scenarios that included the range of possibilities in unpenetrated portions of the field. Four potential compartmentalization scenarios were hypothesized. The uncertainty model combines these scenarios with reservoir parameters and their plausible ranges. Input data comes from multiple sources including: wells, 3D seismic, reservoir flow simulation, geochemistry, fault-seal analysis, sequence stratigraphic analysis, and analogs. Once created, the model is sampled using Monte-Carlo techniques to create probability density functions for a variety of variables including oil in-place and recoverable reserves.Results of the uncertainty analysis support that despite a thinner oil column on the faulted east-side of the field, Erha is an economically attractive opportunity. Further, the results have been to develop data acquisition plans and mitigation strategies that
Reliability-Based Design of Wind Turbine Foundations

DEFF Research Database (Denmark)

Firouzianbandpey, Sarah

reliable, affordable, clean and renewable energy. Wind turbines have gained popularity among other renewable energy generators by having both technically and economically efficient features and by offering competitive production prices compared to other renewable energy sources. Therefore, it is a key...... shorter spatial correlation lengths in the vertical direction as a result of the depositional process. The normalized cone resistance is a better estimator of spatial trends compared to the normalized friction ratio. In geotechnical engineering analysis and design, practitioners ideally would like to know...... the soil properties at many locations, but achieving this goal can be unrealistic and expensive. Therefore, developing ways to determine these parameters using statistical approaches is of great interest. This research employs a random field model to deal with uncertainty in soil properties due to spatial...
Optimization of FRAP uncertainty analysis option

International Nuclear Information System (INIS)

Peck, S.O.

1979-10-01

The automated uncertainty analysis option that has been incorporated in the FRAP codes (FRAP-T5 and FRAPCON-2) provides the user with a means of obtaining uncertainty bands on code predicted variables at user-selected times during a fuel pin analysis. These uncertainty bands are obtained by multiple single fuel pin analyses to generate data which can then be analyzed by second order statistical error propagation techniques. In this process, a considerable amount of data is generated and stored on tape. The user has certain choices to make regarding which independent variables are to be used in the analysis and what order of error propagation equation should be used in modeling the output response. To aid the user in these decisions, a computer program, ANALYZ, has been written and added to the uncertainty analysis option package. A variety of considerations involved in fitting response surface equations and certain pit-falls of which the user should be aware are discussed. An equation is derived expressing a residual as a function of a fitted model and an assumed true model. A variety of experimental design choices are discussed, including the advantages and disadvantages of each approach. Finally, a description of the subcodes which constitute program ANALYZ is provided
Communicating uncertainty in cost-benefit analysis : A cognitive psychological perspective

NARCIS (Netherlands)

Mouter, N.; Holleman, M.; Calvert, S.C.; Annema, J.A.

2013-01-01

Based on a cognitive psychological theory, this paper aims to improve the communication of uncertainty in Cost-Benefit Analysis. The theory is based on different cognitive-personality and cognitive-social psychological constructs that may help explain individual differences in the processing of
Efficient approach for reliability-based optimization based on weighted importance sampling approach

International Nuclear Information System (INIS)

Yuan, Xiukai; Lu, Zhenzhou

2014-01-01

An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the ‘failure probability function (FPF)’. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology
Quantitative Analysis of Uncertainty in Medical Reporting: Creating a Standardized and Objective Methodology.

Science.gov (United States)

Reiner, Bruce I

2018-04-01

Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogram analysis, topic modeling, and machine learning. The derived uncertainty data offers the potential to objectively analyze report uncertainty in real time and correlate with outcomes analysis for the purpose of context and user-specific decision support at the point of care, where intervention would have the greatest clinical impact.
Failure and Maintenance Analysis Using Web-Based Reliability Database System

International Nuclear Information System (INIS)

Hwang, Seok Won; Kim, Myoung Su; Seong, Ki Yeoul; Na, Jang Hwan; Jerng, Dong Wook

2007-01-01

Korea Hydro and Nuclear Power Company has lunched the development of a database system for PSA and Maintenance Rule implementation. It focuses on the easy processing of raw data into a credible and useful database for the risk-informed environment of nuclear power plant operation and maintenance. Even though KHNP had recently completed the PSA for all domestic NPPs as a requirement of the severe accident mitigation strategy, the component failure data were only gathered as a means of quantification purposes for the relevant project. So, the data were not efficient enough for the Living PSA or other generic purposes. Another reason to build a real time database is for the newly adopted Maintenance Rule, which requests the utility to continuously monitor the plant risk based on its operation and maintenance performance. Furthermore, as one of the pre-condition for the Risk Informed Regulation and Application, the nuclear regulatory agency of Korea requests the development and management of domestic database system. KHNP is stacking up data of operation and maintenance on the Enterprise Resource Planning (ERP) system since its first opening on July, 2003. But, so far a systematic review has not been performed to apply the component failure and maintenance history for PSA and other reliability analysis. The data stored in PUMAS before the ERP system is introduced also need to be converted and managed into the new database structure and methodology. This reliability database system is a web-based interface on a UNIX server with Oracle relational database. It is designed to be applicable for all domestic NPPs with a common database structure and the web interfaces, therefore additional program development would not be necessary for data acquisition and processing in the near future. Categorization standards for systems and components have been implemented to analyze all domestic NPPs. For example, SysCode (for a system code) and CpCode (for a component code) were newly
Multidisciplinary System Reliability Analysis

Science.gov (United States)

Mahadevan, Sankaran; Han, Song; Chamis, Christos C. (Technical Monitor)

2001-01-01

The objective of this study is to develop a new methodology for estimating the reliability of engineering systems that encompass multiple disciplines. The methodology is formulated in the context of the NESSUS probabilistic structural analysis code, developed under the leadership of NASA Glenn Research Center. The NESSUS code has been successfully applied to the reliability estimation of a variety of structural engineering systems. This study examines whether the features of NESSUS could be used to investigate the reliability of systems in other disciplines such as heat transfer, fluid mechanics, electrical circuits etc., without considerable programming effort specific to each discipline. In this study, the mechanical equivalence between system behavior models in different disciplines are investigated to achieve this objective. A new methodology is presented for the analysis of heat transfer, fluid flow, and electrical circuit problems using the structural analysis routines within NESSUS, by utilizing the equivalence between the computational quantities in different disciplines. This technique is integrated with the fast probability integration and system reliability techniques within the NESSUS code, to successfully compute the system reliability of multidisciplinary systems. Traditional as well as progressive failure analysis methods for system reliability estimation are demonstrated, through a numerical example of a heat exchanger system involving failure modes in structural, heat transfer and fluid flow disciplines.
Systematic Evaluation of Uncertainty in Material Flow Analysis

DEFF Research Database (Denmark)

Laner, David; Rechberger, Helmut; Astrup, Thomas Fruergaard

2014-01-01

Material flow analysis (MFA) is a tool to investigate material flows and stocks in defined systems as a basis for resource management or environmental pollution control. Because of the diverse nature of sources and the varying quality and availability of data, MFA results are inherently uncertain....... Uncertainty analyses have received increasing attention in recent MFA studies, but systematic approaches for selection of appropriate uncertainty tools are missing. This article reviews existing literature related to handling of uncertainty in MFA studies and evaluates current practice of uncertainty analysis......) and exploratory MFA (identification of critical parameters and system behavior). Whereas mathematically simpler concepts focusing on data uncertainty characterization are appropriate for descriptive MFAs, statistical approaches enabling more-rigorous evaluation of uncertainty and model sensitivity are needed...
Reliability prediction system based on the failure rate model for electronic components

International Nuclear Information System (INIS)

Lee, Seung Woo; Lee, Hwa Ki

2008-01-01

Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. Among the reliability prediction methods are the statistical analysis based method, the similarity analysis method based on an external failure rate database, and the method based on the physics-of-failure model. In this study, we developed a system by which the reliability of electronic components can be predicted by creating a system for the statistical analysis method of predicting reliability most easily. The failure rate models that were applied are MILHDBK- 217F N2, PRISM, and Telcordia (Bellcore), and these were compared with the general purpose system in order to validate the effectiveness of the developed system. Being able to predict the reliability of electronic components from the stage of design, the system that we have developed is expected to contribute to enhancing the reliability of electronic components
Automation and uncertainty analysis of a method for in-vivo range verification in particle therapy.

Science.gov (United States)

Frey, K; Unholtz, D; Bauer, J; Debus, J; Min, C H; Bortfeld, T; Paganetti, H; Parodi, K

2014-10-07

We introduce the automation of the range difference calculation deduced from particle-irradiation induced β(+)-activity distributions with the so-called most-likely-shift approach, and evaluate its reliability via the monitoring of algorithm- and patient-specific uncertainty factors. The calculation of the range deviation is based on the minimization of the absolute profile differences in the distal part of two activity depth profiles shifted against each other. Depending on the workflow of positron emission tomography (PET)-based range verification, the two profiles under evaluation can correspond to measured and simulated distributions, or only measured data from different treatment sessions. In comparison to previous work, the proposed approach includes an automated identification of the distal region of interest for each pair of PET depth profiles and under consideration of the planned dose distribution, resulting in the optimal shift distance. Moreover, it introduces an estimate of uncertainty associated to the identified shift, which is then used as weighting factor to 'red flag' problematic large range differences. Furthermore, additional patient-specific uncertainty factors are calculated using available computed tomography (CT) data to support the range analysis. The performance of the new method for in-vivo treatment verification in the clinical routine is investigated with in-room PET images for proton therapy as well as with offline PET images for proton and carbon ion therapy. The comparison between measured PET activity distributions and predictions obtained by Monte Carlo simulations or measurements from previous treatment fractions is performed. For this purpose, a total of 15 patient datasets were analyzed, which were acquired at Massachusetts General Hospital and Heidelberg Ion-Beam Therapy Center with in-room PET and offline PET/CT scanners, respectively. Calculated range differences between the compared activity distributions are reported in a
Uncertainty analysis techniques

International Nuclear Information System (INIS)

Marivoet, J.; Saltelli, A.; Cadelli, N.

1987-01-01

The origin of the uncertainty affecting Performance Assessments, as well as their propagation to dose and risk results is discussed. The analysis is focused essentially on the uncertainties introduced by the input parameters, the values of which may range over some orders of magnitude and may be given as probability distribution function. The paper briefly reviews the existing sampling techniques used for Monte Carlo simulations and the methods for characterizing the output curves, determining their convergence and confidence limits. Annual doses, expectation values of the doses and risks are computed for a particular case of a possible repository in clay, in order to illustrate the significance of such output characteristics as the mean, the logarithmic mean and the median as well as their ratios. The report concludes that provisionally, due to its better robustness, such estimation as the 90th percentile may be substituted to the arithmetic mean for comparison of the estimated doses with acceptance criteria. In any case, the results obtained through Uncertainty Analyses must be interpreted with caution as long as input data distribution functions are not derived from experiments reasonably reproducing the situation in a well characterized repository and site
Uncertainty analysis for hot channel

International Nuclear Information System (INIS)

Panka, I.; Kereszturi, A.

2006-01-01

The fulfillment of the safety analysis acceptance criteria is usually evaluated by separate hot channel calculations using the results of neutronic or/and thermo hydraulic system calculations. In case of an ATWS event (inadvertent withdrawal of control assembly), according to the analysis, a number of fuel rods are experiencing DNB for a longer time and must be regarded as failed. Their number must be determined for a further evaluation of the radiological consequences. In the deterministic approach, the global power history must be multiplied by different hot channel factors (kx) taking into account the radial power peaking factors for each fuel pin. If DNB occurs it is necessary to perform a few number of hot channel calculations to determine the limiting kx leading just to DNB and fuel failure (the conservative DNBR limit is 1.33). Knowing the pin power distribution from the core design calculation, the number of failed fuel pins can be calculated. The above procedure can be performed by conservative assumptions (e.g. conservative input parameters in the hot channel calculations), as well. In case of hot channel uncertainty analysis, the relevant input parameters (k x, mass flow, inlet temperature of the coolant, pin average burnup, initial gap size, selection of power history influencing the gap conductance value) of hot channel calculations and the DNBR limit are varied considering the respective uncertainties. An uncertainty analysis methodology was elaborated combining the response surface method with the one sided tolerance limit method of Wilks. The results of deterministic and uncertainty hot channel calculations are compared regarding to the number of failed fuel rods, max. temperature of the clad surface and max. temperature of the fuel (Authors)
Development of A Standard Method for Human Reliability Analysis (HRA) of Nuclear Power Plants

International Nuclear Information System (INIS)

Kang, Dae Il; Jung, Won Dea; Kim, Jae Whan

2005-12-01

According as the demand of risk-informed regulation and applications increase, the quality and reliability of a probabilistic safety assessment (PSA) has been more important. KAERI started a study to standardize the process and the rules of HRA (Human Reliability Analysis) which was known as a major contributor to the uncertainty of PSA. The study made progress as follows; assessing the level of quality of the HRAs in Korea and identifying the weaknesses of the HRAs, determining the requirements for developing a standard HRA method, developing the process and rules for quantifying human error probability. Since the risk-informed applications use the ASME and ANS PSA standard to ensure PSA quality, the standard HRA method was developed to meet the ASME and ANS HRA requirements with level of category II. The standard method was based on THERP and ASEP HRA that are widely used for conventional HRA. However, the method focuses on standardizing and specifying the analysis process, quantification rules and criteria to minimize the deviation of the analysis results caused by different analysts. Several HRA experts from different organizations in Korea participated in developing the standard method. Several case studies were interactively undertaken to verify the usability and applicability of the standard method
Development of A Standard Method for Human Reliability Analysis (HRA) of Nuclear Power Plants

Energy Technology Data Exchange (ETDEWEB)

Kang, Dae Il; Jung, Won Dea; Kim, Jae Whan

2005-12-15

According as the demand of risk-informed regulation and applications increase, the quality and reliability of a probabilistic safety assessment (PSA) has been more important. KAERI started a study to standardize the process and the rules of HRA (Human Reliability Analysis) which was known as a major contributor to the uncertainty of PSA. The study made progress as follows; assessing the level of quality of the HRAs in Korea and identifying the weaknesses of the HRAs, determining the requirements for developing a standard HRA method, developing the process and rules for quantifying human error probability. Since the risk-informed applications use the ASME and ANS PSA standard to ensure PSA quality, the standard HRA method was developed to meet the ASME and ANS HRA requirements with level of category II. The standard method was based on THERP and ASEP HRA that are widely used for conventional HRA. However, the method focuses on standardizing and specifying the analysis process, quantification rules and criteria to minimize the deviation of the analysis results caused by different analysts. Several HRA experts from different organizations in Korea participated in developing the standard method. Several case studies were interactively undertaken to verify the usability and applicability of the standard method.
Reliability Analysis of Corroded Reinforced Concrete Beams Using Enhanced HL-RF Method

Directory of Open Access Journals (Sweden)

Arash Mohammadi Farsani

2015-12-01

Full Text Available Steel corrosion of bars in concrete structures is a complex process which leads to the reduction of the cross-section bars and decreasing the resistance of the concrete and steel materials. In this study, reliability analysis of a reinforced concrete beam with corrosion defects under the distributed load was investigated using the enhanced Hasofer-Lind and Rackwitz-Fiessler (EHL-RF method based on relaxed approach. Robustness of the EHL-RF algorithm was compared with the HL-RF using a complicated example. It was seen that the EHL-RF algorithm is more robust than the HL-RF method. Finally, the effects of corrosion time were investigated using the EHL-RF algorithm for a reinforced concrete beam based on flexural strength in the pitting and general corrosion. The model uncertainties were considered in the resistance and load terms of flexural strength limit state function. The results illustrated that increasing the corrosion time-period leads to increase in the failure probability of the corroded concrete beam.

Reliability Analysis of Elasto-Plastic Structures

DEFF Research Database (Denmark)

Thoft-Christensen, Palle; Sørensen, John Dalsgaard

1984-01-01

. Failure of this type of system is defined either as formation of a mechanism or by failure of a prescribed number of elements. In the first case failure is independent of the order in which the elements fail, but this is not so by the second definition. The reliability analysis consists of two parts...... are described and the two definitions of failure can be used by the first formulation, but only the failure definition based on formation of a mechanism by the second formulation. The second part of the reliability analysis is an estimate of the failure probability for the structure on the basis...
Reliability and safety engineering

CERN Document Server

Verma, Ajit Kumar; Karanki, Durga Rao

2016-01-01

Reliability and safety are core issues that must be addressed throughout the life cycle of engineering systems. Reliability and Safety Engineering presents an overview of the basic concepts, together with simple and practical illustrations. The authors present reliability terminology in various engineering fields, viz.,electronics engineering, software engineering, mechanical engineering, structural engineering and power systems engineering. The book describes the latest applications in the area of probabilistic safety assessment, such as technical specification optimization, risk monitoring and risk informed in-service inspection. Reliability and safety studies must, inevitably, deal with uncertainty, so the book includes uncertainty propagation methods: Monte Carlo simulation, fuzzy arithmetic, Dempster-Shafer theory and probability bounds. Reliability and Safety Engineering also highlights advances in system reliability and safety assessment including dynamic system modeling and uncertainty management. Cas...
Nondestructive examination (NDE) Reliability for Inservice Inspection of Light Water Reactors

International Nuclear Information System (INIS)

Doctor, S.R.; Good, M.S.; Heasler, P.G.; Hockey, R.L.; Simonen, F.A.; Spanner, J.C.; Taylor, T.T.; Vo, T.V.

1992-07-01

The Evaluation and Improvement of NDE reliability for Inservice Inspection of Light Water Reactors (NDE Reliability) Program at the Pacific Northwest Laboratory was established by the Nuclear Regulatory Commission to determine the reliability of current inservice inspection (ISI) techniques and to develop recommendations that will ensure a suitably high inspection reliability. The objectives of this program include determining the reliability of ISI performed on the primary systems of commercial light-water reactors (LWRs); using probabilistic fracture mechanics analysis to determine the impact of NDE unreliability on system safety; and evaluating reliability improvements that can be achieved with improved and advanced technology. A final objective is to formulate recommended revisions to the Regulatory and ASME Code requirements, based on material properties, service conditions, and NDE uncertainties
Dynamic analysis and reliability assessment of structures with uncertain-but-bounded parameters under stochastic process excitations

International Nuclear Information System (INIS)

Do, Duy Minh; Gao, Wei; Song, Chongmin; Tangaramvong, Sawekchai

2014-01-01

This paper presents the non-deterministic dynamic analysis and reliability assessment of structures with uncertain-but-bounded parameters under stochastic process excitations. Random ground acceleration from earthquake motion is adopted to illustrate the stochastic process force. The exact change ranges of natural frequencies, random vibration displacement and stress responses of structures are investigated under the interval analysis framework. Formulations for structural reliability are developed considering the safe boundary and structural random vibration responses as interval parameters. An improved particle swarm optimization algorithm, namely randomised lower sequence initialized high-order nonlinear particle swarm optimization algorithm, is employed to capture the better bounds of structural dynamic characteristics, random vibration responses and reliability. Three numerical examples are used to demonstrate the presented method for interval random vibration analysis and reliability assessment of structures. The accuracy of the results obtained by the presented method is verified by the randomised Quasi-Monte Carlo simulation method (QMCSM) and direct Monte Carlo simulation method (MCSM). - Highlights: • Interval uncertainty is introduced into structural random vibration responses. • Interval dynamic reliability assessments of structures are implemented. • Boundaries of structural dynamic response and reliability are achieved
The uncertainty analysis of model results a practical guide

CERN Document Server

Hofer, Eduard

2018-01-01

This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.
Uncertainty analysis of constant amplitude fatigue test data employing the six parameters random fatigue limit model

Directory of Open Access Journals (Sweden)

Leonetti Davide

2018-01-01

Full Text Available Estimating and reducing uncertainty in fatigue test data analysis is a relevant task in order to assess the reliability of a structural connection with respect to fatigue. Several statistical models have been proposed in the literature with the aim of representing the stress range vs. endurance trend of fatigue test data under constant amplitude loading and the scatter in the finite and infinite life regions. In order to estimate the safety level of the connection also the uncertainty related to the amount of information available need to be estimated using the methods provided by the theory of statistic. The Bayesian analysis is employed to reduce the uncertainty due to the often small amount of test data by introducing prior information related to the parameters of the statistical model. In this work, the inference of fatigue test data belonging to cover plated steel beams is presented. The uncertainty is estimated by making use of Bayesian and frequentist methods. The 5% quantile of the fatigue life is estimated by taking into account the uncertainty related to the sample size for both a dataset containing few samples and one containing more data. The S-N curves resulting from the application of the employed methods are compared and the effect of the reduction of uncertainty in the infinite life region is quantified.
The nexus between geopolitical uncertainty and crude oil markets: An entropy-based wavelet analysis

Science.gov (United States)

Uddin, Gazi Salah; Bekiros, Stelios; Ahmed, Ali

2018-04-01

The global financial crisis and the subsequent geopolitical turbulence in energy markets have brought increased attention to the proper statistical modeling especially of the crude oil markets. In particular, we utilize a time-frequency decomposition approach based on wavelet analysis to explore the inherent dynamics and the casual interrelationships between various types of geopolitical, economic and financial uncertainty indices and oil markets. Via the introduction of a mixed discrete-continuous multiresolution analysis, we employ the entropic criterion for the selection of the optimal decomposition level of a MODWT as well as the continuous-time coherency and phase measures for the detection of business cycle (a)synchronization. Overall, a strong heterogeneity in the revealed interrelationships is detected over time and across scales.
Reliability based topology optimization for continuum structures with local failure constraints

DEFF Research Database (Denmark)

Luo, Yangjun; Zhou, Mingdong; Wang, Michael Yu

2014-01-01

This paper presents an effective method for stress constrained topology optimization problems under load and material uncertainties. Based on the Performance Measure Approach (PMA), the optimization problem is formulated as to minimize the objective function under a large number of (stress......-related) target performance constraints. In order to overcome the stress singularity phenomenon caused by the combined stress and reliability constraints, a reduction strategy on target reliability index is proposed and utilized together with the ε-relaxation approach. Meanwhile, an enhanced aggregation method...... is employed to aggregate the selected active constraints using a general K–S function, which avoids expensive computational cost from the large-scale nature of local failure constraints. Several numerical examples are given to demonstrate the validity of the present method....
Monte Carlo Uncertainty Quantification Using Quasi-1D SRM Ballistic Model

Directory of Open Access Journals (Sweden)

Davide Viganò

2016-01-01

Full Text Available Compactness, reliability, readiness, and construction simplicity of solid rocket motors make them very appealing for commercial launcher missions and embarked systems. Solid propulsion grants high thrust-to-weight ratio, high volumetric specific impulse, and a Technology Readiness Level of 9. However, solid rocket systems are missing any throttling capability at run-time, since pressure-time evolution is defined at the design phase. This lack of mission flexibility makes their missions sensitive to deviations of performance from nominal behavior. For this reason, the reliability of predictions and reproducibility of performances represent a primary goal in this field. This paper presents an analysis of SRM performance uncertainties throughout the implementation of a quasi-1D numerical model of motor internal ballistics based on Shapiro’s equations. The code is coupled with a Monte Carlo algorithm to evaluate statistics and propagation of some peculiar uncertainties from design data to rocker performance parameters. The model has been set for the reproduction of a small-scale rocket motor, discussing a set of parametric investigations on uncertainty propagation across the ballistic model.
Reliability of microprocessor-based relay protection devices: Myths and reality

Directory of Open Access Journals (Sweden)

Gurevich Vladimir

2009-01-01

Full Text Available The article examines four basic theses about the ostensibly extremely high reliability of microprocessor-based relay protection (MP touted by supporters of MP. Through detailed analysis based on many references it is shown that the basis of these theses are widespread myths, and actually MP reliability is lower than the reliability of electromechanical and electronic protective relays on discrete components.
A Quantitative Analysis of Uncertainty in the Grading of Written Exams in Mathematics and Physics

Science.gov (United States)

Hammer, Hugo Lewi; Habib, Laurence

2016-01-01

The most common way to grade students in courses at university and university college level is to use final written exams. The aim of final exams is generally to provide a reliable and a valid measurement of the extent to which a student has achieved the learning outcomes for the course. A source of uncertainty in grading students based on an exam…
Analysis of time-dependent reliability of degenerated reinforced concrete structure

Directory of Open Access Journals (Sweden)

Zhang Hongping

2016-07-01

Full Text Available Durability deterioration of structure is a highly random process. The maintenance of degenerated structure involves the calculation of the reliability of time-dependent structure. This study introduced reinforced concrete structure resistance decrease model and related statistical parameters of uncertainty, analyzed resistance decrease rules of corroded bending element of reinforced concrete structure, and finally calculated timedependent reliability of the corroded bending element of reinforced concrete structure, aiming to provide a specific theoretical basis for the application of time-dependent reliability theory.
Analysis and assessment of water treatment plant reliability

Directory of Open Access Journals (Sweden)

Szpak Dawid

2017-03-01

Full Text Available The subject of the publication is the analysis and assessment of the reliability of the surface water treatment plant (WTP. In the study the one parameter method of reliability assessment was used. Based on the flow sheet derived from the water company the reliability scheme of the analysed WTP was prepared. On the basis of the daily WTP work report the availability index Kg for the individual elements included in the WTP, was determined. Then, based on the developed reliability scheme showing the interrelationships between elements, the availability index Kg for the whole WTP was determined. The obtained value of the availability index Kg was compared with the criteria values.
Fuzzy stochastic generalized reliability studies on embankment systems based on first-order approximation theorem

Directory of Open Access Journals (Sweden)

Wang Yajun

2008-12-01

Full Text Available In order to address the complex uncertainties caused by interfacing between the fuzziness and randomness of the safety problem for embankment engineering projects, and to evaluate the safety of embankment engineering projects more scientifically and reasonably, this study presents the fuzzy logic modeling of the stochastic finite element method (SFEM based on the harmonious finite element (HFE technique using a first-order approximation theorem. Fuzzy mathematical models of safety repertories were introduced into the SFEM to analyze the stability of embankments and foundations in order to describe the fuzzy failure procedure for the random safety performance function. The fuzzy models were developed with membership functions with half depressed gamma distribution, half depressed normal distribution, and half depressed echelon distribution. The fuzzy stochastic mathematical algorithm was used to comprehensively study the local failure mechanism of the main embankment section near Jingnan in the Yangtze River in terms of numerical analysis for the probability integration of reliability on the random field affected by three fuzzy factors. The result shows that the middle region of the embankment is the principal zone of concentrated failure due to local fractures. There is also some local shear failure on the embankment crust. This study provides a referential method for solving complex multi-uncertainty problems in engineering safety analysis.
Uncertainty analysis of the 35% reactor inlet header break in a CANDU 6 reactor using RELAP/SCDAPSIM/MOD4.0 with integrated uncertainty analysis option

International Nuclear Information System (INIS)

Dupleac, D.; Perez, M.; Reventos, F.; Allison, C.

2011-01-01

The RELAP/SCDAPSIM/MOD4.0 code, designed to predict the behavior of reactor systems during normal and accident conditions, is being developed as part of an international nuclear technology Software Development and Training Program (SDTP). RELAP/SCDAPSIM/MOD4.0, which is the first version of RELAP5 completely rewritten to FORTRAN 90/95/2000 standards, uses the publicly available RELAP5 and SCDAP models in combination with (a) advanced programming and numerical techniques, (b) advanced SDTP-member-developed models for LWR, HWR, and research reactor analysis, and (c) a variety of other member-developed computational packages. One such computational package is an integrated uncertainty analysis (IUA) package being developed jointly by the Technical University of Catalonia (UPC) and Innovative Systems Software (ISS). RELAP/SCDAPSIM/MOD4.0(IUA) follows the input-propagation approach using probability distribution functions to define the uncertainty of the input parameters. The main steps for this type of methodologies, often referred as to statistical approaches or Wilks’ methods, are the ones that follow: 1. Selection of the plant; 2. Selection of the scenario; 3. Selection of the safety criteria; 4. Identification and ranking of the relevant phenomena based on the safety criteria; 5. Selection of the appropriate code parameters to represent those phenomena; 6. Association of uncertainty by means of Probability Distribution Functions (PDFs) for each selected parameter; 7. Random sampling of the selected parameters according to its PDF and performing multiple computer runs to obtain uncertainty bands with a certain percentile and confidence level; 8. Processing the results of the multiple computer runs to estimate the uncertainty bands for the computed quantities associated with the selected safety criteria. RELAP/SCDAPSIM/MOD4.0(IUA) calculates the number of required code runs given the desired percentile and confidence level, performs the sampling process for the
Uncertainty analysis of the 35% reactor inlet header break in a CANDU 6 reactor using RELAP/SCDAPSIM/MOD4.0 with integrated uncertainty analysis option

Energy Technology Data Exchange (ETDEWEB)

Dupleac, D., E-mail: danieldu@cne.pub.ro [Politehnica Univ. of Bucharest (Romania); Perez, M.; Reventos, F., E-mail: marina.perez@upc.edu, E-mail: francesc.reventos@upc.edu [Technical Univ. of Catalonia (Spain); Allison, C., E-mail: iss@cableone.net [Innovative Systems Software (United States)

2011-07-01

The RELAP/SCDAPSIM/MOD4.0 code, designed to predict the behavior of reactor systems during normal and accident conditions, is being developed as part of an international nuclear technology Software Development and Training Program (SDTP). RELAP/SCDAPSIM/MOD4.0, which is the first version of RELAP5 completely rewritten to FORTRAN 90/95/2000 standards, uses the publicly available RELAP5 and SCDAP models in combination with (a) advanced programming and numerical techniques, (b) advanced SDTP-member-developed models for LWR, HWR, and research reactor analysis, and (c) a variety of other member-developed computational packages. One such computational package is an integrated uncertainty analysis (IUA) package being developed jointly by the Technical University of Catalonia (UPC) and Innovative Systems Software (ISS). RELAP/SCDAPSIM/MOD4.0(IUA) follows the input-propagation approach using probability distribution functions to define the uncertainty of the input parameters. The main steps for this type of methodologies, often referred as to statistical approaches or Wilks’ methods, are the ones that follow: 1. Selection of the plant; 2. Selection of the scenario; 3. Selection of the safety criteria; 4. Identification and ranking of the relevant phenomena based on the safety criteria; 5. Selection of the appropriate code parameters to represent those phenomena; 6. Association of uncertainty by means of Probability Distribution Functions (PDFs) for each selected parameter; 7. Random sampling of the selected parameters according to its PDF and performing multiple computer runs to obtain uncertainty bands with a certain percentile and confidence level; 8. Processing the results of the multiple computer runs to estimate the uncertainty bands for the computed quantities associated with the selected safety criteria. RELAP/SCDAPSIM/MOD4.0(IUA) calculates the number of required code runs given the desired percentile and confidence level, performs the sampling process for the
Use of Model-Based Design Methods for Enhancing Resiliency Analysis of Unmanned Aerial Vehicles

Science.gov (United States)

Knox, Lenora A.

The most common traditional non-functional requirement analysis is reliability. With systems becoming more complex, networked, and adaptive to environmental uncertainties, system resiliency has recently become the non-functional requirement analysis of choice. Analysis of system resiliency has challenges; which include, defining resilience for domain areas, identifying resilience metrics, determining resilience modeling strategies, and understanding how to best integrate the concepts of risk and reliability into resiliency. Formal methods that integrate all of these concepts do not currently exist in specific domain areas. Leveraging RAMSoS, a model-based reliability analysis methodology for Systems of Systems (SoS), we propose an extension that accounts for resiliency analysis through evaluation of mission performance, risk, and cost using multi-criteria decision-making (MCDM) modeling and design trade study variability modeling evaluation techniques. This proposed methodology, coined RAMSoS-RESIL, is applied to a case study in the multi-agent unmanned aerial vehicle (UAV) domain to investigate the potential benefits of a mission architecture where functionality to complete a mission is disseminated across multiple UAVs (distributed) opposed to being contained in a single UAV (monolithic). The case study based research demonstrates proof of concept for the proposed model-based technique and provides sufficient preliminary evidence to conclude which architectural design (distributed vs. monolithic) is most resilient based on insight into mission resilience performance, risk, and cost in addition to the traditional analysis of reliability.
Urban drainage models - making uncertainty analysis simple

DEFF Research Database (Denmark)

Vezzaro, Luca; Mikkelsen, Peter Steen; Deletic, Ana

2012-01-01

in each measured/observed datapoint; an issue which is commonly overlook in the uncertainty analysis of urban drainage models. This comparison allows the user to intuitively estimate the optimum number of simulations required to conduct uncertainty analyses. The output of the method includes parameter......There is increasing awareness about uncertainties in modelling of urban drainage systems and, as such, many new methods for uncertainty analyses have been developed. Despite this, all available methods have limitations which restrict their widespread application among practitioners. Here...
Overview of methods for uncertainty analysis and sensitivity analysis in probabilistic risk assessment

International Nuclear Information System (INIS)

Iman, R.L.; Helton, J.C.

1985-01-01

Probabilistic Risk Assessment (PRA) is playing an increasingly important role in the nuclear reactor regulatory process. The assessment of uncertainties associated with PRA results is widely recognized as an important part of the analysis process. One of the major criticisms of the Reactor Safety Study was that its representation of uncertainty was inadequate. The desire for the capability to treat uncertainties with the MELCOR risk code being developed at Sandia National Laboratories is indicative of the current interest in this topic. However, as yet, uncertainty analysis and sensitivity analysis in the context of PRA is a relatively immature field. In this paper, available methods for uncertainty analysis and sensitivity analysis in a PRA are reviewed. This review first treats methods for use with individual components of a PRA and then considers how these methods could be combined in the performance of a complete PRA. In the context of this paper, the goal of uncertainty analysis is to measure the imprecision in PRA outcomes of interest, and the goal of sensitivity analysis is to identify the major contributors to this imprecision. There are a number of areas that must be considered in uncertainty analysis and sensitivity analysis for a PRA: (1) information, (2) systems analysis, (3) thermal-hydraulic phenomena/fission product behavior, (4) health and economic consequences, and (5) display of results. Each of these areas and the synthesis of them into a complete PRA are discussed
Reliability Based Ship Structural Design

DEFF Research Database (Denmark)

Dogliani, M.; Østergaard, C.; Parmentier, G.

1996-01-01

This paper deals with the development of different methods that allow the reliability-based design of ship structures to be transferred from the area of research to the systematic application in current design. It summarises the achievements of a three-year collaborative research project dealing...... with developments of models of load effects and of structural collapse adopted in reliability formulations which aim at calibrating partial safety factors for ship structural design. New probabilistic models of still-water load effects are developed both for tankers and for containerships. New results are presented...... structure of several tankers and containerships. The results of the reliability analysis were the basis for the definition of a target safety level which was used to asses the partial safety factors suitable for in a new design rules format to be adopted in modern ship structural design. Finally...

Development of Uncertainty Analysis Method for SMART Digital Core Protection and Monitoring System

International Nuclear Information System (INIS)

Koo, Bon Seung; In, Wang Kee; Hwang, Dae Hyun

2012-01-01

The Korea Atomic Energy Research Institute has developed a system-integrated modular advanced reactor (SMART) for a seawater desalination and electricity generation. Online digital core protection and monitoring systems, called SCOPS and SCOMS respectively were developed. SCOPS calculates minimum DNBR and maximum LPD based on the several online measured system parameters. SCOMS calculates the variables of limiting conditions for operation. KAERI developed overall uncertainty analysis methodology which is used statistically combining uncertainty components of SMART core protection and monitoring system. By applying overall uncertainty factors in on-line SCOPS/SCOMS calculation, calculated LPD and DNBR are conservative with a 95/95 probability/confidence level. In this paper, uncertainty analysis method is described for SMART core protection and monitoring system
Reliability analysis of a phaser measurement unit using a generalized fuzzy lambda-tau(GFLT) technique.

Science.gov (United States)

Komal

2018-05-01

Nowadays power consumption is increasing day-by-day. To fulfill failure free power requirement, planning and implementation of an effective and reliable power management system is essential. Phasor measurement unit(PMU) is one of the key device in wide area measurement and control systems. The reliable performance of PMU assures failure free power supply for any power system. So, the purpose of the present study is to analyse the reliability of a PMU used for controllability and observability of power systems utilizing available uncertain data. In this paper, a generalized fuzzy lambda-tau (GFLT) technique has been proposed for this purpose. In GFLT, system components' uncertain failure and repair rates are fuzzified using fuzzy numbers having different shapes such as triangular, normal, cauchy, sharp gamma and trapezoidal. To select a suitable fuzzy number for quantifying data uncertainty, system experts' opinion have been considered. The GFLT technique applies fault tree, lambda-tau method, fuzzified data using different membership functions, alpha-cut based fuzzy arithmetic operations to compute some important reliability indices. Furthermore, in this study ranking of critical components of the system using RAM-Index and sensitivity analysis have also been performed. The developed technique may be helpful to improve system performance significantly and can be applied to analyse fuzzy reliability of other engineering systems. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Uncertainty analysis in the applications of nuclear probabilistic risk assessment

International Nuclear Information System (INIS)

Le Duy, T.D.

2011-01-01

The aim of this thesis is to propose an approach to model parameter and model uncertainties affecting the results of risk indicators used in the applications of nuclear Probabilistic Risk assessment (PRA). After studying the limitations of the traditional probabilistic approach to represent uncertainty in PRA model, a new approach based on the Dempster-Shafer theory has been proposed. The uncertainty analysis process of the proposed approach consists in five main steps. The first step aims to model input parameter uncertainties by belief and plausibility functions according to the data PRA model. The second step involves the propagation of parameter uncertainties through the risk model to lay out the uncertainties associated with output risk indicators. The model uncertainty is then taken into account in the third step by considering possible alternative risk models. The fourth step is intended firstly to provide decision makers with information needed for decision making under uncertainty (parametric and model) and secondly to identify the input parameters that have significant uncertainty contributions on the result. The final step allows the process to be continued in loop by studying the updating of beliefs functions given new data. The proposed methodology was implemented on a real but simplified application of PRA model. (author)
Error Estimation and Uncertainty Propagation in Computational Fluid Mechanics

Science.gov (United States)

Zhu, J. Z.; He, Guowei; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

Numerical simulation has now become an integral part of engineering design process. Critical design decisions are routinely made based on the simulation results and conclusions. Verification and validation of the reliability of the numerical simulation is therefore vitally important in the engineering design processes. We propose to develop theories and methodologies that can automatically provide quantitative information about the reliability of the numerical simulation by estimating numerical approximation error, computational model induced errors and the uncertainties contained in the mathematical models so that the reliability of the numerical simulation can be verified and validated. We also propose to develop and implement methodologies and techniques that can control the error and uncertainty during the numerical simulation so that the reliability of the numerical simulation can be improved.
Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Science.gov (United States)

Exbrayat, Jean-François; Bloom, A. Anthony; Falloon, Pete; Ito, Akihiko; Smallman, T. Luke; Williams, Mathew

2018-02-01

Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) business as usual emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095-2099) compared to 2001-2005, which is 2-3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y-1. Using REA also leads to a 45-68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.
Quantum uncertainty relation based on the mean deviation

OpenAIRE

Sharma, Gautam; Mukhopadhyay, Chiranjib; Sazim, Sk; Pati, Arun Kumar

2018-01-01

Traditional forms of quantum uncertainty relations are invariably based on the standard deviation. This can be understood in the historical context of simultaneous development of quantum theory and mathematical statistics. Here, we present alternative forms of uncertainty relations, in both state dependent and state independent forms, based on the mean deviation. We illustrate the robustness of this formulation in situations where the standard deviation based uncertainty relation is inapplica...
HUMAN RELIABILITY ANALYSIS DENGAN PENDEKATAN COGNITIVE RELIABILITY AND ERROR ANALYSIS METHOD (CREAM

Directory of Open Access Journals (Sweden)

Zahirah Alifia Maulida

2015-01-01

Full Text Available Kecelakaan kerja pada bidang grinding dan welding menempati urutan tertinggi selama lima tahun terakhir di PT. X. Kecelakaan ini disebabkan oleh human error. Human error terjadi karena pengaruh lingkungan kerja fisik dan non fisik.Penelitian kali menggunakan skenario untuk memprediksi serta mengurangi kemungkinan terjadinya error pada manusia dengan pendekatan CREAM (Cognitive Reliability and Error Analysis Method. CREAM adalah salah satu metode human reliability analysis yang berfungsi untuk mendapatkan nilai Cognitive Failure Probability (CFP yang dapat dilakukan dengan dua cara yaitu basic method dan extended method. Pada basic method hanya akan didapatkan nilai failure probabailty secara umum, sedangkan untuk extended method akan didapatkan CFP untuk setiap task. Hasil penelitian menunjukkan faktor- faktor yang mempengaruhi timbulnya error pada pekerjaan grinding dan welding adalah kecukupan organisasi, kecukupan dari Man Machine Interface (MMI & dukungan operasional, ketersediaan prosedur/ perencanaan, serta kecukupan pelatihan dan pengalaman. Aspek kognitif pada pekerjaan grinding yang memiliki nilai error paling tinggi adalah planning dengan nilai CFP 0.3 dan pada pekerjaan welding yaitu aspek kognitif execution dengan nilai CFP 0.18. Sebagai upaya untuk mengurangi nilai error kognitif pada pekerjaan grinding dan welding rekomendasi yang diberikan adalah memberikan training secara rutin, work instrucstion yang lebih rinci dan memberikan sosialisasi alat. Kata kunci: CREAM (cognitive reliability and error analysis method, HRA (human reliability analysis, cognitive error Abstract The accidents in grinding and welding sectors were the highest cases over the last five years in PT. X and it caused by human error. Human error occurs due to the influence of working environment both physically and non-physically. This study will implement an approaching scenario called CREAM (Cognitive Reliability and Error Analysis Method. CREAM is one of human
Reliability-based management of buried pipelines considering external corrosion defects

Science.gov (United States)

Miran, Seyedeh Azadeh

-system. Sensitivity analysis is also performed to determine to which incorporated parameter(s) in the growth models reliability of the studied pipeline is most sensitive. The reliability analysis results suggest that newly generated defects should be considered in calculating failure probability, especially for prediction of long-term performance of the pipeline and also, impact of the statistical uncertainty in the model parameters is significant that should be considered in the reliability analysis. Finally, with the evaluated time-dependent failure probabilities, a life cycle-cost analysis is conducted to determine optimal inspection interval of studied pipeline. The expected total life-cycle costs consists construction cost and expected costs of inspections, repair, and failure. The repair is conducted when failure probability from any described failure mode exceeds pre-defined probability threshold after each inspection. Moreover, this study also investigates impact of repair threshold values and unit costs of inspection and failure on the expected total life-cycle cost and optimal inspection interval through a parametric study. The analysis suggests that a smaller inspection interval leads to higher inspection costs, but can lower failure cost and also repair cost is less significant compared to inspection and failure costs.
MOV reliability evaluation and periodic verification scheduling

Energy Technology Data Exchange (ETDEWEB)

Bunte, B.D.

1996-12-01

The purpose of this paper is to establish a periodic verification testing schedule based on the expected long term reliability of gate or globe motor operated valves (MOVs). The methodology in this position paper determines the nominal (best estimate) design margin for any MOV based on the best available information pertaining to the MOVs design requirements, design parameters, existing hardware design, and present setup. The uncertainty in this margin is then determined using statistical means. By comparing the nominal margin to the uncertainty, the reliability of the MOV is estimated. The methodology is appropriate for evaluating the reliability of MOVs in the GL 89-10 program. It may be used following periodic testing to evaluate and trend MOV performance and reliability. It may also be used to evaluate the impact of proposed modifications and maintenance activities such as packing adjustments. In addition, it may be used to assess the impact of new information of a generic nature which impacts safety related MOVs.
MOV reliability evaluation and periodic verification scheduling

International Nuclear Information System (INIS)

Bunte, B.D.

1996-01-01

The purpose of this paper is to establish a periodic verification testing schedule based on the expected long term reliability of gate or globe motor operated valves (MOVs). The methodology in this position paper determines the nominal (best estimate) design margin for any MOV based on the best available information pertaining to the MOVs design requirements, design parameters, existing hardware design, and present setup. The uncertainty in this margin is then determined using statistical means. By comparing the nominal margin to the uncertainty, the reliability of the MOV is estimated. The methodology is appropriate for evaluating the reliability of MOVs in the GL 89-10 program. It may be used following periodic testing to evaluate and trend MOV performance and reliability. It may also be used to evaluate the impact of proposed modifications and maintenance activities such as packing adjustments. In addition, it may be used to assess the impact of new information of a generic nature which impacts safety related MOVs
Reliability analysis of stiff versus flexible piping

International Nuclear Information System (INIS)

Lu, S.C.

1985-01-01

The overall objective of this research project is to develop a technical basis for flexible piping designs which will improve piping reliability and minimize the use of pipe supports, snubbers, and pipe whip restraints. The current study was conducted to establish the necessary groundwork based on the piping reliability analysis. A confirmatory piping reliability assessment indicated that removing rigid supports and snubbers tends to either improve or affect very little the piping reliability. The authors then investigated a couple of changes to be implemented in Regulatory Guide (RG) 1.61 and RG 1.122 aimed at more flexible piping design. They concluded that these changes substantially reduce calculated piping responses and allow piping redesigns with significant reduction in number of supports and snubbers without violating ASME code requirements. Furthermore, the more flexible piping redesigns are capable of exhibiting reliability levels equal to or higher than the original stiffer design. An investigation of the malfunction of pipe whip restraints confirmed that the malfunction introduced higher thermal stresses and tended to reduce the overall piping reliability. Finally, support and component reliabilities were evaluated based on available fragility data. Results indicated that the support reliability usually exhibits a moderate decrease as the piping flexibility increases. Most on-line pumps and valves showed an insignificant reduction in reliability for a more flexible piping design
Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

Energy Technology Data Exchange (ETDEWEB)

Wang, Ying; Zhou, Zhi; Botterud, Audun; Zhang, Kaifeng

2018-01-01

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.
Reliability analysis of RC containment structures under combined loads

International Nuclear Information System (INIS)

Hwang, H.; Reich, M.; Kagami, S.

1984-01-01

This paper discusses a reliability analysis method and load combination design criteria for reinforced concrete containment structures under combined loads. The probability based reliability analysis method is briefly described. For load combination design criteria, derivations of the load factors for accidental pressure due to a design basis accident and safe shutdown earthquake (SSE) for three target limit state probabilities are presented
Treatment of uncertainties in the IPCC: a philosophical analysis

Science.gov (United States)

Jebeile, J.; Drouet, I.

2014-12-01

The IPCC produces scientific reports out of findings on climate and climate change. Because the findings are uncertain in many respects, the production of reports requires aggregating assessments of uncertainties of different kinds. This difficult task is currently regulated by the Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. The note recommends that two metrics—i.e. confidence and likelihood— be used for communicating the degree of certainty in findings. Confidence is expressed qualitatively "based on the type, amount, quality, and consistency of evidence […] and the degree of agreement", while likelihood is expressed probabilistically "based on statistical analysis of observations or model results, or expert judgment". Therefore, depending on the evidence evaluated, authors have the choice to present either an assigned level of confidence or a quantified measure of likelihood. But aggregating assessments of uncertainties of these two different kinds express distinct and conflicting methodologies. So the question arises whether the treatment of uncertainties in the IPCC is rationally justified. In order to answer the question, it is worth comparing the IPCC procedures with the formal normative theories of epistemic rationality which have been developed by philosophers. These theories—which include contributions to the philosophy of probability and to bayesian probabilistic confirmation theory—are relevant for our purpose because they are commonly used to assess the rationality of common collective jugement formation based on uncertain knowledge. In this paper we make the comparison and pursue the following objectives: i/we determine whether the IPCC confidence and likelihood can be compared with the notions of uncertainty targeted by or underlying the formal normative theories of epistemic rationality; ii/we investigate whether the formal normative theories of epistemic rationality justify
Comprehensive neutron cross-section and secondary energy distribution uncertainty analysis for a fusion reactor

International Nuclear Information System (INIS)

Gerstl, S.A.W.; LaBauve, R.J.; Young, P.G.

1980-05-01

On the example of General Atomic's well-documented Power Generating Fusion Reactor (PGFR) design, this report exercises a comprehensive neutron cross-section and secondary energy distribution (SED) uncertainty analysis. The LASL sensitivity and uncertainty analysis code SENSIT is used to calculate reaction cross-section sensitivity profiles and integral SED sensitivity coefficients. These are then folded with covariance matrices and integral SED uncertainties to obtain the resulting uncertainties of three calculated neutronics design parameters: two critical radiation damage rates and a nuclear heating rate. The report documents the first sensitivity-based data uncertainty analysis, which incorporates a quantitative treatment of the effects of SED uncertainties. The results demonstrate quantitatively that the ENDF/B-V cross-section data files for C, H, and O, including their SED data, are fully adequate for this design application, while the data for Fe and Ni are at best marginally adequate because they give rise to response uncertainties up to 25%. Much higher response uncertainties are caused by cross-section and SED data uncertainties in Cu (26 to 45%), tungsten (24 to 54%), and Cr (up to 98%). Specific recommendations are given for re-evaluations of certain reaction cross-sections, secondary energy distributions, and uncertainty estimates
Uncertainty Propagation in Monte Carlo Depletion Analysis

International Nuclear Information System (INIS)

Shim, Hyung Jin; Kim, Yeong-il; Park, Ho Jin; Joo, Han Gyu; Kim, Chang Hyo

2008-01-01

A new formulation aimed at quantifying uncertainties of Monte Carlo (MC) tallies such as k eff and the microscopic reaction rates of nuclides and nuclide number densities in MC depletion analysis and examining their propagation behaviour as a function of depletion time step (DTS) is presented. It is shown that the variance of a given MC tally used as a measure of its uncertainty in this formulation arises from four sources; the statistical uncertainty of the MC tally, uncertainties of microscopic cross sections and nuclide number densities, and the cross correlations between them and the contribution of the latter three sources can be determined by computing the correlation coefficients between the uncertain variables. It is also shown that the variance of any given nuclide number density at the end of each DTS stems from uncertainties of the nuclide number densities (NND) and microscopic reaction rates (MRR) of nuclides at the beginning of each DTS and they are determined by computing correlation coefficients between these two uncertain variables. To test the viability of the formulation, we conducted MC depletion analysis for two sample depletion problems involving a simplified 7x7 fuel assembly (FA) and a 17x17 PWR FA, determined number densities of uranium and plutonium isotopes and their variances as well as k ∞ and its variance as a function of DTS, and demonstrated the applicability of the new formulation for uncertainty propagation analysis that need be followed in MC depletion computations. (authors)
Discussion of OECD LWR Uncertainty Analysis in Modelling Benchmark

International Nuclear Information System (INIS)

Ivanov, K.; Avramova, M.; Royer, E.; Gillford, J.

2013-01-01

The demand for best estimate calculations in nuclear reactor design and safety evaluations has increased in recent years. Uncertainty quantification has been highlighted as part of the best estimate calculations. The modelling aspects of uncertainty and sensitivity analysis are to be further developed and validated on scientific grounds in support of their performance and application to multi-physics reactor simulations. The Organization for Economic Co-operation and Development (OECD) / Nuclear Energy Agency (NEA) Nuclear Science Committee (NSC) has endorsed the creation of an Expert Group on Uncertainty Analysis in Modelling (EGUAM). Within the framework of activities of EGUAM/NSC the OECD/NEA initiated the Benchmark for Uncertainty Analysis in Modelling for Design, Operation, and Safety Analysis of Light Water Reactor (OECD LWR UAM benchmark). The general objective of the benchmark is to propagate the predictive uncertainties of code results through complex coupled multi-physics and multi-scale simulations. The benchmark is divided into three phases with Phase I highlighting the uncertainty propagation in stand-alone neutronics calculations, while Phase II and III are focused on uncertainty analysis of reactor core and system respectively. This paper discusses the progress made in Phase I calculations, the Specifications for Phase II and the incoming challenges in defining Phase 3 exercises. The challenges of applying uncertainty quantification to complex code systems, in particular the time-dependent coupled physics models are the large computational burden and the utilization of non-linear models (expected due to the physics coupling). (authors)
Cassini Spacecraft Uncertainty Analysis Data and Methodology Review and Update/Volume 1: Updated Parameter Uncertainty Models for the Consequence Analysis

Energy Technology Data Exchange (ETDEWEB)

WHEELER, TIMOTHY A.; WYSS, GREGORY D.; HARPER, FREDERICK T.

2000-11-01

Uncertainty distributions for specific parameters of the Cassini General Purpose Heat Source Radioisotope Thermoelectric Generator (GPHS-RTG) Final Safety Analysis Report consequence risk analysis were revised and updated. The revisions and updates were done for all consequence parameters for which relevant information exists from the joint project on Probabilistic Accident Consequence Uncertainty Analysis by the United States Nuclear Regulatory Commission and the Commission of European Communities.
Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

Directory of Open Access Journals (Sweden)

A. E. Sikorska

2012-04-01

Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
Reliability Analysis of Fatigue Fracture of Wind Turbine Drivetrain Components

DEFF Research Database (Denmark)

Berzonskis, Arvydas; Sørensen, John Dalsgaard

2016-01-01

in the volume of the casted ductile iron main shaft, on the reliability of the component. The probabilistic reliability analysis conducted is based on fracture mechanics models. Additionally, the utilization of the probabilistic reliability for operation and maintenance planning and quality control is discussed....

Reliability Based Management of Marine Fouling

DEFF Research Database (Denmark)

Faber, Michael Havbro; Hansen, Peter Friis

1999-01-01

The present paper describes the results of a recent study on the application of methods from structural reliability to optimise management of marine fouling on jacket type structures.In particular the study addresses effects on the structural response by assessment and quantification of uncertain......The present paper describes the results of a recent study on the application of methods from structural reliability to optimise management of marine fouling on jacket type structures.In particular the study addresses effects on the structural response by assessment and quantification...... of uncertainties of a set of parameters. These are the seasonal variation of marine fouling parameters, the wave loading (taking into account the seasonal variation in sea-state statistics), and the effects of spatial variations and seasonal effects of marine fouling parameters. Comparison of design values...
Reliability-Based Optimization of Series Systems of Parallel Systems

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...
Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

Directory of Open Access Journals (Sweden)

Matthew Parkan

2018-02-01

Full Text Available Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys, using a probabilistic approach. First, a coarse segmentation (marker controlled watershed is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes. By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered segmentation.
Extensive neutronic sensitivity-uncertainty analysis of a fusion reactor shielding blanket

International Nuclear Information System (INIS)

Hogenbirk, A.

1994-01-01

In this paper the results are presented of an extensive neutronic sensitivity-uncertainty study performed for the design of a shielding blanket for a next-step fusion reactor, such as ITER. A code system was used, which was developed at ECN Petten. The uncertainty in an important response parameter, the neutron heating in the inboard superconducting coils, was evaluated. Neutron transport calculations in the 100 neutron group GAM-II structure were performed using the code ANISN. For the sensitivity and uncertainty calculations the code SUSD was used. Uncertainties due to cross-section uncertainties were taken into account as well as uncertainties due to uncertainties in energy and angular distributions of scattered neutrons (SED and SAD uncertainties, respectively). The subject of direct-term uncertainties (i.e. uncertainties due to uncertainties in the kerma factors of the superconducting coils) is briefly touched upon. It is shown that SAD uncertainties, which have been largely neglected until now, contribute significantly to the total uncertainty. Moreover, the contribution of direct-term uncertainties may be large. The total uncertainty in the neutron heating, only due to Fe cross-sections, amounts to approximately 25%, which is rather large. However, uncertainty data are scarce and the data may very well be conservative. It is shown in this paper that with the code system used, sensitivity and uncertainty calculations can be performed in a straightforward way. Therefore, it is suggested that emphasis is now put on the generation of realistic, reliable covariance data for cross-sections as well as for angular and energy distributions. ((orig.))
Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

NARCIS (Netherlands)

Pratomo, J.; Kuffer, M.; Martinez, Javier; Kohli, D.

2017-01-01

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our
Design reliability engineering

International Nuclear Information System (INIS)

Buden, D.; Hunt, R.N.M.

1989-01-01

Improved design techniques are needed to achieve high reliability at minimum cost. This is especially true of space systems where lifetimes of many years without maintenance are needed and severe mass limitations exist. Reliability must be designed into these systems from the start. Techniques are now being explored to structure a formal design process that will be more complete and less expensive. The intent is to integrate the best features of design, reliability analysis, and expert systems to design highly reliable systems to meet stressing needs. Taken into account are the large uncertainties that exist in materials, design models, and fabrication techniques. Expert systems are a convenient method to integrate into the design process a complete definition of all elements that should be considered and an opportunity to integrate the design process with reliability, safety, test engineering, maintenance and operator training. 1 fig
Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

Science.gov (United States)

West, Thomas K., IV; Johnston, Christopher O.; Hosder, Serhat

2016-01-01

The objective of this work was to perform sensitivity analysis and uncertainty quantification for afterbody radiative heating predictions of Stardust capsule during Earth entry at peak afterbody radiation conditions. The radiation environment in the afterbody region poses significant challenges for accurate uncertainty quantification and sensitivity analysis due to the complexity of the flow physics, computational cost, and large number of un-certain variables. In this study, first a sparse collocation non-intrusive polynomial chaos approach along with global non-linear sensitivity analysis was used to identify the most significant uncertain variables and reduce the dimensions of the stochastic problem. Then, a total order stochastic expansion was constructed over only the important parameters for an efficient and accurate estimate of the uncertainty in radiation. Based on previous work, 388 uncertain parameters were considered in the radiation model, which came from the thermodynamics, flow field chemistry, and radiation modeling. The sensitivity analysis showed that only four of these variables contributed significantly to afterbody radiation uncertainty, accounting for almost 95% of the uncertainty. These included the electronic- impact excitation rate for N between level 2 and level 5 and rates of three chemical reactions in uencing N, N(+), O, and O(+) number densities in the flow field.
An estimation of uncertainties in containment P/T analysis using CONTEMPT/LT code

International Nuclear Information System (INIS)

Kang, Y.M.; Park, G.C.; Lee, U.C.; Kang, C.S.

1991-01-01

In a nuclear power plant, the containment design pressure and temperature (P/T) have been established based on the unrealistic conservatism with suffering from a drawback in the economics. Thus, it is necessary that the uncertainties of design P/T values have to be well defined through an extensive uncertainty analysis with plant-specific input data and or models used in the computer code. This study is to estimate plant-specific uncertainties of containment design P/T using the Monte Carlo method in Kori-3 reactor. Kori-3 plant parameters and Uchida heat transfer coefficient are selected to be treated statistically after the sensitivity study. The Monte Carlo analysis has performed based on the response surface method with the CONTEMPT/LT code and Latin Hypercube sampling technique. Finally, the design values based on 95 %/95 % probability are compared with worst estimated values to assess the design margin. (author)
Reliability assessment of single-phase grid-connected PV microinverters considering mission profile and uncertainties

DEFF Research Database (Denmark)

Zare, Mohammad Hadi; Mohamadian, Mustafa; Wang, Huai

2017-01-01

Microinverters usually connect a PV panel to a Single-phase power grid. In such system, the input power is constant while the output power oscillates twice the line frequency. Thus, the input and output power differences should be stored in a storage component, which is typically an electrolytic ...... irritation of two different places on the micro inverter lifetime is studied....... capacitor. However, electrolytic capacitors are usually blamed for their short lifetime. Recently, some active power decoupling methods are introduced in the literature which can takes advantage of high reliable film capacitors. However, some extra switches and diodes are added to the microinverter which...... can influence the microinverter lifetime. This paper investigates the microinverter reliability according to mission profile where it is installed. To get more accurate results, uncertainties in both lifetime model and manufacturing process are considered. The effect of ambient temperature and solar...
The design and use of reliability data base with analysis tool

Energy Technology Data Exchange (ETDEWEB)

Doorepall, J.; Cooke, R.; Paulsen, J.; Hokstadt, P.

1996-06-01

With the advent of sophisticated computer tools, it is possible to give a distributed population of users direct access to reliability component operational histories. This allows the user a greater freedom in defining statistical populations of components and selecting failure modes. However, the reliability data analyst`s current analytical instrumentarium is not adequate for this purpose. The terminology used in organizing and gathering reliability data is standardized, and the statistical methods used in analyzing this data are not always suitably chosen. This report attempts to establish a baseline with regard to terminology and analysis methods, to support the use of a new analysis tool. It builds on results obtained in several projects for the ESTEC and SKI on the design of reliability databases. Starting with component socket time histories, we identify a sequence of questions which should be answered prior to the employment of analytical methods. These questions concern the homogeneity and stationarity of (possible dependent) competing failure modes and the independence of competing failure modes. Statistical tests, some of them new, are proposed for answering these questions. Attention is given to issues of non-identifiability of competing risk and clustering of failure-repair events. These ideas have been implemented in an analysis tool for grazing component socket time histories, and illustrative results are presented. The appendix provides background on statistical tests and competing failure modes. (au) 4 tabs., 17 ills., 61 refs.
The design and use of reliability data base with analysis tool

International Nuclear Information System (INIS)

Doorepall, J.; Cooke, R.; Paulsen, J.; Hokstadt, P.

1996-06-01

With the advent of sophisticated computer tools, it is possible to give a distributed population of users direct access to reliability component operational histories. This allows the user a greater freedom in defining statistical populations of components and selecting failure modes. However, the reliability data analyst's current analytical instrumentarium is not adequate for this purpose. The terminology used in organizing and gathering reliability data is standardized, and the statistical methods used in analyzing this data are not always suitably chosen. This report attempts to establish a baseline with regard to terminology and analysis methods, to support the use of a new analysis tool. It builds on results obtained in several projects for the ESTEC and SKI on the design of reliability databases. Starting with component socket time histories, we identify a sequence of questions which should be answered prior to the employment of analytical methods. These questions concern the homogeneity and stationarity of (possible dependent) competing failure modes and the independence of competing failure modes. Statistical tests, some of them new, are proposed for answering these questions. Attention is given to issues of non-identifiability of competing risk and clustering of failure-repair events. These ideas have been implemented in an analysis tool for grazing component socket time histories, and illustrative results are presented. The appendix provides background on statistical tests and competing failure modes. (au) 4 tabs., 17 ills., 61 refs
Uncertainty analysis for the BEACON-COLSS core monitoring system application

International Nuclear Information System (INIS)

Morita, T.; Boyd, W.A.; Seong, K.B.

2005-01-01

This paper will cover the measurement uncertainty analysis of BEACON-COLSS core monitoring system. The uncertainty evaluation is made by using a BEACON-COLSS simulation program. By simulating the BEACON on-line operation for analytically generated reactor conditions, accuracy of the 'Measured' results can be evaluated by comparing to analytically generated 'Truth'. The DNB power margin is evaluated based on the Combustion Engineering's Modified Statistical Combination of Uncertainties (MSCU) using the CETOPD code for the DNBR calculation. A BEACON-COLSS simulation program for the uncertainty evaluation function has been established for plant applications. Qualification work has been completed for two Combustion Engineering plants. Results of the BEACON-COLSS measured peaking factors and DNBR power margin are plant type dependent and are applicable to reload cores as long as the core geometry and detector layout are unchanged. (authors)
Nordic reference study on uncertainty and sensitivity analysis

International Nuclear Information System (INIS)

Hirschberg, S.; Jacobsson, P.; Pulkkinen, U.; Porn, K.

1989-01-01

This paper provides a review of the first phase of Nordic reference study on uncertainty and sensitivity analysis. The main objective of this study is to use experiences form previous Nordic Benchmark Exercises and reference studies concerning critical modeling issues such as common cause failures and human interactions, and to demonstrate the impact of associated uncertainties on the uncertainty of the investigated accident sequence. This has been done independently by three working groups which used different approaches to modeling and to uncertainty analysis. The estimated uncertainty interval for the analyzed accident sequence is large. Also the discrepancies between the groups are substantial but can be explained. Sensitivity analyses which have been carried out concern e.g. use of different CCF-quantification models, alternative handling of CCF-data, time windows for operator actions and time dependences in phase mission operation, impact of state-of-knowledge dependences and ranking of dominating uncertainty contributors. Specific findings with respect to these issues are summarized in the paper
Reliability analysis of diverse safety logic systems of fast breeder reactor

International Nuclear Information System (INIS)

Ravi Kumar, Bh.; Apte, P.R.; Srivani, L.; Ilango Sambasivan, S.; Swaminathan, P.

2006-01-01

Safety Logic for Fast Breeder Reactor (FBR) is designed to initiate safety action against Design Basis Events. Based on the outputs of various processing circuits, Safety logic system drives the control rods of the shutdown system. So, Safety Logic system is classified as safety critical system. Therefore, reliability analysis has to be performed. This paper discusses the Reliability analysis of Diverse Safety logic systems of FBRs. For this literature survey on safety critical systems, system reliability approach and standards to be followed like IEC-61508 are discussed in detail. For Programmable Logic device based systems, Hardware Description Languages (HDL) are used. So this paper also discusses the Verification and Validation for HDLs. Finally a case study for the Reliability analysis of Safety logic is discussed. (author)
RELIABILITY ANALYSIS OF BENDING ELIABILITY ANALYSIS OF ...

African Journals Online (AJOL)

eobe

Reliability analysis of the safety levels of the criteria slabs, have been .... was also noted [2] that if the risk level or β < 3.1), the ... reliability analysis. A study [6] has shown that all geometric variables, ..... Germany, 1988. 12. Hasofer, A. M and ...
Uncertainties in Forecasting Streamflow using Entropy Theory

Science.gov (United States)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.
Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, appendices A and B

International Nuclear Information System (INIS)

Harper, F.T.; Young, M.L.; Miller, L.A.; Hora, S.C.; Lui, C.H.; Goossens, L.H.J.; Cooke, R.M.; Paesler-Sauer, J.; Helton, J.C.

1995-01-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the second of a three-volume document describing the project and contains two appendices describing the rationales for the dispersion and deposition data along with short biographies of the 16 experts who participated in the project
Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, appendices A and B

Energy Technology Data Exchange (ETDEWEB)

Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States); Lui, C.H. [Nuclear Regulatory Commission, Washington, DC (United States); Goossens, L.H.J.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Paesler-Sauer, J. [Research Center, Karlsruhe (Germany); Helton, J.C. [and others

1995-01-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the second of a three-volume document describing the project and contains two appendices describing the rationales for the dispersion and deposition data along with short biographies of the 16 experts who participated in the project.
Statistical analysis of the uncertainty related to flood hazard appraisal

Science.gov (United States)

Notaro, Vincenza; Freni, Gabriele

2015-12-01

The estimation of flood hazard frequency statistics for an urban catchment is of great interest in practice. It provides the evaluation of potential flood risk and related damage and supports decision making for flood risk management. Flood risk is usually defined as function of the probability, that a system deficiency can cause flooding (hazard), and the expected damage, due to the flooding magnitude (damage), taking into account both the exposure and the vulnerability of the goods at risk. The expected flood damage can be evaluated by an a priori estimation of potential damage caused by flooding or by interpolating real damage data. With regard to flood hazard appraisal several procedures propose to identify some hazard indicator (HI) such as flood depth or the combination of flood depth and velocity and to assess the flood hazard corresponding to the analyzed area comparing the HI variables with user-defined threshold values or curves (penalty curves or matrixes). However, flooding data are usually unavailable or piecemeal allowing for carrying out a reliable flood hazard analysis, therefore hazard analysis is often performed by means of mathematical simulations aimed at evaluating water levels and flow velocities over catchment surface. As results a great part of the uncertainties intrinsic to flood risk appraisal can be related to the hazard evaluation due to the uncertainty inherent to modeling results and to the subjectivity of the user defined hazard thresholds applied to link flood depth to a hazard level. In the present work, a statistical methodology was proposed for evaluating and reducing the uncertainties connected with hazard level estimation. The methodology has been applied to a real urban watershed as case study.
Understanding Climate Uncertainty with an Ocean Focus

Science.gov (United States)

Tokmakian, R. T.

2009-12-01

Uncertainty in climate simulations arises from various aspects of the end-to-end process of modeling the Earth’s climate. First, there is uncertainty from the structure of the climate model components (e.g. ocean/ice/atmosphere). Even the most complex models are deficient, not only in the complexity of the processes they represent, but in which processes are included in a particular model. Next, uncertainties arise from the inherent error in the initial and boundary conditions of a simulation. Initial conditions are the state of the weather or climate at the beginning of the simulation and other such things, and typically come from observations. Finally, there is the uncertainty associated with the values of parameters in the model. These parameters may represent physical constants or effects, such as ocean mixing, or non-physical aspects of modeling and computation. The uncertainty in these input parameters propagates through the non-linear model to give uncertainty in the outputs. The models in 2020 will no doubt be better than today’s models, but they will still be imperfect, and development of uncertainty analysis technology is a critical aspect of understanding model realism and prediction capability. Smith [2002] and Cox and Stephenson [2007] discuss the need for methods to quantify the uncertainties within complicated systems so that limitations or weaknesses of the climate model can be understood. In making climate predictions, we need to have available both the most reliable model or simulation and a methods to quantify the reliability of a simulation. If quantitative uncertainty questions of the internal model dynamics are to be answered with complex simulations such as AOGCMs, then the only known path forward is based on model ensembles that characterize behavior with alternative parameter settings [e.g. Rougier, 2007]. The relevance and feasibility of using "Statistical Analysis of Computer Code Output" (SACCO) methods for examining uncertainty in

Tolerance analysis in manufacturing using process capability ratio with measurement uncertainty

DEFF Research Database (Denmark)

Mahshid, Rasoul; Mansourvar, Zahra; Hansen, Hans Nørgaard

2017-01-01

. In this paper, a new statistical analysis was applied to manufactured products to assess achieved tolerances when the process is known while using capability ratio and expanded uncertainty. The analysis has benefits for process planning, determining actual precision limits, process optimization, troubleshoot......Tolerance analysis provides valuable information regarding performance of manufacturing process. It allows determining the maximum possible variation of a quality feature in production. Previous researches have focused on application of tolerance analysis to the design of mechanical assemblies...... malfunctioning existing part. The capability measure is based on a number of measurements performed on part’s quality variable. Since the ratio relies on measurements, elimination of any possible error has notable negative impact on results. Therefore, measurement uncertainty was used in combination with process...
Reliability of the Emergency Severity Index: Meta-analysis

Directory of Open Access Journals (Sweden)

Amir Mirhaghi

2015-01-01

Full Text Available Objectives: Although triage systems based on the Emergency Severity Index (ESI have many advantages in terms of simplicity and clarity, previous research has questioned their reliability in practice. Therefore, the aim of this meta-analysis was to determine the reliability of ESI triage scales. Methods: This metaanalysis was performed in March 2014. Electronic research databases were searched and articles conforming to the Guidelines for Reporting Reliability and Agreement Studies were selected. Two researchers independently examined selected abstracts. Data were extracted in the following categories: version of scale (latest/older, participants (adult/paediatric, raters (nurse, physician or expert, method of reliability (intra/inter-rater, reliability statistics (weighted/unweighted kappa and the origin and publication year of the study. The effect size was obtained by the Z-transformation of reliability coefficients. Data were pooled with random-effects models and a meta-regression was performed based on the method of moments estimator. Results: A total of 19 studies from six countries were included in the analysis. The pooled coefficient for the ESI triage scales was substantial at 0.791 (95% confidence interval: 0.787‒0.795. Agreement was higher with the latest and adult versions of the scale and among expert raters, compared to agreement with older and paediatric versions of the scales and with other groups of raters, respectively. Conclusion: ESI triage scales showed an acceptable level of overall reliability. However, ESI scales require more development in order to see full agreement from all rater groups. Further studies concentrating on other aspects of reliability assessment are needed.
Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

Science.gov (United States)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.
Analysis and recommendations for a reliable programming of software based safety systems

International Nuclear Information System (INIS)

Nunez McLeod, J.; Nunez McLeod, J.E.; Rivera, S.S.

1997-01-01

The present paper summarizes the results of several studies performed for the development of high software on i486 microprocessors, towards its utilization for control and safety systems for nuclear power plants. The work is based on software programmed in C language. Several recommendations oriented to high reliability software are analyzed, relating the requirements on high level language to its influence on assembler level. Several metrics are implemented, that allow for the quantification of the results achieved. New metrics were developed and other were adapted, in order to obtain more efficient indexes for the software description. Such metrics are helpful to visualize the adaptation of the software under development to the quality rules under use. A specific program developed to assist the reliability analyst on this quantification is also present in the paper. It performs the analysis of an executable program written in C language, disassembling it and evaluating its inter al structures. (author)
Towards minimizing measurement uncertainty in total petroleum hydrocarbon determination by GC-FID

Energy Technology Data Exchange (ETDEWEB)

Saari, E.

2009-07-01

Despite tightened environmental legislation, spillages of petroleum products remain a serious problem worldwide. The environmental impacts of these spillages are always severe and reliable methods for the identification and quantitative determination of petroleum hydrocarbons in environmental samples are therefore needed. Great improvements in the definition and analysis of total petroleum hydrocarbons (TPH) were finally introduced by international organizations for standardization in 2004. This brought some coherence to the determination and, nowadays, most laboratories seem to employ ISO/DIS 16703:2004, ISO 9377-2:2000 and CEN prEN 14039:2004:E draft international standards for analysing TPH in soil. The implementation of these methods, however, usually fails because the reliability of petroleum hydrocarbon determination has proved to be poor.This thesis describes the assessment of measurement uncertainty for TPH determination in soil. Chemometric methods were used to both estimate the main uncertainty sources and identify the most significant factors affecting these uncertainty sources. The method used for the determinations was based on gas chromatography utilizing flame ionization detection (GC-FID).Chemometric methodology applied in estimating measurement uncertainty for TPH determination showed that the measurement uncertainty is in actual fact dominated by the analytical uncertainty. Within the specific concentration range studied, the analytical uncertainty accounted for as much as 68-80% of the measurement uncertainty. The robustness of the analytical method used for petroleum hydrocarbon determination was then studied in more detail. A two-level Plackett-Burman design and a D-optimal design were utilized to assess the main analytical uncertainty sources of the sample treatment and GC determination procedures. It was also found that the matrix-induced systematic error may also significantly reduce the reliability of petroleum hydrocarbon determination
Component fragility data base for reliability and probability studies

International Nuclear Information System (INIS)

Bandyopadhyay, K.; Hofmayer, C.; Kassier, M.; Pepper, S.

1989-01-01

Safety-related equipment in a nuclear plant plays a vital role in its proper operation and control, and failure of such equipment due to an earthquake may pose a risk to the safe operation of the plant. Therefore, in order to assess the overall reliability of a plant, the reliability of performance of the equipment should be studied first. The success of a reliability or a probability study depends to a great extent on the data base. To meet this demand, Brookhaven National Laboratory (BNL) has formed a test data base relating the seismic capacity of equipment specimens to the earthquake levels. Subsequently, the test data have been analyzed for use in reliability and probability studies. This paper describes the data base and discusses the analysis methods. The final results that can be directly used in plant reliability and probability studies are also presented in this paper
Quantification of margins and mixed uncertainties using evidence theory and stochastic expansions

International Nuclear Information System (INIS)

Shah, Harsheel; Hosder, Serhat; Winter, Tyler

2015-01-01

The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presence of mixed (aleatory and multiple sources of epistemic) uncertainty to the reliability and performance assessment of complex engineering systems through the use of quantification of margins and uncertainties (QMU) methodology. This study focuses on quantifying the simulation uncertainties, both in the design condition and the performance boundaries along with the determination of margins. To address the possibility of multiple sources and intervals for epistemic uncertainty characterization, DSTE is used for uncertainty quantification. An approach to incorporate aleatory uncertainty in Dempster–Shafer structures is presented by discretizing the aleatory variable distributions into sets of intervals. In view of excessive computational costs for large scale applications and repetitive simulations needed for DSTE analysis, a stochastic response surface based on point-collocation non-intrusive polynomial chaos (NIPC) has been implemented as the surrogate for the model response. The technique is demonstrated on a model problem with non-linear analytical functions representing the outputs and performance boundaries of two coupled systems. Finally, the QMU approach is demonstrated on a multi-disciplinary analysis of a high speed civil transport (HSCT). - Highlights: • Quantification of margins and uncertainties (QMU) methodology with evidence theory. • Treatment of both inherent and epistemic uncertainties within evidence theory. • Stochastic expansions for representation of performance metrics and boundaries. • Demonstration of QMU on an analytical problem. • QMU analysis applied to an aerospace system (high speed civil transport)
Integrated system reliability analysis

DEFF Research Database (Denmark)

Gintautas, Tomas; Sørensen, John Dalsgaard

Specific targets: 1) The report shall describe the state of the art of reliability and risk-based assessment of wind turbine components. 2) Development of methodology for reliability and risk-based assessment of the wind turbine at system level. 3) Describe quantitative and qualitative measures...
Reliability analysis techniques in power plant design

International Nuclear Information System (INIS)

Chang, N.E.

1981-01-01

An overview of reliability analysis techniques is presented as applied to power plant design. The key terms, power plant performance, reliability, availability and maintainability are defined. Reliability modeling, methods of analysis and component reliability data are briefly reviewed. Application of reliability analysis techniques from a design engineering approach to improving power plant productivity is discussed. (author)
Benchmarking and application of the state-of-the-art uncertainty analysis methods XSUSA and SHARK-X

International Nuclear Information System (INIS)

Aures, A.; Bostelmann, F.; Hursin, M.; Leray, O.

2017-01-01

Highlights: • Application of the uncertainty analysis methods XSUSA and SHARK-X. • Propagation of nuclear data uncertainty through PWR pin cell depletion calculation. • Uncertainty quantification of eigenvalue, nuclide densities and Doppler coefficient. • Top contributor to overall output uncertainty by sensitivity analysis. • Comparison with SAMPLER and TSUNAMI of the SCALE code package. - Abstract: This study presents collaborative work performed between GRS and PSI on benchmarking and application of the state-of-the-art uncertainty analysis methods XSUSA and SHARK-X. Applied to a PWR pin cell depletion calculation, both methods propagate input uncertainty from nuclear data to output uncertainty. The uncertainty of the multiplication factors, nuclide densities, and fuel temperature coefficients derived by both methods are compared at various burnup steps. Comparisons of these quantities are furthermore performed with the SAMPLER module of SCALE 6.2. The perturbation theory based TSUNAMI module of both SCALE 6.1 and SCALE 6.2 is additionally applied for comparisons of the reactivity coefficient.
Some reflections on uncertainty analysis and management

International Nuclear Information System (INIS)

Aven, Terje

2010-01-01

A guide to quantitative uncertainty analysis and management in industry has recently been issued. The guide provides an overall framework for uncertainty modelling and characterisations, using probabilities but also other uncertainty representations (including the Dempster-Shafer theory). A number of practical applications showing how to use the framework are presented. The guide is considered as an important contribution to the field, but there is a potential for improvements. These relate mainly to the scientific basis and clarification of critical issues, for example, concerning the meaning of a probability and the concept of model uncertainty. A reformulation of the framework is suggested using probabilities as the only representation of uncertainty. Several simple examples are included to motivate and explain the basic ideas of the modified framework.
Integrating reliability analysis and design

International Nuclear Information System (INIS)

Rasmuson, D.M.

1980-10-01

This report describes the Interactive Reliability Analysis Project and demonstrates the advantages of using computer-aided design systems (CADS) in reliability analysis. Common cause failure problems require presentations of systems, analysis of fault trees, and evaluation of solutions to these. Results have to be communicated between the reliability analyst and the system designer. Using a computer-aided design system saves time and money in the analysis of design. Computer-aided design systems lend themselves to cable routing, valve and switch lists, pipe routing, and other component studies. At EG and G Idaho, Inc., the Applicon CADS is being applied to the study of water reactor safety systems
A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment

International Nuclear Information System (INIS)

Purba, Julwan Hendry

2014-01-01

Highlights: • We propose a fuzzy-based reliability approach to evaluate basic event reliabilities. • It implements the concepts of failure possibilities and fuzzy sets. • Experts evaluate basic event failure possibilities using qualitative words. • Triangular fuzzy numbers mathematically represent qualitative failure possibilities. • It is a very good alternative for conventional reliability approach. - Abstract: Fault tree analysis has been widely utilized as a tool for nuclear power plant probabilistic safety assessment. This analysis can be completed only if all basic events of the system fault tree have their quantitative failure rates or failure probabilities. However, it is difficult to obtain those failure data due to insufficient data, environment changing or new components. This study proposes a fuzzy-based reliability approach to evaluate basic events of system fault trees whose failure precise probability distributions of their lifetime to failures are not available. It applies the concept of failure possibilities to qualitatively evaluate basic events and the concept of fuzzy sets to quantitatively represent the corresponding failure possibilities. To demonstrate the feasibility and the effectiveness of the proposed approach, the actual basic event failure probabilities collected from the operational experiences of the David–Besse design of the Babcock and Wilcox reactor protection system fault tree are used to benchmark the failure probabilities generated by the proposed approach. The results confirm that the proposed fuzzy-based reliability approach arises as a suitable alternative for the conventional probabilistic reliability approach when basic events do not have the corresponding quantitative historical failure data for determining their reliability characteristics. Hence, it overcomes the limitation of the conventional fault tree analysis for nuclear power plant probabilistic safety assessment
Global sensitivity analysis in wastewater treatment plant model applications: Prioritizing sources of uncertainty

DEFF Research Database (Denmark)

Sin, Gürkan; Gernaey, Krist; Neumann, Marc B.

2011-01-01

This study demonstrates the usefulness of global sensitivity analysis in wastewater treatment plant (WWTP) design to prioritize sources of uncertainty and quantify their impact on performance criteria. The study, which is performed with the Benchmark Simulation Model no. 1 plant design, complements...... insight into devising useful ways for reducing uncertainties in the plant performance. This information can help engineers design robust WWTP plants....... a previous paper on input uncertainty characterisation and propagation (Sin et al., 2009). A sampling-based sensitivity analysis is conducted to compute standardized regression coefficients. It was found that this method is able to decompose satisfactorily the variance of plant performance criteria (with R2...
Uncertainty Analysis of the Temperature–Resistance Relationship of Temperature Sensing Fabric

Directory of Open Access Journals (Sweden)

Muhammad Dawood Husain

2016-11-01

Full Text Available This paper reports the uncertainty analysis of the temperature–resistance (TR data of the newly developed temperature sensing fabric (TSF, which is a double-layer knitted structure fabricated on an electronic flat-bed knitting machine, made of polyester as a basal yarn, and embedded with fine metallic wire as sensing element. The measurement principle of the TSF is identical to temperature resistance detector (RTD; that is, change in resistance due to change in temperature. The regression uncertainty (uncertainty within repeats and repeatability uncertainty (uncertainty among repeats were estimated by analysing more than 300 TR experimental repeats of 50 TSF samples. The experiments were performed under dynamic heating and cooling environments on a purpose-built test rig within the temperature range of 20–50 °C. The continuous experimental data was recorded through LabVIEW-based graphical user interface. The result showed that temperature and resistance values were not only repeatable but reproducible, with only minor variations. The regression uncertainty was found to be less than ±0.3 °C; the TSF sample made of Ni and W wires showed regression uncertainty of <±0.13 °C in comparison to Cu-based TSF samples (>±0.18 °C. The cooling TR data showed considerably reduced values (±0.07 °C of uncertainty in comparison with the heating TR data (±0.24 °C. The repeatability uncertainty was found to be less than ±0.5 °C. By increasing the number of samples and repeats, the uncertainties may be reduced further. The TSF could be used for continuous measurement of the temperature profile on the surface of the human body.
Role of frameworks, models, data, and judgment in human reliability analysis

Energy Technology Data Exchange (ETDEWEB)

Hannaman, G W

1986-05-01

Many advancements in the methods for treating human interactions in PRA studies have occurred in the last decade. These advancements appear to increase the capability of PRAs to extend beyond just the assessment of the human's importance to safety. However, variations in the application of these advanced models, data, and judgements in recent PRAs make quantitative comparisons among studies extremely difficult. This uncertainty in the analysis diminishes the usefulness of the PRA study for upgrading procedures, enhancing traning, simulator design, technical specification guidance, and for aid in designing the man-machine interface. Hence, there is a need for a framework to guide analysts in incorporating human interactions into the PRA systems analyses so that future users of a PRA study will have a clear understanding of the approaches, models, data, and assumptions which were employed in the initial study. This paper describes the role of the systematic human action reliability procedure (SHARP) in providing a road map through the complex terrain of human reliability that promises to improve the reproducibility of such analysis in the areas of selecting the models, data, representations, and assumptions. Also described is the role that a human cognitive reliability model can have in collecting data from simulators and helping analysts assign human reliability parameters in a PRA study. Use of these systematic approaches to perform or upgrade existing PRAs promises to make PRA studies more useful as risk management tools.
Multi-Disciplinary System Reliability Analysis

Science.gov (United States)

Mahadevan, Sankaran; Han, Song

1997-01-01

The objective of this study is to develop a new methodology for estimating the reliability of engineering systems that encompass multiple disciplines. The methodology is formulated in the context of the NESSUS probabilistic structural analysis code developed under the leadership of NASA Lewis Research Center. The NESSUS code has been successfully applied to the reliability estimation of a variety of structural engineering systems. This study examines whether the features of NESSUS could be used to investigate the reliability of systems in other disciplines such as heat transfer, fluid mechanics, electrical circuits etc., without considerable programming effort specific to each discipline. In this study, the mechanical equivalence between system behavior models in different disciplines are investigated to achieve this objective. A new methodology is presented for the analysis of heat transfer, fluid flow, and electrical circuit problems using the structural analysis routines within NESSUS, by utilizing the equivalence between the computational quantities in different disciplines. This technique is integrated with the fast probability integration and system reliability techniques within the NESSUS code, to successfully compute the system reliability of multi-disciplinary systems. Traditional as well as progressive failure analysis methods for system reliability estimation are demonstrated, through a numerical example of a heat exchanger system involving failure modes in structural, heat transfer and fluid flow disciplines.
Results of a Demonstration Assessment of Passive System Reliability Utilizing the Reliability Method for Passive Systems (RMPS)

Energy Technology Data Exchange (ETDEWEB)

Bucknor, Matthew; Grabaskas, David; Brunett, Acacia; Grelle, Austin

2015-04-26

Advanced small modular reactor designs include many advantageous design features such as passively driven safety systems that are arguably more reliable and cost effective relative to conventional active systems. Despite their attractiveness, a reliability assessment of passive systems can be difficult using conventional reliability methods due to the nature of passive systems. Simple deviations in boundary conditions can induce functional failures in a passive system, and intermediate or unexpected operating modes can also occur. As part of an ongoing project, Argonne National Laboratory is investigating various methodologies to address passive system reliability. The Reliability Method for Passive Systems (RMPS), a systematic approach for examining reliability, is one technique chosen for this analysis. This methodology is combined with the Risk-Informed Safety Margin Characterization (RISMC) approach to assess the reliability of a passive system and the impact of its associated uncertainties. For this demonstration problem, an integrated plant model of an advanced small modular pool-type sodium fast reactor with a passive reactor cavity cooling system is subjected to a station blackout using RELAP5-3D. This paper discusses important aspects of the reliability assessment, including deployment of the methodology, the uncertainty identification and quantification process, and identification of key risk metrics.
Uncertainty and sensitivity analysis of fission gas behavior in engineering-scale fuel modeling

Energy Technology Data Exchange (ETDEWEB)

Pastore, Giovanni, E-mail: Giovanni.Pastore@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Swiler, L.P., E-mail: LPSwile@sandia.gov [Optimization and Uncertainty Quantification, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185-1318 (United States); Hales, J.D., E-mail: Jason.Hales@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Novascone, S.R., E-mail: Stephen.Novascone@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Perez, D.M., E-mail: Danielle.Perez@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Spencer, B.W., E-mail: Benjamin.Spencer@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States); Luzzi, L., E-mail: Lelio.Luzzi@polimi.it [Politecnico di Milano, Department of Energy, Nuclear Engineering Division, via La Masa 34, I-20156 Milano (Italy); Van Uffelen, P., E-mail: Paul.Van-Uffelen@ec.europa.eu [European Commission, Joint Research Centre, Institute for Transuranium Elements, Hermann-von-Helmholtz-Platz 1, D-76344 Karlsruhe (Germany); Williamson, R.L., E-mail: Richard.Williamson@inl.gov [Fuel Modeling and Simulation, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-3840 (United States)

2015-01-15

The role of uncertainties in fission gas behavior calculations as part of engineering-scale nuclear fuel modeling is investigated using the BISON fuel performance code with a recently implemented physics-based model for fission gas release and swelling. Through the integration of BISON with the DAKOTA software, a sensitivity analysis of the results to selected model parameters is carried out based on UO{sub 2} single-pellet simulations covering different power regimes. The parameters are varied within ranges representative of the relative uncertainties and consistent with the information in the open literature. The study leads to an initial quantitative assessment of the uncertainty in fission gas behavior predictions with the parameter characterization presently available. Also, the relative importance of the single parameters is evaluated. Moreover, a sensitivity analysis is carried out based on simulations of a fuel rod irradiation experiment, pointing out a significant impact of the considered uncertainties on the calculated fission gas release and cladding diametral strain. The results of the study indicate that the commonly accepted deviation between calculated and measured fission gas release by a factor of 2 approximately corresponds to the inherent modeling uncertainty at high fission gas release. Nevertheless, significantly higher deviations may be expected for values around 10% and lower. Implications are discussed in terms of directions of research for the improved modeling of fission gas behavior for engineering purposes.
A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China

Directory of Open Access Journals (Sweden)

Mingwu Wang

2014-01-01

Full Text Available The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole.

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