Quantification of uncertainty in gamma spectrometric analysis of food and environmental samples

International Nuclear Information System (INIS)

Yii Mei Wo; Zaharudin Ahmad; Norfaizal Mohamed

2005-01-01

Gamma Spectrometry is widely used to determine the activity of gamma-ray emitter radionuclide inside a sample. Reporting the activity of the measurement for a sample should not be a single value only but it shall be associated with a reasonable uncertainty value since disintegration of radionuclide is a random/spontaneous process. This paper will focus on how the uncertainty was estimated, quantified and calculated, when measuring the activity of Cs-134 and Cs-137 in food and Ra-226, Ra-228 and K-40 in the environmental samples. (Author)

Uncertainty estimation of analysis of Fe, Ca, Zr, Ba, La, Ti and Ce in sediment sample using XRF method

International Nuclear Information System (INIS)

Sukirno; Agus Taftazani

2010-01-01

An uncertainty of analysis of Fe, Ca, Zr, Ba, La, Ti and Ce in river sediment of Panfuran Wariness sample by X RF method has been done. The result value of testing is meaningless if it isn't completed without uncertainty value. The calculation of Ba metal have been presented for example. The aim of the research is to get accreditation certificate of X-Ray Fluorescence method on laboratory of analytical PTAPB – BATAN as well as ISO guide 17025-2005. The result of calculation uncertainty of Fe, Zr, Ba, La, Ce, Ti and Ca analysis showed that the uncertainty components come from: preparation of sample and standard/comparator, purity of material, counting statistic (sample and standard ) and repeatability. The results showed that metals in river sediment of Pancuran Wonosari were Fe = 7.290%, Zr = 54.5 mg/kg, Ba = 1661.6 mg/kg, La = 22.9 mg/kg, Ce = 161.0 mg/kg, Ti = 3193.2 and Ca = 7.816%, and the result of uncertainty estimate of Fe, Zr, Ba, La, Ce, Ti and Ca were ± 0.60%, ± 4.5 mg/kg, ± 55 mg/kg, ± 1.4 mg/kg, 12.0 mg/kg, ± 208 mg/kg and ± 0.61%. (author)

An algorithm to improve sampling efficiency for uncertainty propagation using sampling based method

International Nuclear Information System (INIS)

Campolina, Daniel; Lima, Paulo Rubens I.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

2015-01-01

Sample size and computational uncertainty were varied in order to investigate sample efficiency and convergence of the sampling based method for uncertainty propagation. Transport code MCNPX was used to simulate a LWR model and allow the mapping, from uncertain inputs of the benchmark experiment, to uncertain outputs. Random sampling efficiency was improved through the use of an algorithm for selecting distributions. Mean range, standard deviation range and skewness were verified in order to obtain a better representation of uncertainty figures. Standard deviation of 5 pcm in the propagated uncertainties for 10 n-samples replicates was adopted as convergence criterion to the method. Estimation of 75 pcm uncertainty on reactor k eff was accomplished by using sample of size 93 and computational uncertainty of 28 pcm to propagate 1σ uncertainty of burnable poison radius. For a fixed computational time, in order to reduce the variance of the uncertainty propagated, it was found, for the example under investigation, it is preferable double the sample size than double the amount of particles followed by Monte Carlo process in MCNPX code. (author)

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

Science.gov (United States)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Geostatistical Sampling Methods for Efficient Uncertainty Analysis in Flow and Transport Problems

Science.gov (United States)

Liodakis, Stylianos; Kyriakidis, Phaedon; Gaganis, Petros

2015-04-01

In hydrogeological applications involving flow and transport of in heterogeneous porous media the spatial distribution of hydraulic conductivity is often parameterized in terms of a lognormal random field based on a histogram and variogram model inferred from data and/or synthesized from relevant knowledge. Realizations of simulated conductivity fields are then generated using geostatistical simulation involving simple random (SR) sampling and are subsequently used as inputs to physically-based simulators of flow and transport in a Monte Carlo framework for evaluating the uncertainty in the spatial distribution of solute concentration due to the uncertainty in the spatial distribution of hydraulic con- ductivity [1]. Realistic uncertainty analysis, however, calls for a large number of simulated concentration fields; hence, can become expensive in terms of both time and computer re- sources. A more efficient alternative to SR sampling is Latin hypercube (LH) sampling, a special case of stratified random sampling, which yields a more representative distribution of simulated attribute values with fewer realizations [2]. Here, term representative implies realizations spanning efficiently the range of possible conductivity values corresponding to the lognormal random field. In this work we investigate the efficiency of alternative methods to classical LH sampling within the context of simulation of flow and transport in a heterogeneous porous medium. More precisely, we consider the stratified likelihood (SL) sampling method of [3], in which attribute realizations are generated using the polar simulation method by exploring the geometrical properties of the multivariate Gaussian distribution function. In addition, we propose a more efficient version of the above method, here termed minimum energy (ME) sampling, whereby a set of N representative conductivity realizations at M locations is constructed by: (i) generating a representative set of N points distributed on the

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

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.

The state of the art of the impact of sampling uncertainty on measurement uncertainty

Science.gov (United States)

Leite, V. J.; Oliveira, E. C.

2018-03-01

The measurement uncertainty is a parameter that marks the reliability and can be divided into two large groups: sampling and analytical variations. Analytical uncertainty is a controlled process, performed in the laboratory. The same does not occur with the sampling uncertainty, which, because it faces several obstacles and there is no clarity on how to perform the procedures, has been neglected, although it is admittedly indispensable to the measurement process. This paper aims at describing the state of the art of sampling uncertainty and at assessing its relevance to measurement uncertainty.

Validation and uncertainty estimation of fast neutron activation analysis method for Cu, Fe, Al, Si elements in sediment samples

International Nuclear Information System (INIS)

Sunardi; Samin Prihatin

2010-01-01

Validation and uncertainty estimation of Fast Neutron Activation Analysis (FNAA) method for Cu, Fe, Al, Si elements in sediment samples has been conduced. The aim of the research is to confirm whether FNAA method is still matches to ISO/lEC 17025-2005 standard. The research covered the verification, performance, validation of FNM and uncertainty estimation. Standard of SRM 8704 and sediments were weighted for certain weight and irradiated with 14 MeV fast neutron and then counted using gamma spectrometry. The result of validation method for Cu, Fe, Al, Si element showed that the accuracy were in the range of 95.89-98.68 %, while the precision were in the range 1.13-2.29 %. The result of uncertainty estimation for Cu, Fe, Al, and Si were 2.67, 1.46, 1.71 and 1.20 % respectively. From this data, it can be concluded that the FNM method is still reliable and valid for element contents analysis in samples, because the accuracy is up to 95 % and the precision is under 5 %, while the uncertainty are relatively small and suitable for the range 95 % level of confidence where the uncertainty maximum is 5 %. (author)

Assessment the impact of samplers change on the uncertainty related to geothermalwater sampling

Science.gov (United States)

Wątor, Katarzyna; Mika, Anna; Sekuła, Klaudia; Kmiecik, Ewa

2018-02-01

The aim of this study is to assess the impact of samplers change on the uncertainty associated with the process of the geothermal water sampling. The study was carried out on geothermal water exploited in Podhale region, southern Poland (Małopolska province). To estimate the uncertainty associated with sampling the results of determinations of metasilicic acid (H2SiO3) in normal and duplicate samples collected in two series were used (in each series the samples were collected by qualified sampler). Chemical analyses were performed using ICP-OES method in the certified Hydrogeochemical Laboratory of the Hydrogeology and Engineering Geology Department at the AGH University of Science and Technology in Krakow (Certificate of Polish Centre for Accreditation No. AB 1050). To evaluate the uncertainty arising from sampling the empirical approach was implemented, based on double analysis of normal and duplicate samples taken from the same well in the series of testing. The analyses of the results were done using ROBAN software based on technique of robust statistics analysis of variance (rANOVA). Conducted research proved that in the case of qualified and experienced samplers uncertainty connected with the sampling can be reduced what results in small measurement uncertainty.

Method validation and uncertainty evaluation of organically bound tritium analysis in environmental sample.

Science.gov (United States)

Huang, Yan-Jun; Zeng, Fan; Zhang, Bing; Chen, Chao-Feng; Qin, Hong-Juan; Wu, Lian-Sheng; Guo, Gui-Yin; Yang, Li-Tao; Shang-Guan, Zhi-Hong

2014-08-01

The analytical method for organically bound tritium (OBT) was developed in our laboratory. The optimized operating conditions and parameters were established for sample drying, special combustion, distillation, and measurement on a liquid scintillation spectrometer (LSC). Selected types of OBT samples such as rice, corn, rapeseed, fresh lettuce and pork were analyzed for method validation of recovery rate reproducibility, the minimum detection concentration, and the uncertainty for typical low level environmental sample was evaluated. The combustion water recovery rate of different dried environmental sample was kept at about 80%, the minimum detection concentration of OBT ranged from 0.61 to 0.89 Bq/kg (dry weight), depending on the hydrogen content. It showed that this method is suitable for OBT analysis of environmental sample with stable recovery rate, and the combustion water yield of a sample with weight about 40 g would provide sufficient quantity for measurement on LSC. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Application of status uncertainty analysis methods for AP1000 LBLOCA calculation

International Nuclear Information System (INIS)

Zhang Shunxiang; Liang Guoxing

2012-01-01

Parameter uncertainty analysis is developed by using the reasonable method to establish the response relations between input parameter uncertainties and output uncertainties. The application of the parameter uncertainty analysis makes the simulation of plant state more accuracy and improves the plant economy with reasonable security assurance. The AP1000 LBLOCA was analyzed in this paper and the results indicate that the random sampling statistical analysis method, sensitivity analysis numerical method and traditional error propagation analysis method can provide quite large peak cladding temperature (PCT) safety margin, which is much helpful for choosing suitable uncertainty analysis method to improve the plant economy. Additionally, the random sampling statistical analysis method applying mathematical statistics theory makes the largest safety margin due to the reducing of the conservation. Comparing with the traditional conservative bounding parameter analysis method, the random sampling method can provide the PCT margin of 100 K, while the other two methods can only provide 50-60 K. (authors)

Sampling-based nuclear data uncertainty quantification for continuous energy Monte-Carlo codes

International Nuclear Information System (INIS)

Zhu, T.

2015-01-01

nuclear data uncertainty format. The first stage of NUSS development focuses on applying simple random sampling (SRS) algorithm for uncertainty quantification. The effect of combining multigroup and ACE format on the propagated nuclear data uncertainties is assessed. It is found that the number of energy groups has minor impact on the precision of κ_e_f_f uncertainty as long as the group structure reflects the neutron flux spectrum. Successful verification of the NUSS tool for propagating nuclear data uncertainties through MCNPX and quantifying MCNPX output parameter uncertainties is obtained. The second stage of NUSS development is motivated by the need for an efficient sensitivity analysis methodology based on global sampling and coupled with MCNPX. For complex systems, the computing time for obtaining a breakdown of total uncertainty contributions by individual inputs becomes prohibitive when many MCNPX runs are required. The capability of determining simultaneously the total uncertainty and individual nuclear data uncertainty contributions is thus researched and implemented into the NUSS-RF tool. It is based on the Random Balance Design algorithm and is validated by three mathematical test cases for both linear and nonlinear models and correlated inputs. NUSS-RF is then applied to demonstrate the efficient decomposition of total uncertainty by individual nuclear data. However an attempt to decompose total uncertainty into individual contributions using the conventional S/U method shows different decomposition results when the inputs are correlated. The investigation and findings of this PhD work are valuable because of the introduction of global sensitivity analysis into the existing repertoire of nuclear data uncertainty quantification methods. The NUSS tool is expected to be useful for expanding the types of MCNPX-related applications, such as an upgrade to the current PSI criticality safety assessment methodology for Swiss application, for which nuclear data

Sampling-based nuclear data uncertainty quantification for continuous energy Monte-Carlo codes

Energy Technology Data Exchange (ETDEWEB)

Zhu, T.

2015-07-01

nuclear data uncertainty format. The first stage of NUSS development focuses on applying simple random sampling (SRS) algorithm for uncertainty quantification. The effect of combining multigroup and ACE format on the propagated nuclear data uncertainties is assessed. It is found that the number of energy groups has minor impact on the precision of κ{sub eff} uncertainty as long as the group structure reflects the neutron flux spectrum. Successful verification of the NUSS tool for propagating nuclear data uncertainties through MCNPX and quantifying MCNPX output parameter uncertainties is obtained. The second stage of NUSS development is motivated by the need for an efficient sensitivity analysis methodology based on global sampling and coupled with MCNPX. For complex systems, the computing time for obtaining a breakdown of total uncertainty contributions by individual inputs becomes prohibitive when many MCNPX runs are required. The capability of determining simultaneously the total uncertainty and individual nuclear data uncertainty contributions is thus researched and implemented into the NUSS-RF tool. It is based on the Random Balance Design algorithm and is validated by three mathematical test cases for both linear and nonlinear models and correlated inputs. NUSS-RF is then applied to demonstrate the efficient decomposition of total uncertainty by individual nuclear data. However an attempt to decompose total uncertainty into individual contributions using the conventional S/U method shows different decomposition results when the inputs are correlated. The investigation and findings of this PhD work are valuable because of the introduction of global sensitivity analysis into the existing repertoire of nuclear data uncertainty quantification methods. The NUSS tool is expected to be useful for expanding the types of MCNPX-related applications, such as an upgrade to the current PSI criticality safety assessment methodology for Swiss application, for which nuclear data

Assessment of statistical uncertainty in the quantitative analysis of solid samples in motion using laser-induced breakdown spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Cabalin, L.M.; Gonzalez, A. [Department of Analytical Chemistry, University of Malaga, E-29071 Malaga (Spain); Ruiz, J. [Department of Applied Physics I, University of Malaga, E-29071 Malaga (Spain); Laserna, J.J., E-mail: laserna@uma.e [Department of Analytical Chemistry, University of Malaga, E-29071 Malaga (Spain)

2010-08-15

Statistical uncertainty in the quantitative analysis of solid samples in motion by laser-induced breakdown spectroscopy (LIBS) has been assessed. For this purpose, a LIBS demonstrator was designed and constructed in our laboratory. The LIBS system consisted of a laboratory-scale conveyor belt, a compact optical module and a Nd:YAG laser operating at 532 nm. The speed of the conveyor belt was variable and could be adjusted up to a maximum speed of 2 m s{sup -1}. Statistical uncertainty in the analytical measurements was estimated in terms of precision (reproducibility and repeatability) and accuracy. The results obtained by LIBS on shredded scrap samples under real conditions have demonstrated that the analytical precision and accuracy of LIBS is dependent on the sample geometry, position on the conveyor belt and surface cleanliness. Flat, relatively clean scrap samples exhibited acceptable reproducibility and repeatability; by contrast, samples with an irregular shape or a dirty surface exhibited a poor relative standard deviation.

Assessment of statistical uncertainty in the quantitative analysis of solid samples in motion using laser-induced breakdown spectroscopy

Science.gov (United States)

Cabalín, L. M.; González, A.; Ruiz, J.; Laserna, J. J.

2010-08-01

Statistical uncertainty in the quantitative analysis of solid samples in motion by laser-induced breakdown spectroscopy (LIBS) has been assessed. For this purpose, a LIBS demonstrator was designed and constructed in our laboratory. The LIBS system consisted of a laboratory-scale conveyor belt, a compact optical module and a Nd:YAG laser operating at 532 nm. The speed of the conveyor belt was variable and could be adjusted up to a maximum speed of 2 m s - 1 . Statistical uncertainty in the analytical measurements was estimated in terms of precision (reproducibility and repeatability) and accuracy. The results obtained by LIBS on shredded scrap samples under real conditions have demonstrated that the analytical precision and accuracy of LIBS is dependent on the sample geometry, position on the conveyor belt and surface cleanliness. Flat, relatively clean scrap samples exhibited acceptable reproducibility and repeatability; by contrast, samples with an irregular shape or a dirty surface exhibited a poor relative standard deviation.

Assessment of statistical uncertainty in the quantitative analysis of solid samples in motion using laser-induced breakdown spectroscopy

International Nuclear Information System (INIS)

Cabalin, L.M.; Gonzalez, A.; Ruiz, J.; Laserna, J.J.

2010-01-01

Statistical uncertainty in the quantitative analysis of solid samples in motion by laser-induced breakdown spectroscopy (LIBS) has been assessed. For this purpose, a LIBS demonstrator was designed and constructed in our laboratory. The LIBS system consisted of a laboratory-scale conveyor belt, a compact optical module and a Nd:YAG laser operating at 532 nm. The speed of the conveyor belt was variable and could be adjusted up to a maximum speed of 2 m s -1 . Statistical uncertainty in the analytical measurements was estimated in terms of precision (reproducibility and repeatability) and accuracy. The results obtained by LIBS on shredded scrap samples under real conditions have demonstrated that the analytical precision and accuracy of LIBS is dependent on the sample geometry, position on the conveyor belt and surface cleanliness. Flat, relatively clean scrap samples exhibited acceptable reproducibility and repeatability; by contrast, samples with an irregular shape or a dirty surface exhibited a poor relative standard deviation.

Uncertainty Analysis of In leakage Test for Pressurized Control Room Envelop

Energy Technology Data Exchange (ETDEWEB)

Lee, J. B. [KHNP Central Research Institute, Daejeon (Korea, Republic of)

2013-10-15

In leakage tests for control room envelops(CRE) of newly constructed nuclear power plants are required to prove the control room habitability. Results of the in leakage tests should be analyzed using an uncertainty analysis. Test uncertainty can be an issue if the test results for pressurized CREs show low in leakage. To have a better knowledge of the test uncertainty, a statistical model for the uncertainty analysis is described here and a representative uncertainty analysis of a sample in leakage test is presented. A statistical method for analyzing the uncertainty of the in leakage test is presented here and a representative uncertainty analysis of a sample in leakage test was performed. By using the statistical method we can evaluate the test result with certain level of significance. This method can be more helpful when the difference of the two mean values of the test result is small.

Uncertainty Analysis of In leakage Test for Pressurized Control Room Envelop

International Nuclear Information System (INIS)

Lee, J. B.

2013-01-01

In leakage tests for control room envelops(CRE) of newly constructed nuclear power plants are required to prove the control room habitability. Results of the in leakage tests should be analyzed using an uncertainty analysis. Test uncertainty can be an issue if the test results for pressurized CREs show low in leakage. To have a better knowledge of the test uncertainty, a statistical model for the uncertainty analysis is described here and a representative uncertainty analysis of a sample in leakage test is presented. A statistical method for analyzing the uncertainty of the in leakage test is presented here and a representative uncertainty analysis of a sample in leakage test was performed. By using the statistical method we can evaluate the test result with certain level of significance. This method can be more helpful when the difference of the two mean values of the test result is small

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)

Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

Science.gov (United States)

Wahl, N.; Hennig, P.; Wieser, H. P.; Bangert, M.

2017-07-01

The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ≤slant {5} min). The resulting standard deviation (expectation value) of dose show average global γ{3% / {3}~mm} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity

Sensitivity/uncertainty analysis of a borehole scenario comparing Latin Hypercube Sampling and deterministic sensitivity approaches

International Nuclear Information System (INIS)

Harper, W.V.; Gupta, S.K.

1983-10-01

A computer code was used to study steady-state flow for a hypothetical borehole scenario. The model consists of three coupled equations with only eight parameters and three dependent variables. This study focused on steady-state flow as the performance measure of interest. Two different approaches to sensitivity/uncertainty analysis were used on this code. One approach, based on Latin Hypercube Sampling (LHS), is a statistical sampling method, whereas, the second approach is based on the deterministic evaluation of sensitivities. The LHS technique is easy to apply and should work well for codes with a moderate number of parameters. Of deterministic techniques, the direct method is preferred when there are many performance measures of interest and a moderate number of parameters. The adjoint method is recommended when there are a limited number of performance measures and an unlimited number of parameters. This unlimited number of parameters capability can be extremely useful for finite element or finite difference codes with a large number of grid blocks. The Office of Nuclear Waste Isolation will use the technique most appropriate for an individual situation. For example, the adjoint method may be used to reduce the scope to a size that can be readily handled by a technique such as LHS. Other techniques for sensitivity/uncertainty analysis, e.g., kriging followed by conditional simulation, will be used also. 15 references, 4 figures, 9 tables

Uncertainty and sensitivity analysis in nuclear accident consequence assessment

International Nuclear Information System (INIS)

Karlberg, Olof.

1989-01-01

This report contains the results of a four year project in research contracts with the Nordic Cooperation in Nuclear Safety and the National Institute for Radiation Protection. An uncertainty/sensitivity analysis methodology consisting of Latin Hypercube sampling and regression analysis was applied to an accident consequence model. A number of input parameters were selected and the uncertainties related to these parameter were estimated within a Nordic group of experts. Individual doses, collective dose, health effects and their related uncertainties were then calculated for three release scenarios and for a representative sample of meteorological situations. From two of the scenarios the acute phase after an accident were simulated and from one the long time consequences. The most significant parameters were identified. The outer limits of the calculated uncertainty distributions are large and will grow to several order of magnitudes for the low probability consequences. The uncertainty in the expectation values are typical a factor 2-5 (1 Sigma). The variation in the model responses due to the variation of the weather parameters is fairly equal to the parameter uncertainty induced variation. The most important parameters showed out to be different for each pathway of exposure, which could be expected. However, the overall most important parameters are the wet deposition coefficient and the shielding factors. A general discussion of the usefulness of uncertainty analysis in consequence analysis is also given. (au)

Propagating Mixed Uncertainties in Cyber Attacker Payoffs: Exploration of Two-Phase Monte Carlo Sampling and Probability Bounds Analysis

Energy Technology Data Exchange (ETDEWEB)

Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.; Halappanavar, Mahantesh

2016-09-16

Securing cyber-systems on a continual basis against a multitude of adverse events is a challenging undertaking. Game-theoretic approaches, that model actions of strategic decision-makers, are increasingly being applied to address cybersecurity resource allocation challenges. Such game-based models account for multiple player actions and represent cyber attacker payoffs mostly as point utility estimates. Since a cyber-attacker’s payoff generation mechanism is largely unknown, appropriate representation and propagation of uncertainty is a critical task. In this paper we expand on prior work and focus on operationalizing the probabilistic uncertainty quantification framework, for a notional cyber system, through: 1) representation of uncertain attacker and system-related modeling variables as probability distributions and mathematical intervals, and 2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis.

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

The effect of short-range spatial variability on soil sampling uncertainty

Energy Technology Data Exchange (ETDEWEB)

Perk, Marcel van der [Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht (Netherlands)], E-mail: m.vanderperk@geo.uu.nl; De Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Laboratori, Misure ed Attivita di Campo, Via di Castel Romano, 100-00128 Roma (Italy); Fajgelj, Ales; Sansone, Umberto [International Atomic Energy Agency (IAEA), Agency' s Laboratories Seibersdorf, A-1400 Vienna (Austria); Jeran, Zvonka; Jacimovic, Radojko [Jozef Stefan Institute, Jamova 39, 1000 Ljubljana (Slovenia)

2008-11-15

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

The effect of short-range spatial variability on soil sampling uncertainty.

Science.gov (United States)

Van der Perk, Marcel; de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Fajgelj, Ales; Sansone, Umberto; Jeran, Zvonka; Jaćimović, Radojko

2008-11-01

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

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

Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

Directory of Open Access Journals (Sweden)

Laura Grisotto

2016-04-01

Full Text Available In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters

Directory of Open Access Journals (Sweden)

Simon van Mourik

2014-06-01

Full Text Available Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

Sensitivity and uncertainty analysis applied to a repository in rock salt

International Nuclear Information System (INIS)

Polle, A.N.

1996-12-01

This document describes the sensitivity and uncertainty analysis with UNCSAM, as applied to a repository in rock salt for the EVEREST project. UNCSAM is a dedicated software package for sensitivity and uncertainty analysis, which was already used within the preceding PROSA project. The use of UNCSAM provides a flexible interface to EMOS ECN by substituting the sampled values in the various input files to be used by EMOS ECN ; the model calculations for this repository were performed with the EMOS ECN code. Preceding the sensitivity and uncertainty analysis, a number of preparations has been carried out to facilitate EMOS ECN with the probabilistic input data. For post-processing the EMOS ECN results, the characteristic output signals were processed. For the sensitivity and uncertainty analysis with UNCSAM the stochastic input, i.e. sampled values, and the output for the various EMOS ECN runs have been analyzed. (orig.)

Uncertainty analysis in comparative NAA applied to geological and biological matrices

International Nuclear Information System (INIS)

Zahn, Guilherme S.; Ticianelli, Regina B.; Lange, Camila N.; Favaro, Deborah I.T.; Figueiredo, Ana M.G.

2015-01-01

Comparative nuclear activation analysis is a multielemental primary analytical technique that may be used in a rather broad spectrum of matrices with minimal-to-none sample preprocessing. Although the total activation of a chemical element in a sample depends on a rather large set of parameters, when the sample is irradiated together with a well-known comparator, most of these parameters are crossed out and the concentration of that element can be determined simply by using the activities and masses of the comparator and the sample, the concentration of this chemical element in the sample, the half-life of the formed radionuclide and the time between counting the sample and the comparator. This simplification greatly reduces not only the calculations required, but also the uncertainty associated with the measurement; nevertheless, a cautious analysis must be carried out in order to make sure all relevant uncertainties are properly treated, so that the final result can be as representative of the measurement as possible. In this work, this analysis was performed for geological matrices, where concentrations of the interest nuclides are rather high, but so is the density and average atomic number of the sample, as well as for a biological matrix, in order to allow for a comparison. The results show that the largest part of the uncertainty comes from the activity measurements and from the concentration of the comparator, and that while the influence of time-related terms in the final uncertainty can be safely neglected, the uncertainty in the masses may be relevant under specific circumstances. (author)

Uncertainty analysis in comparative NAA applied to geological and biological matrices

Energy Technology Data Exchange (ETDEWEB)

Zahn, Guilherme S.; Ticianelli, Regina B.; Lange, Camila N.; Favaro, Deborah I.T.; Figueiredo, Ana M.G., E-mail: gzahn@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2015-07-01

Comparative nuclear activation analysis is a multielemental primary analytical technique that may be used in a rather broad spectrum of matrices with minimal-to-none sample preprocessing. Although the total activation of a chemical element in a sample depends on a rather large set of parameters, when the sample is irradiated together with a well-known comparator, most of these parameters are crossed out and the concentration of that element can be determined simply by using the activities and masses of the comparator and the sample, the concentration of this chemical element in the sample, the half-life of the formed radionuclide and the time between counting the sample and the comparator. This simplification greatly reduces not only the calculations required, but also the uncertainty associated with the measurement; nevertheless, a cautious analysis must be carried out in order to make sure all relevant uncertainties are properly treated, so that the final result can be as representative of the measurement as possible. In this work, this analysis was performed for geological matrices, where concentrations of the interest nuclides are rather high, but so is the density and average atomic number of the sample, as well as for a biological matrix, in order to allow for a comparison. The results show that the largest part of the uncertainty comes from the activity measurements and from the concentration of the comparator, and that while the influence of time-related terms in the final uncertainty can be safely neglected, the uncertainty in the masses may be relevant under specific circumstances. (author)

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.

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.

Uncertainty determination of analysis of Ti, V, Cl, Ce, Cr, Cs, Sc, Co, Fe and Ca in solid samples by INAA method using standard addition according to ISO - guide 17025

International Nuclear Information System (INIS)

Sumining; Agus Taftazani

2003-01-01

Uncertainty of analysis of Ti, V, Cl, Ce, Cr, Cs, Sc, Co, Fe and Ca in solid samples by INAA (/instrumental Neutron Activation Analysis) method using comparative technique and standard addition have been carried out at INAA laboratory of P3TM BATAN. The calculation of Ti have been presented as the example. Uncertainty sources of INAA are sampling, sample and standard preparation, irradiation and counting. Sample were come from IAEA (International Atomic Energy Agency) which had ready for analyzed therefore only for sample and standard preparation, irradiation and counting factors were determined. Analysis were done by relative technique, that sample and standard were irradiated together in same capsule therefore irradiation time, neutron flux, irradiation geometry and isotopic properties. will be eliminated. Uncertainty of counting factors were covering radioactivity decay during the counting, pulse losses caused by random counting, counting geometry, and counting rate. Relative technique makes the uncertainty come from counting time for sample and standard that was settled by same counting equipment can be neglected. Uncertainty of counting geometry and thickness of uranium was not detected so there is no contribution come from The fission product. Variation of fuel target nuclides number didn't occurred because the combustion was not occurred during irradiation, and analytical results were not influenced by the chemical status. (author)

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)

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

Estimation of Peaking Factor Uncertainty due to Manufacturing Tolerance using Statistical Sampling Method

Energy Technology Data Exchange (ETDEWEB)

Lee, Kyung Hoon; Park, Ho Jin; Lee, Chung Chan; Cho, Jin Young [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2015-10-15

The purpose of this paper is to study the effect on output parameters in the lattice physics calculation due to the last input uncertainty such as manufacturing deviations from nominal value for material composition and geometric dimensions. In a nuclear design and analysis, the lattice physics calculations are usually employed to generate lattice parameters for the nodal core simulation and pin power reconstruction. These lattice parameters which consist of homogenized few-group cross-sections, assembly discontinuity factors, and form-functions can be affected by input uncertainties which arise from three different sources: 1) multi-group cross-section uncertainties, 2) the uncertainties associated with methods and modeling approximations utilized in lattice physics codes, and 3) fuel/assembly manufacturing uncertainties. In this paper, data provided by the light water reactor (LWR) uncertainty analysis in modeling (UAM) benchmark has been used as the manufacturing uncertainties. First, the effect of each input parameter has been investigated through sensitivity calculations at the fuel assembly level. Then, uncertainty in prediction of peaking factor due to the most sensitive input parameter has been estimated using the statistical sampling method, often called the brute force method. For our analysis, the two-dimensional transport lattice code DeCART2D and its ENDF/B-VII.1 based 47-group library were used to perform the lattice physics calculation. Sensitivity calculations have been performed in order to study the influence of manufacturing tolerances on the lattice parameters. The manufacturing tolerance that has the largest influence on the k-inf is the fuel density. The second most sensitive parameter is the outer clad diameter.

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

MUP, CEC-DES, STRADE. Codes for uncertainty propagation, experimental design and stratified random sampling techniques

International Nuclear Information System (INIS)

Amendola, A.; Astolfi, M.; Lisanti, B.

1983-01-01

The report describes the how-to-use of the codes: MUP (Monte Carlo Uncertainty Propagation) for uncertainty analysis by Monte Carlo simulation, including correlation analysis, extreme value identification and study of selected ranges of the variable space; CEC-DES (Central Composite Design) for building experimental matrices according to the requirements of Central Composite and Factorial Experimental Designs; and, STRADE (Stratified Random Design) for experimental designs based on the Latin Hypercube Sampling Techniques. Application fields, of the codes are probabilistic risk assessment, experimental design, sensitivity analysis and system identification problems

Uncertainty analysis of nuclear waste package corrosion

International Nuclear Information System (INIS)

Kurth, R.E.; Nicolosi, S.L.

1986-01-01

This paper describes the results of an evaluation of three uncertainty analysis methods for assessing the possible variability in calculating the corrosion process in a nuclear waste package. The purpose of the study is the determination of how each of three uncertainty analysis methods, Monte Carlo, Latin hypercube sampling (LHS) and a modified discrete probability distribution method, perform in such calculations. The purpose is not to examine the absolute magnitude of the numbers but rather to rank the performance of each of the uncertainty methods in assessing the model variability. In this context it was found that the Monte Carlo method provided the most accurate assessment but at a prohibitively high cost. The modified discrete probability method provided accuracy close to that of the Monte Carlo for a fraction of the cost. The LHS method was found to be too inaccurate for this calculation although it would be appropriate for use in a model which requires substantially more computer time than the one studied in this paper

Estimation of Uncertainty in Aerosol Concentration Measured by Aerosol Sampling System

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Chan; Song, Yong Jae; Jung, Woo Young; Lee, Hyun Chul; Kim, Gyu Tae; Lee, Doo Yong [FNC Technology Co., Yongin (Korea, Republic of)

2016-10-15

FNC Technology Co., Ltd has been developed test facilities for the aerosol generation, mixing, sampling and measurement under high pressure and high temperature conditions. The aerosol generation system is connected to the aerosol mixing system which injects SiO{sub 2}/ethanol mixture. In the sampling system, glass fiber membrane filter has been used to measure average mass concentration. Based on the experimental results using main carrier gas of steam and air mixture, the uncertainty estimation of the sampled aerosol concentration was performed by applying Gaussian error propagation law. FNC Technology Co., Ltd. has been developed the experimental facilities for the aerosol measurement under high pressure and high temperature. The purpose of the tests is to develop commercial test module for aerosol generation, mixing and sampling system applicable to environmental industry and safety related system in nuclear power plant. For the uncertainty calculation of aerosol concentration, the value of the sampled aerosol concentration is not measured directly, but must be calculated from other quantities. The uncertainty of the sampled aerosol concentration is a function of flow rates of air and steam, sampled mass, sampling time, condensed steam mass and its absolute errors. These variables propagate to the combination of variables in the function. Using operating parameters and its single errors from the aerosol test cases performed at FNC, the uncertainty of aerosol concentration evaluated by Gaussian error propagation law is less than 1%. The results of uncertainty estimation in the aerosol sampling system will be utilized for the system performance data.

Uncertainty and sensitivity analysis in a Probabilistic Safety Analysis level-1

International Nuclear Information System (INIS)

Nunez Mc Leod, Jorge E.; Rivera, Selva S.

1996-01-01

A methodology for sensitivity and uncertainty analysis, applicable to a Probabilistic Safety Assessment Level I has been presented. The work contents are: correct association of distributions to parameters, importance and qualification of expert opinions, generations of samples according to sample sizes, and study of the relationships among system variables and systems response. A series of statistical-mathematical techniques are recommended along the development of the analysis methodology, as well as different graphical visualization for the control of the study. (author)

A study on antimony determination in environmental samples by neutron activation analysis: validation of the methodology and determination of the uncertainty of the measurement

International Nuclear Information System (INIS)

Matsubara, Tassiane Cristina Martins

2011-01-01

detection. The analysis of CRM indicated that the accuracy and precision of the results depend on the type of matrix analyzed, its concentration in the sample, radioisotope measured and of the decay time used for the measurements. The analysis of the components that contribute to the uncertainty of the Sb concentration indicated that the largest uncertainty contribution is given by statistical counting of the sample. The findings also showed that the value of combined standard uncertainty depends on the radioisotopes of Sb measured and the decay time used for counting. This study showed that NAA is a very adequate method for Sb determinations in environmental samples furnishing results with low uncertainty values. (author)

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.

Uncertainty Analysis Based on Sparse Grid Collocation and Quasi-Monte Carlo Sampling with Application in Groundwater Modeling

Science.gov (United States)

Zhang, G.; Lu, D.; Ye, M.; Gunzburger, M.

2011-12-01

Markov Chain Monte Carlo (MCMC) methods have been widely used in many fields of uncertainty analysis to estimate the posterior distributions of parameters and credible intervals of predictions in the Bayesian framework. However, in practice, MCMC may be computationally unaffordable due to slow convergence and the excessive number of forward model executions required, especially when the forward model is expensive to compute. Both disadvantages arise from the curse of dimensionality, i.e., the posterior distribution is usually a multivariate function of parameters. Recently, sparse grid method has been demonstrated to be an effective technique for coping with high-dimensional interpolation or integration problems. Thus, in order to accelerate the forward model and avoid the slow convergence of MCMC, we propose a new method for uncertainty analysis based on sparse grid interpolation and quasi-Monte Carlo sampling. First, we construct a polynomial approximation of the forward model in the parameter space by using the sparse grid interpolation. This approximation then defines an accurate surrogate posterior distribution that can be evaluated repeatedly at minimal computational cost. Second, instead of using MCMC, a quasi-Monte Carlo method is applied to draw samples in the parameter space. Then, the desired probability density function of each prediction is approximated by accumulating the posterior density values of all the samples according to the prediction values. Our method has the following advantages: (1) the polynomial approximation of the forward model on the sparse grid provides a very efficient evaluation of the surrogate posterior distribution; (2) the quasi-Monte Carlo method retains the same accuracy in approximating the PDF of predictions but avoids all disadvantages of MCMC. The proposed method is applied to a controlled numerical experiment of groundwater flow modeling. The results show that our method attains the same accuracy much more efficiently

A comparison of uncertainty analysis methods using a groundwater flow model

International Nuclear Information System (INIS)

Doctor, P.G.; Jacobson, E.A.; Buchanan, J.A.

1988-06-01

This report evaluates three uncertainty analysis methods that are proposed for use in performances assessment activities within the OCRWM and Nuclear Regulatory Commission (NRC) communities. The three methods are Monte Carlo simulation with unconstrained sampling, Monte Carlo simulation with Latin Hypercube sampling, and first-order analysis. Monte Carlo simulation with unconstrained sampling is a generally accepted uncertainty analysis method, but it has the disadvantage of being costly and time consuming. Latin Hypercube sampling was proposed to make Monte Carlo simulation more efficient. However, although it was originally formulated for independent variables, which is a major drawback in performance assessment modeling, Latin Hypercube can be used to generate correlated samples. The first-order method is efficient to implement because it is based on the first-order Taylor series expansion; however, there is concern that it does not adequately describe the variability for complex models. These three uncertainty analysis methods were evaluated using a calibrated groundwater flow model of a unconfined aquifer in southern Arizona. The two simulation methods produced similar results, although the Latin Hypercube method tends to produce samples whose estimates of statistical parameters are closer to the desired parameters. The mean travel times for the first-order method does not agree with those of the simulations. In additions, the first-order method produces estimates of variance in travel times that are more variable than those produced by the simulation methods, resulting in nonconservative tolerance intervals. 13 refs., 33 figs

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

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

Energy Technology Data Exchange (ETDEWEB)

Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.

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.

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).

Deterministic methods for sensitivity and uncertainty analysis in large-scale computer models

International Nuclear Information System (INIS)

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

1987-01-01

The fields of sensitivity and uncertainty analysis are dominated by statistical techniques when large-scale modeling codes are being analyzed. This paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. The paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. The paper demonstrates the deterministic approach to sensitivity and uncertainty analysis as applied to a sample problem that models the flow of water through a borehole. The sample problem 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. The DUA method gives a more accurate result based upon only two model executions compared to fifty executions in the statistical case

Multielemental analysis of 18 essential and toxic elements in amniotic fluid samples by ICP-MS: Full procedure validation and estimation of measurement uncertainty.

Science.gov (United States)

Markiewicz, B; Sajnóg, A; Lorenc, W; Hanć, A; Komorowicz, I; Suliburska, J; Kocyłowski, R; Barałkiewicz, D

2017-11-01

Amniotic fluid is the substantial factor in the development of an embryo and fetus due to the fact that water and solutes contained in it penetrate the fetal membranes in an hydrostatic and osmotic way as well as being swallowed by the fetus. Elemental composition of amniotic fluid influences the growth and health of the fetus, therefore, an analysis of amniotic fluid is important because the results would indicate abnormal levels of minerals or toxic elements. Inductively coupled plasma mass spectroscopy (ICP-MS) is often used for determination of trace and ultra-trace level elements in a wide range of matrices including biological samples because of its unique analytical capabilities. In the case of trace and ultra-trace level analysis detailed characteristics of analytical procedure as well as properties of the analytical result are particularly important. The purpose of this study was to develop a new analytical procedure for multielemental analysis of 18 elements (Al, As, Ba, Ca, Cd, Co, Cr, Cu, Mg, Mn, Ni, Pb, Sb, Se, Sr, U, V and Zn) in amniotic fluid samples using ICP-MS. Dynamic reaction cell (DRC) with two reaction gases, ammonia and oxygen, was involved in the experiment to eliminate spectral interferences. Detailed validation was conducted using 3 certified reference mterials (CRMs) and real amniotic fluid samples collected from patients. Repeatability for all analyzed analytes was found to range from 0.70% to 8.0% and for intermediate precision results varied from 1.3% to 15%. Trueness expressed as recovery ranged from 80% to 125%. Traceability was assured through the analyses of CRMs. Uncertainty of the results was also evaluated using single-laboratory validation approach. The obtained expanded uncertainty (U) results for CRMs, expressed as a percentage of the concentration of an analyte, were found to be between 8.3% for V and 45% for Cd. Standard uncertainty of the precision was found to have a greater influence on the combined standard uncertainty

Demonstration uncertainty/sensitivity analysis using the health and economic consequence model CRAC2

International Nuclear Information System (INIS)

Alpert, D.J.; Iman, R.L.; Johnson, J.D.; Helton, J.C.

1985-01-01

This paper summarizes a demonstration uncertainty/sensitivity analysis performed on the reactor accident consequence model CRAC2. The study was performed with uncertainty/sensitivity analysis techniques compiled as part of the MELCOR program. The principal objectives of the study were: 1) to demonstrate the use of the uncertainty/sensitivity analysis techniques on a health and economic consequence model, 2) to test the computer models which implement the techniques, 3) to identify possible difficulties in performing such an analysis, and 4) to explore alternative means of analyzing, displaying, and describing the results. Demonstration of the applicability of the techniques was the motivation for performing this study; thus, the results should not be taken as a definitive uncertainty analysis of health and economic consequences. Nevertheless, significant insights on health and economic consequence analysis can be drawn from the results of this type of study. Latin hypercube sampling (LHS), a modified Monte Carlo technique, was used in this study. LHS generates a multivariate input structure in which all the variables of interest are varied simultaneously and desired correlations between variables are preserved. LHS has been shown to produce estimates of output distribution functions that are comparable with results of larger random samples

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

Science.gov (United States)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

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

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

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

Estimation of sampling error uncertainties in observed surface air temperature change in China

Science.gov (United States)

Hua, Wei; Shen, Samuel S. P.; Weithmann, Alexander; Wang, Huijun

2017-08-01

This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K2 while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K2. In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year)-1 occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year)-1 in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period.

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...

Comparison between conservative perturbation and sampling based methods for propagation of Non-Neutronic uncertainties

International Nuclear Information System (INIS)

Campolina, Daniel de A.M.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

2013-01-01

For all the physical components that comprise a nuclear system there is an uncertainty. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a best estimate calculation that has been replacing the conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using sampling based method is recent because of the huge computational effort required. In this work a sample space of MCNP calculations were used as a black box model to propagate the uncertainty of system parameters. The efficiency of the method was compared to a conservative method. Uncertainties in input parameters of the reactor considered non-neutronic uncertainties, including geometry dimensions and density. The effect of the uncertainties on the effective multiplication factor of the system was analyzed respect to the possibility of using many uncertainties in the same input. If the case includes more than 46 parameters with uncertainty in the same input, the sampling based method is proved to be more efficient than the conservative method. (author)

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)

Estimating annual bole biomass production using uncertainty analysis

Science.gov (United States)

Travis J. Woolley; Mark E. Harmon; Kari B. O' Connell

2007-01-01

Two common sampling methodologies coupled with a simple statistical model were evaluated to determine the accuracy and precision of annual bole biomass production (BBP) and inter-annual variability estimates using this type of approach. We performed an uncertainty analysis using Monte Carlo methods in conjunction with radial growth core data from trees in three Douglas...

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

Uncertainties in Coastal Ocean Color Products: Impacts of Spatial Sampling

Science.gov (United States)

Pahlevan, Nima; Sarkar, Sudipta; Franz, Bryan A.

2016-01-01

With increasing demands for ocean color (OC) products with improved accuracy and well characterized, per-retrieval uncertainty budgets, it is vital to decompose overall estimated errors into their primary components. Amongst various contributing elements (e.g., instrument calibration, atmospheric correction, inversion algorithms) in the uncertainty of an OC observation, less attention has been paid to uncertainties associated with spatial sampling. In this paper, we simulate MODIS (aboard both Aqua and Terra) and VIIRS OC products using 30 m resolution OC products derived from the Operational Land Imager (OLI) aboard Landsat-8, to examine impacts of spatial sampling on both cross-sensor product intercomparisons and in-situ validations of R(sub rs) products in coastal waters. Various OLI OC products representing different productivity levels and in-water spatial features were scanned for one full orbital-repeat cycle of each ocean color satellite. While some view-angle dependent differences in simulated Aqua-MODIS and VIIRS were observed, the average uncertainties (absolute) in product intercomparisons (due to differences in spatial sampling) at regional scales are found to be 1.8%, 1.9%, 2.4%, 4.3%, 2.7%, 1.8%, and 4% for the R(sub rs)(443), R(sub rs)(482), R(sub rs)(561), R(sub rs)(655), Chla, K(sub d)(482), and b(sub bp)(655) products, respectively. It is also found that, depending on in-water spatial variability and the sensor's footprint size, the errors for an in-situ validation station in coastal areas can reach as high as +/- 18%. We conclude that a) expected biases induced by the spatial sampling in product intercomparisons are mitigated when products are averaged over at least 7 km × 7 km areas, b) VIIRS observations, with improved consistency in cross-track spatial sampling, yield more precise calibration/validation statistics than that of MODIS, and c) use of a single pixel centered on in-situ coastal stations provides an optimal sampling size for

Using the sampling method to propagate uncertainties of physical parameters in systems with fissile material

International Nuclear Information System (INIS)

Campolina, Daniel de Almeida Magalhães

2015-01-01

There is an uncertainty for all the components that comprise the model of a nuclear system. Assessing the impact of uncertainties in the simulation of fissionable material systems is essential for a realistic calculation that has been replacing conservative model calculations as the computational power increases. The propagation of uncertainty in a simulation using a Monte Carlo code by sampling the input parameters is recent because of the huge computational effort required. By analyzing the propagated uncertainty to the effective neutron multiplication factor (k eff ), the effects of the sample size, computational uncertainty and efficiency of a random number generator to represent the distributions that characterize physical uncertainty in a light water reactor was investigated. A program entitled GB s ample was implemented to enable the application of the random sampling method, which requires an automated process and robust statistical tools. The program was based on the black box model and the MCNPX code was used in and parallel processing for the calculation of particle transport. The uncertainties considered were taken from a benchmark experiment in which the effects in k eff due to physical uncertainties is done through a conservative method. In this work a script called GB s ample was implemented to automate the sampling based method, use multiprocessing and assure the necessary robustness. It has been found the possibility of improving the efficiency of the random sampling method by selecting distributions obtained from a random number generator in order to obtain a better representation of uncertainty figures. After the convergence of the method is achieved, in order to reduce the variance of the uncertainty propagated without increase in computational time, it was found the best number o components to be sampled. It was also observed that if the sampling method is used to calculate the effect on k eff due to physical uncertainties reported by

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.

Uncertainty modeling in vibration, control and fuzzy analysis of structural systems

CERN Document Server

Halder, Achintya; Ayyub, Bilal M

1997-01-01

This book gives an overview of the current state of uncertainty modeling in vibration, control, and fuzzy analysis of structural and mechanical systems. It is a coherent compendium written by leading experts and offers the reader a sampling of exciting research areas in several fast-growing branches in this field. Uncertainty modeling and analysis are becoming an integral part of system definition and modeling in many fields. The book consists of ten chapters that report the work of researchers, scientists and engineers on theoretical developments and diversified applications in engineering sy

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)

Forecasting the Number of Soil Samples Required to Reduce Remediation Cost Uncertainty

OpenAIRE

Demougeot-Renard, Hélène; de Fouquet, Chantal; Renard, Philippe

2008-01-01

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of vol...

Uncertainty and sensitivity analysis of environmental transport models

International Nuclear Information System (INIS)

Margulies, T.S.; Lancaster, L.E.

1985-01-01

An uncertainty and sensitivity analysis has been made of the CRAC-2 (Calculations of Reactor Accident Consequences) atmospheric transport and deposition models. Robustness and uncertainty aspects of air and ground deposited material and the relative contribution of input and model parameters were systematically studied. The underlying data structures were investigated using a multiway layout of factors over specified ranges generated via a Latin hypercube sampling scheme. The variables selected in our analysis include: weather bin, dry deposition velocity, rain washout coefficient/rain intensity, duration of release, heat content, sigma-z (vertical) plume dispersion parameter, sigma-y (crosswind) plume dispersion parameter, and mixing height. To determine the contributors to the output variability (versus distance from the site) step-wise regression analyses were performed on transformations of the spatial concentration patterns simulated. 27 references, 2 figures, 3 tables

Comparison of uncertainties related to standardization of urine samples with volume and creatinine concentration

DEFF Research Database (Denmark)

Garde, Anne Helene; Hansen, Ase Marie; Kristiansen, Jesper

2004-01-01

When measuring biomarkers in urine, volume (and time) or concentration of creatinine are both accepted methods of standardization for diuresis. Both types of standardization contribute uncertainty to the final result. The aim of the present paper was to compare the uncertainty introduced when usi...... increase in convenience for the participants, when collecting small volumes rather than complete 24 h samples....... the two types of standardization on 24 h samples from healthy individuals. Estimates of uncertainties were based on results from the literature supplemented with data from our own studies. Only the difference in uncertainty related to the two standardization methods was evaluated. It was found...... that the uncertainty associated with creatinine standardization (19-35%) was higher than the uncertainty related to volume standardization (up to 10%, when not correcting for deviations from 24 h) for 24 h urine samples. However, volume standardization introduced an average bias of 4% due to missed volumes...

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

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

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

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

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)

Large sample neutron activation analysis of a reference inhomogeneous sample

International Nuclear Information System (INIS)

Vasilopoulou, T.; Athens National Technical University, Athens; Tzika, F.; Stamatelatos, I.E.; Koster-Ammerlaan, M.J.J.

2011-01-01

A benchmark experiment was performed for Neutron Activation Analysis (NAA) of a large inhomogeneous sample. The reference sample was developed in-house and consisted of SiO 2 matrix and an Al-Zn alloy 'inhomogeneity' body. Monte Carlo simulations were employed to derive appropriate correction factors for neutron self-shielding during irradiation as well as self-attenuation of gamma rays and sample geometry during counting. The large sample neutron activation analysis (LSNAA) results were compared against reference values and the trueness of the technique was evaluated. An agreement within ±10% was observed between LSNAA and reference elemental mass values, for all matrix and inhomogeneity elements except Samarium, provided that the inhomogeneity body was fully simulated. However, in cases that the inhomogeneity was treated as not known, the results showed a reasonable agreement for most matrix elements, while large discrepancies were observed for the inhomogeneity elements. This study provided a quantification of the uncertainties associated with inhomogeneity in large sample analysis and contributed to the identification of the needs for future development of LSNAA facilities for analysis of inhomogeneous samples. (author)

Uncertainty and Sensitivity Analysis Results Obtained in the 1996 Performance Assessment for the Waste Isolation Pilot Plant

International Nuclear Information System (INIS)

Bean, J.E.; Berglund, J.W.; Davis, F.J.; Economy, K.; Garner, J.W.; Helton, J.C.; Johnson, J.D.; MacKinnon, R.J.; Miller, J.; O'Brien, D.G.; Ramsey, J.L.; Schreiber, J.D.; Shinta, A.; Smith, L.N.; Stockman, C.; Stoelzel, D.M.; Vaughn, P.

1998-01-01

The Waste Isolation Pilot Plant (WPP) is located in southeastern New Mexico and is being developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of transuranic (TRU) waste. A detailed performance assessment (PA) for the WIPP was carried out in 1996 and supports an application by the DOE to the U.S. Environmental Protection Agency (EPA) for the certification of the WIPP for the disposal of TRU waste. The 1996 WIPP PA uses a computational structure that maintains a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the many possible disruptions that could occur over the 10,000 yr regulatory period that applies to the WIPP and subjective uncertainty arising from the imprecision with which many of the quantities required in the PA are known. Important parts of this structure are (1) the use of Latin hypercube sampling to incorporate the effects of subjective uncertainty, (2) the use of Monte Carlo (i.e., random) sampling to incorporate the effects of stochastic uncertainty, and (3) the efficient use of the necessarily limited number of mechanistic calculations that can be performed to support the analysis. The use of Latin hypercube sampling generates a mapping from imprecisely known analysis inputs to analysis outcomes of interest that provides both a display of the uncertainty in analysis outcomes (i.e., uncertainty analysis) and a basis for investigating the effects of individual inputs on these outcomes (i.e., sensitivity analysis). The sensitivity analysis procedures used in the PA include examination of scatterplots, stepwise regression analysis, and partial correlation analysis. Uncertainty and sensitivity analysis results obtained as part of the 1996 WIPP PA are presented and discussed. Specific topics considered include two phase flow in the vicinity of the repository, radionuclide release from the repository, fluid flow and radionuclide

Uncertainty and Sensitivity Analysis Results Obtained in the 1996 Performance Assessment for the Waste Isolation Pilot Plant

Energy Technology Data Exchange (ETDEWEB)

Bean, J.E.; Berglund, J.W.; Davis, F.J.; Economy, K.; Garner, J.W.; Helton, J.C.; Johnson, J.D.; MacKinnon, R.J.; Miller, J.; O' Brien, D.G.; Ramsey, J.L.; Schreiber, J.D.; Shinta, A.; Smith, L.N.; Stockman, C.; Stoelzel, D.M.; Vaughn, P.

1998-09-01

The Waste Isolation Pilot Plant (WPP) is located in southeastern New Mexico and is being developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of transuranic (TRU) waste. A detailed performance assessment (PA) for the WIPP was carried out in 1996 and supports an application by the DOE to the U.S. Environmental Protection Agency (EPA) for the certification of the WIPP for the disposal of TRU waste. The 1996 WIPP PA uses a computational structure that maintains a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the many possible disruptions that could occur over the 10,000 yr regulatory period that applies to the WIPP and subjective uncertainty arising from the imprecision with which many of the quantities required in the PA are known. Important parts of this structure are (1) the use of Latin hypercube sampling to incorporate the effects of subjective uncertainty, (2) the use of Monte Carlo (i.e., random) sampling to incorporate the effects of stochastic uncertainty, and (3) the efficient use of the necessarily limited number of mechanistic calculations that can be performed to support the analysis. The use of Latin hypercube sampling generates a mapping from imprecisely known analysis inputs to analysis outcomes of interest that provides both a display of the uncertainty in analysis outcomes (i.e., uncertainty analysis) and a basis for investigating the effects of individual inputs on these outcomes (i.e., sensitivity analysis). The sensitivity analysis procedures used in the PA include examination of scatterplots, stepwise regression analysis, and partial correlation analysis. Uncertainty and sensitivity analysis results obtained as part of the 1996 WIPP PA are presented and discussed. Specific topics considered include two phase flow in the vicinity of the repository, radionuclide release from the repository, fluid flow and radionuclide

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

Robustness of an uncertainty and sensitivity analysis of early exposure results with the MACCS reactor accident consequence model

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; McKay, M.D.; Shiver, A.W.; Sprung, J.L.

1995-01-01

Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis were used in an investigation with the MACCS model of the early health effects associated with a severe accident at a nuclear power station. The following results were obtained in tests to check the robustness of the analysis techniques: two independent Latin hypercube samples produced similar uncertainty and sensitivity analysis results; setting important variables to best-estimate values produced substantial reductions in uncertainty, while setting the less important variables to best-estimate values had little effect on uncertainty; similar sensitivity analysis results were obtained when the original uniform and loguniform distributions assigned to the 34 imprecisely known input variables were changed to left-triangular distributions and then to right-triangular distributions; and analyses with rank-transformed and logarithmically-transformed data produced similar results and substantially outperformed analyses with raw (i.e., untransformed) data

Validation of methods for WDXRF and OES-spark techniques in steel analysis. Determination of the uncertainty of measurements for API steel plant sample

International Nuclear Information System (INIS)

Silva, Carlos Eduardo da

2009-01-01

The increase of gas and petroleum demanding in the national and international markets, the specification and certification of the chemical analysis results of gas and oil pipe-line have became more and more challenging. A continuous development has been outlined to improve API grade alloy steels (American Petroleum Institute). The steel properties depend on the chemical composition control and process parameters during its manufacturing. In this work, a comparison of the measurement uncertainty between X-ray fluorescence (WD system) and Optical Emission (OES-spark) spectrometry, for API grade steel plant samples analysis, was outlined. Usually, this kind of analyze requires less than 40 seconds for full chemical characterization for adjustment in the process parameters production. The main influence source in the chemical analysis was evaluated for design of experiments. The constituents and trace elements such as Al, Si, P, S, Ti, V, Cr, Mn, Co, Ni, Cu, As, Nb, Mo and Sn were determined using the ASTM E-322, E-415, E-1009 and E-1085 standard methodologies. The 185A and 187A certified reference materials from CMI (Czech Metrology Institute) were used for evaluation of the methods. The uncertainty of the measurement, precision, accuracy, repeatability and reproducibility of the measurements were obtained applying statistic tests, recommended by ISO/IEC 17025. The uncertainty of measurement for each element is discussed for both techniques. (author)

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...

Analysis of uncertainties caused by the atmospheric dispersion model in accident consequence assessments with UFOMOD

International Nuclear Information System (INIS)

Fischer, F.; Ehrhardt, J.

1988-06-01

Various techniques available for uncertainty analysis of large computer models are applied, described and selected as most appropriate for analyzing the uncertainty in the predictions of accident consequence assessments. The investigation refers to the atmospheric dispersion and deposition submodel (straight-line Gaussian plume model) of UFOMOD, whose most important input variables and parameters are linked with probability distributions derived from expert judgement. Uncertainty bands show how much variability exists, sensitivity measures determine what causes this variability in consequences. Results are presented as confidence bounds of complementary cumulative frequency distributions (CCFDs) of activity concentrations, organ doses and health effects, partially as a function of distance from the site. In addition the ranked influence of the uncertain parameters on the different consequence types is shown. For the estimation of confidence bounds it was sufficient to choose a model parameter sample size of n (n=59) equal to 1.5 times the number of uncertain model parameters. Different samples or an increase of sample size did not change the 5%-95% - confidence bands. To get statistically stable results of the sensitivity analysis, larger sample sizes are needed (n=100, 200). Random or Latin-hypercube sampling schemes as tools for uncertainty and sensitivity analyses led to comparable results. (orig.) [de

Bayesian analysis for uncertainty estimation of a canopy transpiration model

Science.gov (United States)

Samanta, S.; Mackay, D. S.; Clayton, M. K.; Kruger, E. L.; Ewers, B. E.

2007-04-01

A Bayesian approach was used to fit a conceptual transpiration model to half-hourly transpiration rates for a sugar maple (Acer saccharum) stand collected over a 5-month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman-Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.

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

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.

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.

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.

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.

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

Uncertainty and Sensitivity Analysis Applied to the Validation of BWR Bundle Thermal-Hydraulic Calculations

International Nuclear Information System (INIS)

Hernandez-Solis, Augusto

2010-04-01

This work has two main objectives. The first one is to enhance the validation process of the thermal-hydraulic features of the Westinghouse code POLCA-T. This is achieved by computing a quantitative validation limit based on statistical uncertainty analysis. This validation theory is applied to some of the benchmark cases of the following macroscopic BFBT exercises: 1) Single and two phase bundle pressure drops, 2) Steady-state cross-sectional averaged void fraction, 3) Transient cross-sectional averaged void fraction and 4) Steady-state critical power tests. Sensitivity analysis is also performed to identify the most important uncertain parameters for each exercise. The second objective consists in showing the clear advantages of using the quasi-random Latin Hypercube Sampling (LHS) strategy over simple random sampling (SRS). LHS allows a much better coverage of the input uncertainties than SRS because it densely stratifies across the range of each input probability distribution. The aim here is to compare both uncertainty analyses on the BWR assembly void axial profile prediction in steady-state, and on the transient void fraction prediction at a certain axial level coming from a simulated re-circulation pump trip scenario. It is shown that the replicated void fraction mean (either in steady-state or transient conditions) has less variability when using LHS than SRS for the same number of calculations (i.e. same input space sample size) even if the resulting void fraction axial profiles are non-monotonic. It is also shown that the void fraction uncertainty limits achieved with SRS by running 458 calculations (sample size required to cover 95% of 8 uncertain input parameters with a 95% confidence), result in the same uncertainty limits achieved by LHS with only 100 calculations. These are thus clear indications on the advantages of using LHS. Finally, the present study contributes to a realistic analysis of nuclear reactors, in the sense that the uncertainties of

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)

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.

Hydrocoin level 3 - Testing methods for sensitivity/uncertainty analysis

International Nuclear Information System (INIS)

Grundfelt, B.; Lindbom, B.; Larsson, A.; Andersson, K.

1991-01-01

The HYDROCOIN study is an international cooperative project for testing groundwater hydrology modelling strategies for performance assessment of nuclear waste disposal. The study was initiated in 1984 by the Swedish Nuclear Power Inspectorate and the technical work was finalised in 1987. The participating organisations are regulatory authorities as well as implementing organisations in 10 countries. The study has been performed at three levels aimed at studying computer code verification, model validation and sensitivity/uncertainty analysis respectively. The results from the first two levels, code verification and model validation, have been published in reports in 1988 and 1990 respectively. This paper focuses on some aspects of the results from Level 3, sensitivity/uncertainty analysis, for which a final report is planned to be published during 1990. For Level 3, seven test cases were defined. Some of these aimed at exploring the uncertainty associated with the modelling results by simply varying parameter values and conceptual assumptions. In other test cases statistical sampling methods were applied. One of the test cases dealt with particle tracking and the uncertainty introduced by this type of post processing. The amount of results available is substantial although unevenly spread over the test cases. It has not been possible to cover all aspects of the results in this paper. Instead, the different methods applied will be illustrated by some typical analyses. 4 figs., 9 refs

Geological-structural models used in SR 97. Uncertainty analysis

Energy Technology Data Exchange (ETDEWEB)

Saksa, P.; Nummela, J. [FINTACT Oy (Finland)

1998-10-01

The uncertainty of geological-structural models was studied for the three sites in SR 97, called Aberg, Beberg and Ceberg. The evaluation covered both regional and site scale models, the emphasis being placed on fracture zones in the site scale. Uncertainty is a natural feature of all geoscientific investigations. It originates from measurements (errors in data, sampling limitations, scale variation) and conceptualisation (structural geometries and properties, ambiguous geometric or parametric solutions) to name the major ones. The structures of A-, B- and Ceberg are fracture zones of varying types. No major differences in the conceptualisation between the sites were noted. One source of uncertainty in the site models is the non-existence of fracture and zone information in the scale from 10 to 300 - 1000 m. At Aberg the development of the regional model has been performed very thoroughly. At the site scale one major source of uncertainty is that a clear definition of the target area is missing. Structures encountered in the boreholes are well explained and an interdisciplinary approach in interpretation have taken place. Beberg and Ceberg regional models contain relatively large uncertainties due to the investigation methodology and experience available at that time. In site scale six additional structures were proposed both to Beberg and Ceberg to variant analysis of these sites. Both sites include uncertainty in the form of many non-interpreted fractured sections along the boreholes. Statistical analysis gives high occurrences of structures for all three sites: typically 20 - 30 structures/km{sup 3}. Aberg has highest structural frequency, Beberg comes next and Ceberg has the lowest. The borehole configuration, orientations and surveying goals were inspected to find whether preferences or factors causing bias were present. Data from Aberg supports the conclusion that Aespoe sub volume would be an anomalously fractured, tectonised unit of its own. This means that

Geological-structural models used in SR 97. Uncertainty analysis

International Nuclear Information System (INIS)

Saksa, P.; Nummela, J.

1998-10-01

The uncertainty of geological-structural models was studied for the three sites in SR 97, called Aberg, Beberg and Ceberg. The evaluation covered both regional and site scale models, the emphasis being placed on fracture zones in the site scale. Uncertainty is a natural feature of all geoscientific investigations. It originates from measurements (errors in data, sampling limitations, scale variation) and conceptualisation (structural geometries and properties, ambiguous geometric or parametric solutions) to name the major ones. The structures of A-, B- and Ceberg are fracture zones of varying types. No major differences in the conceptualisation between the sites were noted. One source of uncertainty in the site models is the non-existence of fracture and zone information in the scale from 10 to 300 - 1000 m. At Aberg the development of the regional model has been performed very thoroughly. At the site scale one major source of uncertainty is that a clear definition of the target area is missing. Structures encountered in the boreholes are well explained and an interdisciplinary approach in interpretation have taken place. Beberg and Ceberg regional models contain relatively large uncertainties due to the investigation methodology and experience available at that time. In site scale six additional structures were proposed both to Beberg and Ceberg to variant analysis of these sites. Both sites include uncertainty in the form of many non-interpreted fractured sections along the boreholes. Statistical analysis gives high occurrences of structures for all three sites: typically 20 - 30 structures/km 3 . Aberg has highest structural frequency, Beberg comes next and Ceberg has the lowest. The borehole configuration, orientations and surveying goals were inspected to find whether preferences or factors causing bias were present. Data from Aberg supports the conclusion that Aespoe sub volume would be an anomalously fractured, tectonised unit of its own. This means that the

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

Uncertainties combined in algae and water in chemical analysis in determinations with ICP-OES

International Nuclear Information System (INIS)

Souza, Poliana Santos de

2014-01-01

One way to determine if some trace elements in algae and water is through uncertainty calculations. Spectrometry and inductively coupled plasma optical emission (ICP-OES) is widely used in this procedure, because it allows the analysis in waters and areas of solid samples. Thus, some elements (Fe, Ca and Mg) were used to calculate the uncertainty. (author)

Systematic and non-systematic effects of the uncertainty of the sample position in gamma-ray spectrometry

International Nuclear Information System (INIS)

Vidmar, T.; Korun, M.

2004-01-01

When cylindrical samples placed coaxially with the detector are measured on a gamma-ray spectrometer, the position of the sample very often deviates from an ideal one with the axes of the sample and the detector less than perfectly aligned. If a calibrated source is used prior to the measurement and is presumed to have been positioned correctly, one might conclude that the misalignment of the measured sample should result in an uncertainty of the reported nuclide activity, since the efficiencies of the sample and the calibrated source are effectively different due to the difference in placement. The efficiency of a displaced cylindrical sample, however, is always lower than the one of a sample that is perfectly aligned. The net effect of misalignment can therefore be not only an increase in the uncertainty of the activity, but also a systematic error in its evaluation. Since the Guide to the Expression of Uncertainty in Measurement requires that all such systematic effects be corrected for, we have developed a method to assess the change in the efficiency resulting from misalignment and to introduce the required correction. The calculation of this correction only requires knowledge of basic sample and detector data. The uncertainty of the reported activity can then also be assessed and is influenced by the uncertainty of the efficiency evaluated around its new, corrected value. An appropriate expression for this uncertainty has been derived

Procedures for uncertainty and sensitivity analysis in repository performance assessment

International Nuclear Information System (INIS)

Poern, K.; Aakerlund, O.

1985-10-01

The objective of the project was mainly a literature study of available methods for the treatment of parameter uncertainty propagation and sensitivity aspects in complete models such as those concerning geologic disposal of radioactive waste. The study, which has run parallel with the development of a code package (PROPER) for computer assisted analysis of function, also aims at the choice of accurate, cost-affective methods for uncertainty and sensitivity analysis. Such a choice depends on several factors like the number of input parameters, the capacity of the model and the computer reresources required to use the model. Two basic approaches are addressed in the report. In one of these the model of interest is directly simulated by an efficient sampling technique to generate an output distribution. Applying the other basic method the model is replaced by an approximating analytical response surface, which is then used in the sampling phase or in moment matching to generate the output distribution. Both approaches are illustrated by simple examples in the report. (author)

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

Validation of methodology and uncertainty assessment of antimony determination in environmental materials using Neutron Activation Analysis

International Nuclear Information System (INIS)

Matsubara, Tassiane C.M.; Saiki, Mitiko; Zahn, Guilherme S.; Moreira, Edson G.

2013-01-01

Antimony is an element found in low concentrations in the environment. However, its determination has attracted great interest because of the knowledge of its toxicity and increasing application. Neutron activation analysis (NAA) is a suitable method for the determination of several elements in different types, but in case of Sb, the analysis presents some difficulties due to spectral interferences. The objective of this research was to validate the method of NAA and uncertainty assessment for Sb determination in environmental samples. The experimental procedure consisted of irradiating twelve certified reference samples of different kind of matrices. The samples were irradiated in the nuclear research reactor IEA R1 IPEN/CNEN/SP followed by measurement of induced radioactivity, using a hyperpure germanium detector coupled to a gamma ray spectrometry. The radioisotopes 122 Sb and 124 Sb were measured and the Sb concentrations with their respective uncertainties were obtained by the comparative method. Relative errors and values of Z scores were calculated to evaluate the accuracy of the results for Sb determination in certified reference materials. The evaluation of the components that contribute to uncertainty measurement of the Sb concentration, showed that the major uncertainty contribution is due to statistical counting. The results also indicated that the uncertainty value of the combined standard uncertainty depends on the radioisotope measured and the decay time used for counting. (author)

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

Influence of short-term sampling parameters on the uncertainty of the Lden environmental noise indicator

International Nuclear Information System (INIS)

Mateus, M; Carrilho, J Dias; Da Silva, M Gameiro

2015-01-01

The present study deals with the influence of the sampling parameters on the uncertainty of noise equivalent level in environmental noise measurements. The study has been carried out through the test of different sampling strategies doing resampling trials over continuous monitoring noise files obtained previously in an urban location in the city of Coimbra, in Portugal. On short term measurements, not only the duration of the sampling episodes but also its number have influence on the uncertainty of the result. This influence is higher for the time periods where sound levels suffer a greater variation, such as during the night period. In this period, in case both parameters (duration and number of sampling episodes) are not carefully selected, the uncertainty level can reach too high values contributing to a loss of precision of the measurements. With the obtained data it was investigated the sampling parameters influence on the long term noise indicator uncertainty, calculated according the Draft 1st CD ISO 1996-2:2012 proposed method. It has been verified that this method allows the possibility of defining a general methodology which enables the setting of the parameters once the precision level is fixed. For the three reference periods defined for environmental noise (day, evening and night), it was possible to derive a two variable power law representing the uncertainty of the determined values as a function of the two sampling parameters: duration of sampling episode and number of episodes

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...

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)

Statistical Analysis Of Tank 19F Floor Sample Results

International Nuclear Information System (INIS)

Harris, S.

2010-01-01

Representative sampling has been completed for characterization of the residual material on the floor of Tank 19F as per the statistical sampling plan developed by Harris and Shine. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples results to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL95%) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current scrape sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 19F. The uncertainty is quantified in this report by an UCL95% on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL95% was based entirely on the six current scrape sample results (each averaged across three analytical determinations).

Demonstration uncertainty/sensitivity analysis using the health and economic consequence model CRAC2

International Nuclear Information System (INIS)

Alpert, D.J.; Iman, R.L.; Johnson, J.D.; Helton, J.C.

1984-12-01

The techniques for performing uncertainty/sensitivity analyses compiled as part of the MELCOR program appear to be well suited for use with a health and economic consequence model. Two replicate samples of size 50 gave essentially identical results, indicating that for this case, a Latin hypercube sample of size 50 seems adequate to represent the distribution of results. Though the intent of this study was a demonstration of uncertainty/sensitivity analysis techniques, a number of insights relevant to health and economic consequence modeling can be gleaned: uncertainties in early deaths are significantly greater than uncertainties in latent cancer deaths; though the magnitude of the source term is the largest source of variation in estimated distributions of early deaths, a number of additional parameters are also important; even with the release fractions for a full SST1, one quarter of the CRAC2 runs gave no early deaths; and comparison of the estimates of mean early deaths for a full SST1 release in this study with those of recent point estimates for similar conditions indicates that the recent estimates may be significant overestimations of early deaths. Estimates of latent cancer deaths, however, are roughly comparable. An analysis of the type described here can provide insights in a number of areas. First, the variability in the results gives an indication of the potential uncertainty associated with the calculations. Second, the sensitivity of the results to assumptions about the input variables can be determined. Research efforts can then be concentrated on reducing the uncertainty in the variables which are the largest contributors to uncertainty in results

Evaluation of the uncertainty associated with sample holders in NAA measurements in LAN/IPEN

Energy Technology Data Exchange (ETDEWEB)

Zahn, Guilherme S.; Ticianelli, Regina B.; Saiki, Mitiko; Genezini, Frederico A., E-mail: ticianelli@ipen.br [Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP), São Paulo, SP (Brazil)

2017-07-01

In IPEN's Neutron Activation Laboratory (LAN/IPEN), thin stainless steel sample holders are used for gamma spectrometry in NAA measurements. This material is very practical, but its chemical composition may be troublesome, as it presents large amounts of elements with intermediate atomic number, with attenuation factors for low-energy gamma-rays that must not be neglected. In this study, count rates obtained using different sample holders were compared. To accomplish that, an Am-241 source, with 59-keV gamma emission, was used so that low-energy gamma attenuation differences can be determined. Moreover, in order to study the energy dependence of these differences, a Ho-166m source was also used. From these results, it was possible to analyze the experimental error associated to the variations between sample holders, with the aim of introducing an addictive term to the uncertainty analysis of comparative Neutron Activation Analysis results. (author)

Accounting for sensor calibration, data validation, measurement and sampling uncertainties in monitoring urban drainage systems.

Science.gov (United States)

Bertrand-Krajewski, J L; Bardin, J P; Mourad, M; Béranger, Y

2003-01-01

Assessing the functioning and the performance of urban drainage systems on both rainfall event and yearly time scales is usually based on online measurements of flow rates and on samples of influent effluent for some rainfall events per year. In order to draw pertinent scientific and operational conclusions from the measurement results, it is absolutely necessary to use appropriate methods and techniques in order to i) calibrate sensors and analytical methods, ii) validate raw data, iii) evaluate measurement uncertainties, iv) evaluate the number of rainfall events to sample per year in order to determine performance indicator with a given uncertainty. Based an previous work, the paper gives a synthetic review of required and techniques, and illustrates their application to storage and settling tanks. Experiments show that, controlled and careful experimental conditions, relative uncertainties are about 20% for flow rates in sewer pipes, 6-10% for volumes, 25-35% for TSS concentrations and loads, and 18-276% for TSS removal rates. In order to evaluate the annual pollutant interception efficiency of storage and settling tanks with a given uncertainty, efforts should first be devoted to decrease the sampling uncertainty by increasing the number of sampled events.

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...

Estimating the uncertainty from sampling in pollution crime investigation: The importance of metrology in the forensic interpretation of environmental data.

Science.gov (United States)

Barazzetti Barbieri, Cristina; de Souza Sarkis, Jorge Eduardo

2018-07-01

The forensic interpretation of environmental analytical data is usually challenging due to the high geospatial variability of these data. The measurements' uncertainty includes contributions from the sampling and from the sample handling and preparation processes. These contributions are often disregarded in analytical techniques results' quality assurance. A pollution crime investigation case was used to carry out a methodology able to address these uncertainties in two different environmental compartments, freshwater sediments and landfill leachate. The methodology used to estimate the uncertainty was the duplicate method (that replicates predefined steps of the measurement procedure in order to assess its precision) and the parameters used to investigate the pollution were metals (Cr, Cu, Ni, and Zn) in the leachate, the suspect source, and in the sediment, the possible sink. The metal analysis results were compared to statutory limits and it was demonstrated that Cr and Ni concentrations in sediment samples exceeded the threshold levels at all sites downstream the pollution sources, considering the expanded uncertainty U of the measurements and a probability of contamination >0.975, at most sites. Cu and Zn concentrations were above the statutory limits at two sites, but the classification was inconclusive considering the uncertainties of the measurements. Metal analyses in leachate revealed that Cr concentrations were above the statutory limits with a probability of contamination >0.975 in all leachate ponds while the Cu, Ni and Zn probability of contamination was below 0.025. The results demonstrated that the estimation of the sampling uncertainty, which was the dominant component of the combined uncertainty, is required for a comprehensive interpretation of the environmental analyses results, particularly in forensic cases. Copyright © 2018 Elsevier B.V. All rights reserved.

Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad; Janssen, Hans

2015-02-01

The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of midpoints in the hypercube intervals (midpoint LHS). Both approaches have been extensively used, but no attempt has been previously made to compare the efficiency and robustness of their resulting sample designs. In this study we compare the two approaches and show that the space-filling characteristics of OLHS designs are sensitive to the initial design that is fed into the optimization algorithm. It is also illustrated that the space-filling characteristics of OLHS designs based on midpoint LHS are significantly better those based on random LHS. The two approaches are compared by incorporating their resulting sample designs in Monte Carlo simulation (MCS) for uncertainty propagation analysis, and then, by employing the sample designs in the selection of the training set for constructing non-intrusive polynomial chaos expansion (NIPCE) meta-models which subsequently replace the original full model in MCSs. The analysis is based on two case studies involving numerical simulation of density dependent flow and solute transport in porous media within the context of seawater intrusion in coastal aquifers. We show that the use of midpoint LHS as the initial design increases the efficiency and robustness of the resulting MCSs and NIPCE meta-models. The study also illustrates that this

PCT Uncertainty Analysis Using Unscented Transform with Random Orthogonal Matrix

Energy Technology Data Exchange (ETDEWEB)

Fynana, Douglas A.; Ahn, Kwang-Il [KAERI, Daejeon (Korea, Republic of); Lee, John C. [Univ. of Michigan, Michigan (United States)

2015-05-15

Most Best Estimate Plus Uncertainty (BEPU) methods employ nonparametric order statistics through Wilks' formula to quantify uncertainties of best estimate simulations of nuclear power plant (NPP) transients. 95%/95% limits, the 95''t{sup h} percentile at a 95% confidence level, are obtained by randomly sampling all uncertainty contributors through conventional Monte Carlo (MC). Advantages are simple implementation of MC sampling of input probability density functions (pdfs) and limited computational expense of 1''s{sup t}, 2''n{sup d}, and 3''r{sup d} order Wilks' formula requiring only 59, 93, or 124 simulations, respectively. A disadvantage of small sample size is large sample to sample variation of statistical estimators. This paper presents a new efficient sampling based algorithm for accurate estimation of mean and variance of the output parameter pdf. The algorithm combines a deterministic sampling method, the unscented transform (UT), with random sampling through the generation of a random orthogonal matrix (ROM). The UT guarantees the mean, covariance, and 3''r{sup d} order moments of the multivariate input parameter distributions are exactly preserved by the sampled input points and the orthogonal transformation of the points by a ROM guarantees the sample error of all 4''t{sup h} order and higher moments are unbiased. The UT with ROM algorithm is applied to the uncertainty quantification of the peak clad temperature (PCT) during a large break loss-of-coolant accident (LBLOCA) in an OPR1000 NPP to demonstrate the applicability of the new algorithm to BEPU. This paper presented a new algorithm combining the UT with ROM for efficient multivariate parameter sampling that ensures sample input covariance and 3''r{sup d} order moments are exactly preserved and 4''th moment errors are small and unbiased. The advantageous sample properties guarantee higher order accuracy and

Traceability and measurement uncertainty in sample preparation (W5)

International Nuclear Information System (INIS)

Wegscheider, W.; Walner, U.; Moser, J.

2002-01-01

Full text: Very few chemical measurements are being made directly on the object of interest and sample preparation is thus the rule rather than the exception in daily practice. Unfortunately the operations undertaken in the course of sample preparation are prone to rendering a sample useless for the purpose of interpreting a measurement performed on it, as it might not represent the original and relevant status any longer. Sample preparation along with sampling itself constitutes therefore a procedure that leads to a loss of representation of the original specimen or population. On the other hand it is also not sufficient to confine aspects of traceability and measurement uncertainty to the ultimate measurement, as the key purpose of measuring is to supply adequate data for some kind of decision, be it in production, in health, in the environment, or indeed in any other circumstance. These considerations have led to severe confusion in the community as to what traceability really means in chemistry. CITAC and EURACHEM have only recently issued a preliminary document that clarifies these issues and gives a firm handle on the future development of quality assurance in analytical chemistry. In this talk it will be attempted to outline the general ideas and procedures that lead to traceability of analytical chemical results accompanied by valid statements of their uncertainty. It will be argued that the central element in achieving these goals is a well-designed validation study that frequently goes beyond those requirements currently laid out in official documents. (author)

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

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)

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.

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 measurement in the homogenization and sample reduction in the physical classification of rice and beans

Directory of Open Access Journals (Sweden)

Dieisson Pivoto

2016-04-01

Full Text Available ABSTRACT: The study aimed to i quantify the measurement uncertainty in the physical tests of rice and beans for a hypothetical defect, ii verify whether homogenization and sample reduction in the physical classification tests of rice and beans is effective to reduce the measurement uncertainty of the process and iii determine whether the increase in size of beans sample increases accuracy and reduces measurement uncertainty in a significant way. Hypothetical defects in rice and beans with different damage levels were simulated according to the testing methodology determined by the Normative Ruling of each product. The homogenization and sample reduction in the physical classification of rice and beans are not effective, transferring to the final test result a high measurement uncertainty. The sample size indicated by the Normative Ruling did not allow an appropriate homogenization and should be increased.

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

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

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.

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

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

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)

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.)

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.

Assessment of Measurement Uncertainty Values of the Scandium Determination in Marine Sediment

International Nuclear Information System (INIS)

Rina-Mulyaningsih, Th.

2005-01-01

The result value of testing is meaningless if it isn't completed with uncertainty value. So that with the analysis result Sc in the marine sediment sample. It was assessed the uncertainty measurement of Sc analysis in marine sediment. The experiment was done in AAN Serpong laboratory. The result of calculation uncertainty on Sc analysis showed that the uncertainty components come from: preparation of sample and standard/comparator, purity of standard, counting statistics (sample and standard), repeatability, nuclear data and decay correction. The assessment on uncertainty must be done for the analysis of others elements, because each elements has difference nuclear and physical properties. (author)

Uncertainty and sensitivity analysis in the scenario simulation with RELAP/SCDAP and MELCOR codes

International Nuclear Information System (INIS)

Garcia J, T.; Cardenas V, J.

2015-09-01

A methodology was implemented for analysis of uncertainty in simulations of scenarios with RELAP/SCDAP V- 3.4 bi-7 and MELCOR V-2.1 codes, same that are used to perform safety analysis in the Comision Nacional de Seguridad Nuclear y Salvaguardias (CNSNS). The uncertainty analysis methodology chosen is a probabilistic method of type Propagation of uncertainty of the input parameters to the departure parameters. Therefore, it began with the selection of the input parameters considered uncertain and are considered of high importance in the scenario for its direct effect on the output interest variable. These parameters were randomly sampled according to intervals of variation or probability distribution functions assigned by expert judgment to generate a set of input files that were run through the simulation code to propagate the uncertainty to the output parameters. Then, through the use or ordered statistical and formula Wilks, was determined that the minimum number of executions required to obtain the uncertainty bands that include a population of 95% at a confidence level of 95% in the results is 93, is important to mention that in this method that number of executions does not depend on the number of selected input parameters. In the implementation routines in Fortran 90 that allowed automate the process to make the uncertainty analysis in transients for RELAP/SCDAP code were generated. In the case of MELCOR code for severe accident analysis, automation was carried out through complement Dakota Uncertainty incorporated into the Snap platform. To test the practical application of this methodology, two analyzes were performed: the first with the simulation of closing transient of the main steam isolation valves using the RELAP/SCDAP code obtaining the uncertainty band of the dome pressure of the vessel; while in the second analysis, the accident simulation of the power total loss (Sbo) was carried out with the Macarol code obtaining the uncertainty band for the

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.

Total sensitivity and uncertainty analysis for LWR pin-cells with improved UNICORN code

International Nuclear Information System (INIS)

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

2017-01-01

Highlights: • A new model is established for the total sensitivity and uncertainty analysis. • The NR approximation applied in S&U analysis can be avoided by the new model. • Sensitivity and uncertainty analysis is performed to PWR pin-cells by the new model. • The effects of the NR approximation for the PWR pin-cells are quantified. - Abstract: In this paper, improvements to the multigroup cross-section perturbation model have been proposed and applied in the self-developed UNICORN code, which is capable of performing the total sensitivity and total uncertainty analysis for the neutron-physics calculations by applying the direct numerical perturbation method and the statistical sampling method respectively. The narrow resonance (NR) approximation was applied in the multigroup cross-section perturbation model, implemented in UNICORN. As improvements to the NR approximation to refine the multigroup cross-section perturbation model, an ultrafine-group cross-section perturbation model has been established, in which the actual perturbations are applied to the ultrafine-group cross-section library and the reconstructions of the resonance cross sections are performed by solving the neutron slowing-down equation. The total sensitivity and total uncertainty analysis were then applied to the LWR pin-cells, using both the multigroup and the ultrafine-group cross-section perturbation models. The numerical results show that the NR approximation overestimates the relative sensitivity coefficients and the corresponding uncertainty results for the LWR pin-cells, and the effects of the NR approximation are significant for σ_(_n_,_γ_) and σ_(_n_,_e_l_a_s_) of "2"3"8U. Therefore, the effects of the NR approximation applied in the total sensitivity and total uncertainty analysis for the neutron-physics calculations of LWR should be taken into account.

STATISTICAL ANALYSIS OF TANK 18F FLOOR SAMPLE RESULTS

Energy Technology Data Exchange (ETDEWEB)

Harris, S.

2010-09-02

Representative sampling has been completed for characterization of the residual material on the floor of Tank 18F as per the statistical sampling plan developed by Shine [1]. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL [2]. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples results [3] to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL{sub 95%}) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 18F. The uncertainty is quantified in this report by an upper 95% confidence limit (UCL{sub 95%}) on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL{sub 95%} was based entirely on the six current scrape sample results (each averaged across three analytical determinations).

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

Uncertainty and sensitivity analysis methodology in a level-I PSA (Probabilistic Safety Assessment)

International Nuclear Information System (INIS)

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

1997-01-01

This work presents a methodology for sensitivity and uncertainty analysis, applicable to a probabilistic safety assessment level I. The work contents are: correct association of distributions to parameters, importance and qualification of expert opinions, generations of samples according to sample sizes, and study of the relationships among system variables and system response. A series of statistical-mathematical techniques are recommended along the development of the analysis methodology, as well different graphical visualization for the control of the study. (author) [es

The effect of uncertainty of reactor parameters obtained using k0-NAA on result of analysis

International Nuclear Information System (INIS)

Sasajima, Fumio

2006-01-01

Neutron Activation Analysis using the k 0 method is a useful method allowing convenient and accurate simultaneous analysis of plural elements, eliminating the need for the use of comparative reference samples. As already well known, it is essential for the correct result of an analysis to obtain the α-factor and f-factor for a neutron spectrum in an irradiation field accurately when an attempt is made to use the k 0 method. For this reason, based on data obtained from the experiment conducted in the JRR-3 PN-3 system, how uncertainty of the measured values for α-factor and f-factor affects the result of an analysis was evaluated. The process of evaluation involved intentionally varying the values for reactor parameters followed by making an analysis of environmental reference samples (NIST SRM-1632c) using the k 0 method to examine the effect of these factors on the concentrations of 19 elements. The result of the evaluation revealed that the degree of the effect of uncertainty on the concentrations of 19 elements was at best approx. 1% under the condition of this experiment assuming that the factor α, a reactor parameter, had uncertainty of approx. 200%. (author)

Artificial intelligence metamodel comparison and application to wind turbine airfoil uncertainty analysis

Directory of Open Access Journals (Sweden)

Yaping Ju

2016-05-01

Full Text Available The Monte Carlo simulation method for turbomachinery uncertainty analysis often requires performing a huge number of simulations, the computational cost of which can be greatly alleviated with the help of metamodeling techniques. An intensive comparative study was performed on the approximation performance of three prospective artificial intelligence metamodels, that is, artificial neural network, radial basis function, and support vector regression. The genetic algorithm was used to optimize the predetermined parameters of each metamodel for the sake of a fair comparison. Through testing on 10 nonlinear functions with different problem scales and sample sizes, the genetic algorithm–support vector regression metamodel was found more accurate and robust than the other two counterparts. Accordingly, the genetic algorithm–support vector regression metamodel was selected and combined with the Monte Carlo simulation method for the uncertainty analysis of a wind turbine airfoil under two types of surface roughness uncertainties. The results show that the genetic algorithm–support vector regression metamodel can capture well the uncertainty propagation from the surface roughness to the airfoil aerodynamic performance. This work is useful to the application of metamodeling techniques in the robust design optimization of turbomachinery.

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

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

Uncertainty relation and probability. Numerical illustration

International Nuclear Information System (INIS)

Fujikawa, Kazuo; Umetsu, Koichiro

2011-01-01

The uncertainty relation and the probability interpretation of quantum mechanics are intrinsically connected, as is evidenced by the evaluation of standard deviations. It is thus natural to ask if one can associate a very small uncertainty product of suitably sampled events with a very small probability. We have shown elsewhere that some examples of the evasion of the uncertainty relation noted in the past are in fact understood in this way. We here numerically illustrate that a very small uncertainty product is realized if one performs a suitable sampling of measured data that occur with a very small probability. We introduce a notion of cyclic measurements. It is also shown that our analysis is consistent with the Landau-Pollak-type uncertainty relation. It is suggested that the present analysis may help reconcile the contradicting views about the 'standard quantum limit' in the detection of gravitational waves. (author)

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.

Uncertainty analysis of a nondestructive radioassay system for transuranic waste

International Nuclear Information System (INIS)

Harker, Y.D.; Blackwood, L.G.; Meachum, T.R.; Yoon, W.Y.

1996-01-01

Radioassay of transuranic waste in 207 liter drums currently stored at the Idaho National Engineering Laboratory is achieved using a Passive Active Neutron (PAN) nondestructive assay system. In order to meet data quality assurance requirements for shipping and eventual permanent storage of these drums at the Waste Isolation Pilot Plant in Carlsbad, New Mexico, the total uncertainty of the PAN system measurements must be assessed. In particular, the uncertainty calculations are required to include the effects of variations in waste matrix parameters and related variables on the final measurement results. Because of the complexities involved in introducing waste matrix parameter effects into the uncertainty calculations, standard methods of analysis (e.g., experimentation followed by propagation of errors) could not be implemented. Instead, a modified statistical sampling and verification approach was developed. In this modified approach the total performance of the PAN system is simulated using computer models of the assay system and the resultant output is compared with the known input to assess the total uncertainty. This paper describes the simulation process and illustrates its application to waste comprised of weapons grade plutonium-contaminated graphite molds

Use of probabilistic methods for analysis of cost and duration uncertainties in a decision analysis framework

International Nuclear Information System (INIS)

Boak, D.M.; Painton, L.

1995-01-01

Probabilistic forecasting techniques have been used in many risk assessment and performance assessment applications on radioactive waste disposal projects such as Yucca Mountain and the Waste Isolation Pilot Plant (WIPP). Probabilistic techniques such as Monte Carlo and Latin Hypercube sampling methods are routinely used to treat uncertainties in physical parameters important in simulating radionuclide transport in a coupled geohydrologic system and assessing the ability of that system to comply with regulatory release limits. However, the use of probabilistic techniques in the treatment of uncertainties in the cost and duration of programmatic alternatives on risk and performance assessment projects is less common. Where significant uncertainties exist and where programmatic decisions must be made despite existing uncertainties, probabilistic techniques may yield important insights into decision options, especially when used in a decision analysis framework and when properly balanced with deterministic analyses. For relatively simple evaluations, these types of probabilistic evaluations can be made using personal computer-based software

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.

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.

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.

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)

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.

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

Review of best estimate plus uncertainty methods of thermal-hydraulic safety analysis

International Nuclear Information System (INIS)

Prosek, A.; Mavko, B.

2003-01-01

In 1988 United States Nuclear Regulatory Commission approved the revised rule on the acceptance of emergency core cooling system (ECCS) performance. Since that there has been significant interest in the development of codes and methodologies for best-estimate loss-of-coolant accident (LOCAs) analyses. Several new best estimate plus uncertainty methods (BEPUs) were developed in the world. The purpose of the paper is to review the developments in the direction of best estimate approaches with uncertainty quantification and to discuss the problems in practical applications of BEPU methods. In general, the licensee methods are following original methods. The study indicated that uncertainty analysis with random sampling of input parameters and the use of order statistics for desired tolerance limits of output parameters is today commonly accepted and mature approach. (author)

Incorporating parametric uncertainty into population viability analysis models

Science.gov (United States)

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

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...

Measuring and explaining eco-efficiencies of wastewater treatment plants in China: An uncertainty analysis perspective.

Science.gov (United States)

Dong, Xin; Zhang, Xinyi; Zeng, Siyu

2017-04-01

In the context of sustainable development, there has been an increasing requirement for an eco-efficiency assessment of wastewater treatment plants (WWTPs). Data envelopment analysis (DEA), a technique that is widely applied for relative efficiency assessment, is used in combination with the tolerances approach to handle WWTPs' multiple inputs and outputs as well as their uncertainty. The economic cost, energy consumption, contaminant removal, and global warming effect during the treatment processes are integrated to interpret the eco-efficiency of WWTPs. A total of 736 sample plants from across China are assessed, and large sensitivities to variations in inputs and outputs are observed for most samples, with only three WWTPs identified as being stably efficient. Size of plant, overcapacity, climate type, and influent characteristics are proven to have a significant influence on both the mean efficiency and performance sensitivity of WWTPs, while no clear relationships were found between eco-efficiency and technology under the framework of uncertainty analysis. The incorporation of uncertainty quantification and environmental impact consideration has improved the liability and applicability of the assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Two-Step Approach to Uncertainty Quantification of Core Simulators

Directory of Open Access Journals (Sweden)

Artem Yankov

2012-01-01

Full Text Available For the multiple sources of error introduced into the standard computational regime for simulating reactor cores, rigorous uncertainty analysis methods are available primarily to quantify the effects of cross section uncertainties. Two methods for propagating cross section uncertainties through core simulators are the XSUSA statistical approach and the “two-step” method. The XSUSA approach, which is based on the SUSA code package, is fundamentally a stochastic sampling method. Alternatively, the two-step method utilizes generalized perturbation theory in the first step and stochastic sampling in the second step. The consistency of these two methods in quantifying uncertainties in the multiplication factor and in the core power distribution was examined in the framework of phase I-3 of the OECD Uncertainty Analysis in Modeling benchmark. With the Three Mile Island Unit 1 core as a base model for analysis, the XSUSA and two-step methods were applied with certain limitations, and the results were compared to those produced by other stochastic sampling-based codes. Based on the uncertainty analysis results, conclusions were drawn as to the method that is currently more viable for computing uncertainties in burnup and transient calculations.

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.

Uncertainty and sensitivity analysis in the neutronic parameters generation for BWR and PWR coupled thermal-hydraulic–neutronic simulations

International Nuclear Information System (INIS)

Ánchel, F.; Barrachina, T.; Miró, R.; Verdú, G.; Juanas, J.; Macián-Juan, R.

2012-01-01

Highlights: ► Best-estimate codes are affected by the uncertainty in the methods and the models. ► Influence of the uncertainty in the macroscopic cross-sections in a BWR and PWR RIA accidents analysis. ► The fast diffusion coefficient, the scattering cross section and both fission cross sections are the most influential factors. ► The absorption cross sections very little influence. ► Using a normal pdf the results are more “conservative” comparing the power peak reached with uncertainty quantified with a uniform pdf. - Abstract: The Best Estimate analysis consists of a coupled thermal-hydraulic and neutronic description of the nuclear system's behavior; uncertainties from both aspects should be included and jointly propagated. This paper presents a study of the influence of the uncertainty in the macroscopic neutronic information that describes a three-dimensional core model on the most relevant results of the simulation of a Reactivity Induced Accident (RIA). The analyses of a BWR-RIA and a PWR-RIA have been carried out with a three-dimensional thermal-hydraulic and neutronic model for the coupled system TRACE-PARCS and RELAP-PARCS. The cross section information has been generated by the SIMTAB methodology based on the joint use of CASMO-SIMULATE. The statistically based methodology performs a Monte-Carlo kind of sampling of the uncertainty in the macroscopic cross sections. The size of the sampling is determined by the characteristics of the tolerance intervals by applying the Noether–Wilks formulas. A number of simulations equal to the sample size have been carried out in which the cross sections used by PARCS are directly modified with uncertainty, and non-parametric statistical methods are applied to the resulting sample of the values of the output variables to determine their intervals of tolerance.

Density meter algorithm and system for estimating sampling/mixing uncertainty

International Nuclear Information System (INIS)

Shine, E.P.

1986-01-01

The Laboratories Department at the Savannah River Plant (SRP) has installed a six-place density meter with an automatic sampling device. This paper describes the statistical software developed to analyze the density of uranyl nitrate solutions using this automated system. The purpose of this software is twofold: to estimate the sampling/mixing and measurement uncertainties in the process and to provide a measurement control program for the density meter. Non-uniformities in density are analyzed both analytically and graphically. The mean density and its limit of error are estimated. Quality control standards are analyzed concurrently with process samples and used to control the density meter measurement error. The analyses are corrected for concentration due to evaporation of samples waiting to be analyzed. The results of this program have been successful in identifying sampling/mixing problems and controlling the quality of analyses

Density meter algorithm and system for estimating sampling/mixing uncertainty

International Nuclear Information System (INIS)

Shine, E.P.

1986-01-01

The Laboratories Department at the Savannah River Plant (SRP) has installed a six-place density meter with an automatic sampling device. This paper describes the statisical software developed to analyze the density of uranyl nitrate solutions using this automated system. The purpose of this software is twofold: to estimate the sampling/mixing and measurement uncertainties in the process and to provide a measurement control program for the density meter. Non-uniformities in density are analyzed both analytically and graphically. The mean density and its limit of error are estimated. Quality control standards are analyzed concurrently with process samples and used to control the density meter measurement error. The analyses are corrected for concentration due to evaporation of samples waiting to be analyzed. The results of this program have been successful in identifying sampling/mixing problems and controlling the quality of analyses

Urban drainage models simplifying uncertainty analysis for practitioners

DEFF Research Database (Denmark)

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

2013-01-01

in each measured/observed datapoint; an issue that is commonly overlooked 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 the 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...

The role of uncertainty analysis in dose reconstruction and risk assessment

International Nuclear Information System (INIS)

Hoffman, F.O.; Simon, S.L.; Thiessen. K.M.

1996-01-01

Dose reconstruction and risk assessment rely heavily on the use of mathematical models to extrapolate information beyond the realm of direct observation. Because models are merely approximations of real systems, their predictions are inherently uncertain. As a result, full disclosure of uncertainty in dose and risk estimates is essential to achieve scientific credibility and to build public trust. The need for formal analysis of uncertainty in model predictions was presented during the nineteenth annual meeting of the NCRP. At that time, quantitative uncertainty analysis was considered a relatively new and difficult subject practiced by only a few investigators. Today, uncertainty analysis has become synonymous with the assessment process itself. When an uncertainty analysis is used iteratively within the assessment process, it can guide experimental research to refine dose and risk estimates, deferring potentially high cost or high consequence decisions until uncertainty is either acceptable or irreducible. Uncertainty analysis is now mandated for all ongoing dose reconstruction projects within the United States, a fact that distinguishes dose reconstruction from other types of exposure and risk assessments. 64 refs., 6 figs., 1 tab

Uncertainty Analysis of Consequence Management (CM) Data Products.

Energy Technology Data Exchange (ETDEWEB)

Hunt, Brian D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eckert-Gallup, Aubrey Celia [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Cochran, Lainy Dromgoole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kraus, Terrence D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Fournier, Sean Donovan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Allen, Mark B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Schetnan, Richard Reed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Simpson, Matthew D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Okada, Colin E. [Remote Sensing Lab. (RSL), Nellis AFB, Las Vegas, NV (United States); Bingham, Avery A. [Remote Sensing Lab. (RSL), Nellis AFB, Las Vegas, NV (United States)

2018-01-01

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

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

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

Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration

Directory of Open Access Journals (Sweden)

Shifei Yuan

2015-07-01

Full Text Available Accurate estimation of model parameters and state of charge (SoC is crucial for the lithium-ion battery management system (BMS. In this paper, the stability of the model parameters and SoC estimation under measurement uncertainty is evaluated by three different factors: (i sampling periods of 1/0.5/0.1 s; (ii current sensor precisions of ±5/±50/±500 mA; and (iii voltage sensor precisions of ±1/±2.5/±5 mV. Firstly, the numerical model stability analysis and parametric sensitivity analysis for battery model parameters are conducted under sampling frequency of 1–50 Hz. The perturbation analysis is theoretically performed of current/voltage measurement uncertainty on model parameter variation. Secondly, the impact of three different factors on the model parameters and SoC estimation was evaluated with the federal urban driving sequence (FUDS profile. The bias correction recursive least square (CRLS and adaptive extended Kalman filter (AEKF algorithm were adopted to estimate the model parameters and SoC jointly. Finally, the simulation results were compared and some insightful findings were concluded. For the given battery model and parameter estimation algorithm, the sampling period, and current/voltage sampling accuracy presented a non-negligible effect on the estimation results of model parameters. This research revealed the influence of the measurement uncertainty on the model parameter estimation, which will provide the guidelines to select a reasonable sampling period and the current/voltage sensor sampling precisions in engineering applications.

Analytic uncertainty and sensitivity analysis of models with input correlations

Science.gov (United States)

Zhu, Yueying; Wang, Qiuping A.; Li, Wei; Cai, Xu

2018-03-01

Probabilistic uncertainty analysis is a common means of evaluating mathematical models. In mathematical modeling, the uncertainty in input variables is specified through distribution laws. Its contribution to the uncertainty in model response is usually analyzed by assuming that input variables are independent of each other. However, correlated parameters are often happened in practical applications. In the present paper, an analytic method is built for the uncertainty and sensitivity analysis of models in the presence of input correlations. With the method, it is straightforward to identify the importance of the independence and correlations of input variables in determining the model response. This allows one to decide whether or not the input correlations should be considered in practice. Numerical examples suggest the effectiveness and validation of our analytic method in the analysis of general models. A practical application of the method is also proposed to the uncertainty and sensitivity analysis of a deterministic HIV model.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling.

Science.gov (United States)

Cressie, Noel; Calder, Catherine A; Clark, James S; Ver Hoef, Jay M; Wikle, Christopher K

2009-04-01

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Verification of uncertainty budgets

DEFF Research Database (Denmark)

Heydorn, Kaj; Madsen, B.S.

2005-01-01

, and therefore it is essential that the applicability of the overall uncertainty budget to actual measurement results be verified on the basis of current experimental data. This should be carried out by replicate analysis of samples taken in accordance with the definition of the measurand, but representing...... the full range of matrices and concentrations for which the budget is assumed to be valid. In this way the assumptions made in the uncertainty budget can be experimentally verified, both as regards sources of variability that are assumed negligible, and dominant uncertainty components. Agreement between...

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.

Joint analysis of epistemic and aleatory uncertainty in stability analysis for geo-hazard assessments

Science.gov (United States)

Rohmer, Jeremy; Verdel, Thierry

2017-04-01

Uncertainty analysis is an unavoidable task of stability analysis of any geotechnical systems. Such analysis usually relies on the safety factor SF (if SF is below some specified threshold), the failure is possible). The objective of the stability analysis is then to estimate the failure probability P for SF to be below the specified threshold. When dealing with uncertainties, two facets should be considered as outlined by several authors in the domain of geotechnics, namely "aleatoric uncertainty" (also named "randomness" or "intrinsic variability") and "epistemic uncertainty" (i.e. when facing "vague, incomplete or imprecise information" such as limited databases and observations or "imperfect" modelling). The benefits of separating both facets of uncertainty can be seen from a risk management perspective because: - Aleatoric uncertainty, being a property of the system under study, cannot be reduced. However, practical actions can be taken to circumvent the potentially dangerous effects of such variability; - Epistemic uncertainty, being due to the incomplete/imprecise nature of available information, can be reduced by e.g., increasing the number of tests (lab or in site survey), improving the measurement methods or evaluating calculation procedure with model tests, confronting more information sources (expert opinions, data from literature, etc.). Uncertainty treatment in stability analysis usually restricts to the probabilistic framework to represent both facets of uncertainty. Yet, in the domain of geo-hazard assessments (like landslides, mine pillar collapse, rockfalls, etc.), the validity of this approach can be debatable. In the present communication, we propose to review the major criticisms available in the literature against the systematic use of probability in situations of high degree of uncertainty. On this basis, the feasibility of using a more flexible uncertainty representation tool is then investigated, namely Possibility distributions (e

Uncertainty propagation in probabilistic safety analysis of nuclear power plants

International Nuclear Information System (INIS)

Fleming, P.V.

1981-09-01

The uncertainty propagation in probabilistic safety analysis of nuclear power plants, is done. The methodology of the minimal cut is implemented in the computer code SVALON and the results for several cases are compared with corresponding results obtained with the SAMPLE code, which employs the Monte Carlo method to propagate the uncertanties. The results have show that, for a relatively small number of dominant minimal cut sets (n approximately 25) and error factors (r approximately 5) the SVALON code yields results which are comparable to those obtained with SAMPLE. An analysis of the unavailability of the low pressure recirculation system of Angra 1 for both the short and long term recirculation phases, are presented. The results for the short term phase are in good agreement with the corresponding one given in WASH-1400. (E.G.) [pt

Report of a CSNI workshop on uncertainty analysis methods. Volume 1 + 2

International Nuclear Information System (INIS)

Wickett, A.J.; Yadigaroglu, G.

1994-08-01

The OECD NEA CSNI Principal Working Group 2 (PWG2) Task Group on Thermal Hydraulic System Behaviour (TGTHSB) has, in recent years, received presentations of a variety of different methods to analyze the uncertainty in the calculations of advanced unbiased (best estimate) codes. Proposals were also made for an International Standard Problem (ISP) to compare the uncertainty analysis methods. The objectives for the Workshop were to discuss and fully understand the principles of uncertainty analysis relevant to LOCA modelling and like problems, to examine the underlying issues from first principles, in preference to comparing and contrasting the currently proposed methods, to reach consensus on the issues identified as far as possible while not avoiding the controversial aspects, to identify as clearly as possible unreconciled differences, and to issue a Status Report. Eight uncertainty analysis methods were presented. A structured discussion of various aspects of uncertainty analysis followed - the need for uncertainty analysis, identification and ranking of uncertainties, characterisation, quantification and combination of uncertainties and applications, resources and future developments. As a result, the objectives set out above were, to a very large extent, achieved. Plans for the ISP were also discussed. Volume 1 contains a record of the discussions on uncertainty methods. Volume 2 is a compilation of descriptions of the eight uncertainty analysis methods presented at the workshop

SWEPP PAN assay system uncertainty analysis: Passive mode measurements of graphite waste

International Nuclear Information System (INIS)

Blackwood, L.G.; Harker, Y.D.; Meachum, T.R.; Yoon, Woo Y.

1997-07-01

The Idaho National Engineering and Environmental Laboratory is being used as a temporary storage facility for transuranic waste generated by the U.S. Nuclear Weapons program at the Rocky Flats Plant (RFP) in Golden, Colorado. Currently, there is a large effort in progress to prepare to ship this waste to the Waste Isolation Pilot Plant (WIPP) in Carlsbad, New Mexico. In order to meet the TRU Waste Characterization Quality Assurance Program Plan nondestructive assay compliance requirements and quality assurance objectives, it is necessary to determine the total uncertainty of the radioassay results produced by the Stored Waste Examination Pilot Plant (SWEPP) Passive Active Neutron (PAN) radioassay system. To this end a modified statistical sampling and verification approach has been developed to determine the total uncertainty of a PAN measurement. In this approach the total performance of the PAN nondestructive assay system is simulated using computer models of the assay system and the resultant output is compared with the known input to assess the total uncertainty. This paper is one of a series of reports quantifying the results of the uncertainty analysis of the PAN system measurements for specific waste types and measurement modes. In particular this report covers passive mode measurements of weapons grade plutonium-contaminated graphite molds contained in 208 liter drums (waste code 300). The validity of the simulation approach is verified by comparing simulated output against results from measurements using known plutonium sources and a surrogate graphite waste form drum. For actual graphite waste form conditions, a set of 50 cases covering a statistical sampling of the conditions exhibited in graphite wastes was compiled using a Latin hypercube statistical sampling approach

Response surface methodology for sensitivity and uncertainty analysis: performance and perspectives

International Nuclear Information System (INIS)

Olivi, L.; Brunelli, F.; Cacciabue, P.C.; Parisi, P.

1985-01-01

Two main aspects have to be taken into account in studying a nuclear accident scenario when using nuclear safety codes as an information source. The first one concerns the behavior of the code response and the set of assumptions to be introduced for its modelling. The second one is connected with the uncertainty features of the code input, often modelled as a probability density function (pdf). The analyst can apply two well-defined approaches depending on whether he wants major emphasis put on either of the aspects. Response Surface Methodology uses polynomial and inverse polynomial models together with the theory of experimental design, expressly developed for the identification procedure. It constitutes a well-established body of techniques able to cover a wide spectrum of requirements, when the first aspect plays the crucial role in the definition of the objectives. Other techniques such as Latin hypercube sampling, stratified sampling or even random sampling can fit better, when the second aspect affects the reliability of the analysis. The ultimate goal for both approaches is the selection of the variable, i.e. the identification of the code input variables most effective on the output and the uncertainty propagation, i.e. the assessment of the pdf to be attributed to the code response. The main aim of this work is to present a sensitivity analysis method, already tested on a real case, sufficiently flexible to be applied in both approaches mentioned

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.

Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation

International Nuclear Information System (INIS)

Helgesson, P.; Sjöstrand, H.; Koning, A.J.; Rydén, J.; Rochman, D.; Alhassan, E.; Pomp, S.

2016-01-01

In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also

Estimating the measurement uncertainty in forensic blood alcohol analysis.

Science.gov (United States)

Gullberg, Rod G

2012-04-01

For many reasons, forensic toxicologists are being asked to determine and report their measurement uncertainty in blood alcohol analysis. While understood conceptually, the elements and computations involved in determining measurement uncertainty are generally foreign to most forensic toxicologists. Several established and well-documented methods are available to determine and report the uncertainty in blood alcohol measurement. A straightforward bottom-up approach is presented that includes: (1) specifying the measurand, (2) identifying the major components of uncertainty, (3) quantifying the components, (4) statistically combining the components and (5) reporting the results. A hypothetical example is presented that employs reasonable estimates for forensic blood alcohol analysis assuming headspace gas chromatography. These computations are easily employed in spreadsheet programs as well. Determining and reporting measurement uncertainty is an important element in establishing fitness-for-purpose. Indeed, the demand for such computations and information from the forensic toxicologist will continue to increase.

Comparative uncertainty analysis of copper loads in stormwater systems using GLUE and grey-box modeling

DEFF Research Database (Denmark)

Lindblom, Erik Ulfson; Madsen, Henrik; Mikkelsen, Peter Steen

2007-01-01

. With the proposed model and input data, the GLUE analysis show that the total sampled copper mass can be predicted within a range of +/- 50% of the median value ( 385 g), whereas the grey-box analysis showed a prediction uncertainty of less than +/- 30%. Future work will clarify the pros and cons of the two methods...

Characterisation of a reference site for quantifying uncertainties related to soil sampling

International Nuclear Information System (INIS)

Barbizzi, Sabrina; Zorzi, Paolo de; Belli, Maria; Pati, Alessandra; Sansone, Umberto; Stellato, Luisa; Barbina, Maria; Deluisa, Andrea; Menegon, Sandro; Coletti, Valter

2004-01-01

An integrated approach to quality assurance in soil sampling remains to be accomplished. - The paper reports a methodology adopted to face problems related to quality assurance in soil sampling. The SOILSAMP project, funded by the Environmental Protection Agency of Italy (APAT), is aimed at (i) establishing protocols for soil sampling in different environments; (ii) assessing uncertainties associated with different soil sampling methods in order to select the 'fit-for-purpose' method; (iii) qualifying, in term of trace elements spatial variability, a reference site for national and international inter-comparison exercises. Preliminary results and considerations are illustrated

Uncertainty Propagation in Hypersonic Vehicle Aerothermoelastic Analysis

Science.gov (United States)

Lamorte, Nicolas Etienne

Hypersonic vehicles face a challenging flight environment. The aerothermoelastic analysis of its components requires numerous simplifying approximations. Identifying and quantifying the effect of uncertainties pushes the limits of the existing deterministic models, and is pursued in this work. An uncertainty quantification framework is used to propagate the effects of identified uncertainties on the stability margins and performance of the different systems considered. First, the aeroelastic stability of a typical section representative of a control surface on a hypersonic vehicle is examined. Variability in the uncoupled natural frequencies of the system is modeled to mimic the effect of aerodynamic heating. Next, the stability of an aerodynamically heated panel representing a component of the skin of a generic hypersonic vehicle is considered. Uncertainty in the location of transition from laminar to turbulent flow and the heat flux prediction is quantified using CFD. In both cases significant reductions of the stability margins are observed. A loosely coupled airframe--integrated scramjet engine is considered next. The elongated body and cowl of the engine flow path are subject to harsh aerothermodynamic loading which causes it to deform. Uncertainty associated with deformation prediction is propagated to the engine performance analysis. The cowl deformation is the main contributor to the sensitivity of the propulsion system performance. Finally, a framework for aerothermoelastic stability boundary calculation for hypersonic vehicles using CFD is developed. The usage of CFD enables one to consider different turbulence conditions, laminar or turbulent, and different models of the air mixture, in particular real gas model which accounts for dissociation of molecules at high temperature. The system is found to be sensitive to turbulence modeling as well as the location of the transition from laminar to turbulent flow. Real gas effects play a minor role in the

Uncertainty Instability Risk Analysis of High Concrete Arch Dam Abutments

Directory of Open Access Journals (Sweden)

Xin Cao

2017-01-01

Full Text Available The uncertainties associated with concrete arch dams rise with the increased height of dams. Given the uncertainties associated with influencing factors, the stability of high arch dam abutments as a fuzzy random event was studied. In addition, given the randomness and fuzziness of calculation parameters as well as the failure criterion, hazard point and hazard surface uncertainty instability risk ratio models were proposed for high arch dam abutments on the basis of credibility theory. The uncertainty instability failure criterion was derived through the analysis of the progressive instability failure process on the basis of Shannon’s entropy theory. The uncertainties associated with influencing factors were quantized by probability or possibility distribution assignments. Gaussian random theory was used to generate random realizations for influence factors with spatial variability. The uncertainty stability analysis method was proposed by combining the finite element analysis and the limit equilibrium method. The instability risk ratio was calculated using the Monte Carlo simulation method and fuzzy random postprocessing. Results corroborate that the modeling approach is sound and that the calculation method is feasible.

Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling

DEFF Research Database (Denmark)

Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik

2008-01-01

propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic......In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit...... of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models...

The importance of input interactions in the uncertainty and sensitivity analysis of nuclear fuel behavior

Energy Technology Data Exchange (ETDEWEB)

Ikonen, T., E-mail: timo.ikonen@vtt.fi; Tulkki, V.

2014-08-15

Highlights: • Uncertainty and sensitivity analysis of modeled nuclear fuel behavior is performed. • Burnup dependency of the uncertainties and sensitivities is characterized. • Input interactions significantly increase output uncertainties for irradiated fuel. • Identification of uncertainty sources is greatly improved with higher order methods. • Results stress the importance of using methods that take interactions into account. - Abstract: The propagation of uncertainties in a PWR fuel rod under steady-state irradiation is analyzed by computational means. A hypothetical steady-state scenario of the Three Mile Island 1 reactor fuel rod is modeled with the fuel performance FRAPCON, using realistic input uncertainties for the fabrication and model parameters, boundary conditions and material properties. The uncertainty and sensitivity analysis is performed by extensive Monte Carlo sampling of the inputs’ probability distribution and by applying correlation coefficient and Sobol’ variance decomposition analyses. The latter includes evaluation of the second order and total effect sensitivity indices, allowing the study of interactions between input variables. The results show that the interactions play a large role in the propagation of uncertainties, and first order methods such as the correlation coefficient analyses are in general insufficient for sensitivity analysis of the fuel rod. Significant improvement over the first order methods can be achieved by using higher order methods. The results also show that both the magnitude of the uncertainties and their propagation depends not only on the output in question, but also on burnup. The latter is due to onset of new phenomena (such as the fission gas release) and the gradual closure of the pellet-cladding gap with increasing burnup. Increasing burnup also affects the importance of input interactions. Interaction effects are typically highest in the moderate burnup (of the order of 10–40 MWd

The use of kragten spreadsheets for uncertainty evaluation of uranium potentiometric analysis by the Brazilian Safeguards Laboratory

International Nuclear Information System (INIS)

Silva, Jose Wanderley S. da; Barros, Pedro Dionisio de; Araujo, Radier Mario S. de

2009-01-01

In safeguards, independent analysis of uranium content and enrichment of nuclear materials to verify operator's declarations is an important tool to evaluate the accountability system applied by nuclear installations. This determination may be performed by nondestructive (NDA) methods, generally done in the field using portable radiation detection systems, or destructive (DA) methods by chemical analysis when more accurate and precise results are necessary. Samples for DA analysis are collected by inspectors during safeguards inspections and sent to Safeguards Laboratory (LASAL) of the Brazilian Nuclear Energy Commission - (CNEN), where the analysis take place. The method used by LASAL for determination of uranium in different physical and chemical forms is the Davies and Gray/NBL using an automatic potentiometric titrator, which performs the titration of uranium IV by a standard solution of K 2 Cr 2 O 7 . Uncertainty budgets have been determined based on the concepts of the ISO 'Guide to the Expression of Uncertainty in Measurement' (GUM). In order to simplify the calculation of the uncertainty, a computational tool named Kragten Spreadsheet was used. Such spreadsheet uses the concepts established by the GUM and provides results that numerically approximates to those obtained by propagation of uncertainty with analytically determined sensitivity coefficients. The main parameters (input quantities) interfering on the uncertainty were studied. In order to evaluate their contribution in the final uncertainty, the uncertainties of all steps of the analytical method were estimated and compiled. (author)

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

Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment. Volume 3, Appendices C, D, E, F, and G

Energy Technology Data Exchange (ETDEWEB)

Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States)] [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 third of a three-volume document describing the project and contains descriptions of the probability assessment principles; the expert identification and selection process; the weighting methods used; the inverse modeling methods; case structures; and summaries of the consequence codes.

Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment. Volume 3, Appendices C, D, E, F, and G

International Nuclear Information System (INIS)

Harper, F.T.; Young, M.L.; Miller, L.A.

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 third of a three-volume document describing the project and contains descriptions of the probability assessment principles; the expert identification and selection process; the weighting methods used; the inverse modeling methods; case structures; and summaries of the consequence codes

Two-dimensional cross-section sensitivity and uncertainty analysis for fusion reactor blankets

International Nuclear Information System (INIS)

Embrechts, M.J.

1982-02-01

A two-dimensional sensitivity and uncertainty analysis for the heating of the TF coil for the FED (fusion engineering device) blanket was performed. The uncertainties calculated are of the same order of magnitude as those resulting from a one-dimensional analysis. The largest uncertainties were caused by the cross section uncertainties for chromium

Phenomenological uncertainty analysis of containment building pressure load caused by severe accident sequences

International Nuclear Information System (INIS)

Park, S.Y.; Ahn, K.I.

2014-01-01

Highlights: • Phenomenological uncertainty analysis has been applied to level 2 PSA. • The methodology provides an alternative to simple deterministic analyses and sensitivity studies. • A realistic evaluation provides a more complete characterization of risks. • Uncertain parameters of MAAP code for the early containment failure were identified. - Abstract: This paper illustrates an application of a severe accident analysis code, MAAP, to the uncertainty evaluation of early containment failure scenarios employed in the containment event tree (CET) model of a reference plant. An uncertainty analysis of containment pressure behavior during severe accidents has been performed for an optimum assessment of an early containment failure model. The present application is mainly focused on determining an estimate of the containment building pressure load caused by severe accident sequences of a nuclear power plant. Key modeling parameters and phenomenological models employed for the present uncertainty analysis are closely related to the in-vessel hydrogen generation, direct containment heating, and gas combustion. The basic approach of this methodology is to (1) develop severe accident scenarios for which containment pressure loads should be performed based on a level 2 PSA, (2) identify severe accident phenomena relevant to an early containment failure, (3) identify the MAAP input parameters, sensitivity coefficients, and modeling options that describe or influence the early containment failure phenomena, (4) prescribe the likelihood descriptions of the potential range of these parameters, and (5) evaluate the code predictions using a number of random combinations of parameter inputs sampled from the likelihood distributions

Risk Characterization uncertainties associated description, sensitivity analysis

International Nuclear Information System (INIS)

Carrillo, M.; Tovar, M.; Alvarez, J.; Arraez, M.; Hordziejewicz, I.; Loreto, I.

2013-01-01

The power point presentation is about risks to the estimated levels of exposure, uncertainty and variability in the analysis, sensitivity analysis, risks from exposure to multiple substances, formulation of guidelines for carcinogenic and genotoxic compounds and risk subpopulations

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.

Design optimization and uncertainty analysis of SMA morphing structures

International Nuclear Information System (INIS)

Oehler, S D; Hartl, D J; Lopez, R; Malak, R J; Lagoudas, D C

2012-01-01

The continuing implementation of shape memory alloys (SMAs) as lightweight solid-state actuators in morphing structures has now motivated research into finding optimized designs for use in aerospace control systems. This work proposes methods that use iterative analysis techniques to determine optimized designs for morphing aerostructures and consider the impact of uncertainty in model variables on the solution. A combination of commercially available and custom coded tools is utilized. ModelCenter, a suite of optimization algorithms and simulation process management tools, is coupled with the Abaqus finite element analysis suite and a custom SMA constitutive model to assess morphing structure designs in an automated fashion. The chosen case study involves determining the optimized configuration of a morphing aerostructure assembly that includes SMA flexures. This is accomplished by altering design inputs representing the placement of active components to minimize a specified cost function. An uncertainty analysis is also conducted using design of experiment methods to determine the sensitivity of the solution to a set of uncertainty variables. This second study demonstrates the effective use of Monte Carlo techniques to simulate the variance of model variables representing the inherent uncertainty in component fabrication processes. This paper outlines the modeling tools used to execute each case study, details the procedures for constructing the optimization problem and uncertainty analysis, and highlights the results from both studies. (paper)

The role of sensitivity analysis in assessing uncertainty

International Nuclear Information System (INIS)

Crick, M.J.; Hill, M.D.

1987-01-01

Outside the specialist world of those carrying out performance assessments considerable confusion has arisen about the meanings of sensitivity analysis and uncertainty analysis. In this paper we attempt to reduce this confusion. We then go on to review approaches to sensitivity analysis within the context of assessing uncertainty, and to outline the types of test available to identify sensitive parameters, together with their advantages and disadvantages. The views expressed in this paper are those of the authors; they have not been formally endorsed by the National Radiological Protection Board and should not be interpreted as Board advice

Uncertainty evaluation in correlated quantities: application to elemental analysis of atmospheric aerosols

International Nuclear Information System (INIS)

Espinosa, A.; Miranda, J.; Pineda, J. C.

2010-01-01

One of the aspects that are frequently overlooked in the evaluation of uncertainty in experimental data is the possibility that the involved quantities are correlated among them, due to different causes. An example in the elemental analysis of atmospheric aerosols using techniques like X-ray Fluorescence (X RF) or Particle Induced X-ray Emission (PIXE). In these cases, the measured elemental concentrations are highly correlated, and then are used to obtain information about other variables, such as the contribution from emitting sources related to soil, sulfate, non-soil potassium or organic matter. This work describes, as an example, the method required to evaluate the uncertainty in variables determined from correlated quantities from a set of atmospheric aerosol samples collected in the Metropolitan Area of the Mexico Valley and analyzed with PIXE. The work is based on the recommendations of the Guide for the Evaluation of Uncertainty published by the International Organization for Standardization. (Author)

Including uncertainty in hazard analysis through fuzzy measures

International Nuclear Information System (INIS)

Bott, T.F.; Eisenhawer, S.W.

1997-12-01

This paper presents a method for capturing the uncertainty expressed by an Hazard Analysis (HA) expert team when estimating the frequencies and consequences of accident sequences and provides a sound mathematical framework for propagating this uncertainty to the risk estimates for these accident sequences. The uncertainty is readily expressed as distributions that can visually aid the analyst in determining the extent and source of risk uncertainty in HA accident sequences. The results also can be expressed as single statistics of the distribution in a manner analogous to expressing a probabilistic distribution as a point-value statistic such as a mean or median. The study discussed here used data collected during the elicitation portion of an HA on a high-level waste transfer process to demonstrate the techniques for capturing uncertainty. These data came from observations of the uncertainty that HA team members expressed in assigning frequencies and consequences to accident sequences during an actual HA. This uncertainty was captured and manipulated using ideas from possibility theory. The result of this study is a practical method for displaying and assessing the uncertainty in the HA team estimates of the frequency and consequences for accident sequences. This uncertainty provides potentially valuable information about accident sequences that typically is lost in the HA process

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

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

DS02 uncertainty analysis

International Nuclear Information System (INIS)

Kaul, Dean C.; Egbert, Stephen D.; Woolson, William A.

2005-01-01

In order to avoid the pitfalls that so discredited DS86 and its uncertainty estimates, and to provide DS02 uncertainties that are both defensible and credible, this report not only presents the ensemble uncertainties assembled from uncertainties in individual computational elements and radiation dose components but also describes how these relate to comparisons between observed and computed quantities at critical intervals in the computational process. These comparisons include those between observed and calculated radiation free-field components, where observations include thermal- and fast-neutron activation and gamma-ray thermoluminescence, which are relevant to the estimated systematic uncertainty for DS02. The comparisons also include those between calculated and observed survivor shielding, where the observations consist of biodosimetric measurements for individual survivors, which are relevant to the estimated random uncertainty for DS02. (J.P.N.)

Sensitivity and uncertainty analysis of the PATHWAY radionuclide transport model

International Nuclear Information System (INIS)

Otis, M.D.

1983-01-01

Procedures were developed for the uncertainty and sensitivity analysis of a dynamic model of radionuclide transport through human food chains. Uncertainty in model predictions was estimated by propagation of parameter uncertainties using a Monte Carlo simulation technique. Sensitivity of model predictions to individual parameters was investigated using the partial correlation coefficient of each parameter with model output. Random values produced for the uncertainty analysis were used in the correlation analysis for sensitivity. These procedures were applied to the PATHWAY model which predicts concentrations of radionuclides in foods grown in Nevada and Utah and exposed to fallout during the period of atmospheric nuclear weapons testing in Nevada. Concentrations and time-integrated concentrations of iodine-131, cesium-136, and cesium-137 in milk and other foods were investigated. 9 figs., 13 tabs

The characterisation and evaluation of uncertainty in probabilistic risk analysis

International Nuclear Information System (INIS)

Parry, G.W.; Winter, P.W.

1980-10-01

The sources of uncertainty in probabilistic risk analysis are discussed using the event/fault tree methodology as an example. The role of statistics in quantifying these uncertainties is investigated. A class of uncertainties is identified which is, at present, unquantifiable, using either classical or Bayesian statistics. It is argued that Bayesian statistics is the more appropriate vehicle for the probabilistic analysis of rare events and a short review is given with some discussion on the representation of ignorance. (author)

Uncertainty of quantitative microbiological methods of pharmaceutical analysis.

Science.gov (United States)

Gunar, O V; Sakhno, N G

2015-12-30

The total uncertainty of quantitative microbiological methods, used in pharmaceutical analysis, consists of several components. The analysis of the most important sources of the quantitative microbiological methods variability demonstrated no effect of culture media and plate-count techniques in the estimation of microbial count while the highly significant effect of other factors (type of microorganism, pharmaceutical product and individual reading and interpreting errors) was established. The most appropriate method of statistical analysis of such data was ANOVA which enabled not only the effect of individual factors to be estimated but also their interactions. Considering all the elements of uncertainty and combining them mathematically the combined relative uncertainty of the test results was estimated both for method of quantitative examination of non-sterile pharmaceuticals and microbial count technique without any product. These data did not exceed 35%, appropriated for a traditional plate count methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Uncertainty analysis of 137Cs and 90Sr activity in borehole water from a waste disposal site

International Nuclear Information System (INIS)

Dafauti, Sunita; Pulhani, Vandana; Datta, D.; Hegde, A.G.

2005-01-01

Uncertainty quantification (UQ) is the quantitative characterization and use of uncertainty in experimental applications. There are two distinct types of uncertainty variability which can be quantified in principle using classical probability theory and lack of knowledge which requires more than classical probability theory for its quantification. Fuzzy set theory was applied to quantify the second type of uncertainty associated with the measurement of activity due to 137 Cs and 90 Sr present in bore-well water samples from a waste disposal site. The upper and lower limits of concentration were computed and it may be concluded from the analysis that the alpha cut technique of fuzzy set theory is a good nonprecise estimator of these types of bounds. (author)

Distribution of pesticide residues in soil and uncertainty of sampling.

Science.gov (United States)

Suszter, Gabriela K; Ambrus, Árpád

2017-08-03

Pesticide residues were determined in about 120 soil cores taken randomly from the top 15 cm layer of two sunflower fields about 30 days after preemergence herbicide treatments. Samples were extracted with acetone-ethyl acetate mixture and the residues were determined with GC-TSD. Residues of dimethenamid, pendimethalin, and prometryn ranged from 0.005 to 2.97 mg/kg. Their relative standard deviations (CV) were between 0.66 and 1.13. The relative frequency distributions of residues in soil cores were very similar to those observed in root and tuber vegetables grown in pesticide treated soils. Based on all available information, a typical CV of 1.00 was estimated for pesticide residues in primary soil samples (soil cores). The corresponding expectable relative uncertainty of sampling is 20% when composite samples of size 25 are taken. To obtain a reliable estimate of the average residues in the top 15 cm layer of soil of a field up to 8 independent replicate random samples should be taken. To obtain better estimate of the actual residue level of the sampled filed would be marginal if larger number of samples were taken.

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.

[Influence of Uncertainty and Uncertainty Appraisal on Self-management in Hemodialysis Patients].

Science.gov (United States)

Jang, Hyung Suk; Lee, Chang Suk; Yang, Young Hee

2015-04-01

This study was done to examine the relation of uncertainty, uncertainty appraisal, and self-management in patients undergoing hemodialysis, and to identify factors influencing self-management. A convenience sample of 92 patients receiving hemodialysis was selected. Data were collected using a structured questionnaire and medical records. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations and multiple regression analysis with the SPSS/WIN 20.0 program. The participants showed a moderate level of uncertainty with the highest score being for ambiguity among the four uncertainty subdomains. Scores for uncertainty danger or opportunity appraisals were under the mid points. The participants were found to perform a high level of self-management such as diet control, management of arteriovenous fistula, exercise, medication, physical management, measurements of body weight and blood pressure, and social activity. The self-management of participants undergoing hemodialysis showed a significant relationship with uncertainty and uncertainty appraisal. The significant factors influencing self-management were uncertainty, uncertainty opportunity appraisal, hemodialysis duration, and having a spouse. These variables explained 32.8% of the variance in self-management. The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of uncertainty opportunity appraisal among patients would improve the self-management of hemodialysis patients.

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...

Principles and applications of measurement and uncertainty analysis in research and calibration

Energy Technology Data Exchange (ETDEWEB)

Wells, C.V.

1992-11-01

Interest in Measurement Uncertainty Analysis has grown in the past several years as it has spread to new fields of application, and research and development of uncertainty methodologies have continued. This paper discusses the subject from the perspectives of both research and calibration environments. It presents a history of the development and an overview of the principles of uncertainty analysis embodied in the United States National Standard, ANSI/ASME PTC 19.1-1985, Measurement Uncertainty. Examples are presented in which uncertainty analysis was utilized or is needed to gain further knowledge of a particular measurement process and to characterize final results. Measurement uncertainty analysis provides a quantitative estimate of the interval about a measured value or an experiment result within which the true value of that quantity is expected to lie. Years ago, Harry Ku of the United States National Bureau of Standards stated that ``The informational content of the statement of uncertainty determines, to a large extent, the worth of the calibrated value.`` Today, that statement is just as true about calibration or research results as it was in 1968. Why is that true? What kind of information should we include in a statement of uncertainty accompanying a calibrated value? How and where do we get the information to include in an uncertainty statement? How should we interpret and use measurement uncertainty information? This discussion will provide answers to these and other questions about uncertainty in research and in calibration. The methodology to be described has been developed by national and international groups over the past nearly thirty years, and individuals were publishing information even earlier. Yet the work is largely unknown in many science and engineering arenas. I will illustrate various aspects of uncertainty analysis with some examples drawn from the radiometry measurement and calibration discipline from research activities.

Principles and applications of measurement and uncertainty analysis in research and calibration

Energy Technology Data Exchange (ETDEWEB)

Wells, C.V.

1992-11-01

Interest in Measurement Uncertainty Analysis has grown in the past several years as it has spread to new fields of application, and research and development of uncertainty methodologies have continued. This paper discusses the subject from the perspectives of both research and calibration environments. It presents a history of the development and an overview of the principles of uncertainty analysis embodied in the United States National Standard, ANSI/ASME PTC 19.1-1985, Measurement Uncertainty. Examples are presented in which uncertainty analysis was utilized or is needed to gain further knowledge of a particular measurement process and to characterize final results. Measurement uncertainty analysis provides a quantitative estimate of the interval about a measured value or an experiment result within which the true value of that quantity is expected to lie. Years ago, Harry Ku of the United States National Bureau of Standards stated that The informational content of the statement of uncertainty determines, to a large extent, the worth of the calibrated value.'' Today, that statement is just as true about calibration or research results as it was in 1968. Why is that true What kind of information should we include in a statement of uncertainty accompanying a calibrated value How and where do we get the information to include in an uncertainty statement How should we interpret and use measurement uncertainty information This discussion will provide answers to these and other questions about uncertainty in research and in calibration. The methodology to be described has been developed by national and international groups over the past nearly thirty years, and individuals were publishing information even earlier. Yet the work is largely unknown in many science and engineering arenas. I will illustrate various aspects of uncertainty analysis with some examples drawn from the radiometry measurement and calibration discipline from research activities.

Error Analysis of CM Data Products Sources of Uncertainty

Energy Technology Data Exchange (ETDEWEB)

Hunt, Brian D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eckert-Gallup, Aubrey Celia [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Cochran, Lainy Dromgoole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kraus, Terrence D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Allen, Mark B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Beal, Bill [National Security Technologies, Joint Base Andrews, MD (United States); Okada, Colin [National Security Technologies, LLC. (NSTec), Las Vegas, NV (United States); Simpson, Mathew [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-02-01

This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE’s Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.

Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

Science.gov (United States)

Lau, William K. M. (Technical Monitor); Bell, Thomas L.; Steiner, Matthias; Zhang, Yu; Wood, Eric F.

2002-01-01

The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

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.

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

New challenges on uncertainty propagation assessment of flood risk analysis

Science.gov (United States)

Martins, Luciano; Aroca-Jiménez, Estefanía; Bodoque, José M.; Díez-Herrero, Andrés

2016-04-01

Natural hazards, such as floods, cause considerable damage to the human life, material and functional assets every year and around the World. Risk assessment procedures has associated a set of uncertainties, mainly of two types: natural, derived from stochastic character inherent in the flood process dynamics; and epistemic, that are associated with lack of knowledge or the bad procedures employed in the study of these processes. There are abundant scientific and technical literature on uncertainties estimation in each step of flood risk analysis (e.g. rainfall estimates, hydraulic modelling variables); but very few experience on the propagation of the uncertainties along the flood risk assessment. Therefore, epistemic uncertainties are the main goal of this work, in particular,understand the extension of the propagation of uncertainties throughout the process, starting with inundability studies until risk analysis, and how far does vary a proper analysis of the risk of flooding. These methodologies, such as Polynomial Chaos Theory (PCT), Method of Moments or Monte Carlo, are used to evaluate different sources of error, such as data records (precipitation gauges, flow gauges...), hydrologic and hydraulic modelling (inundation estimation), socio-demographic data (damage estimation) to evaluate the uncertainties propagation (UP) considered in design flood risk estimation both, in numerical and cartographic expression. In order to consider the total uncertainty and understand what factors are contributed most to the final uncertainty, we used the method of Polynomial Chaos Theory (PCT). It represents an interesting way to handle to inclusion of uncertainty in the modelling and simulation process. PCT allows for the development of a probabilistic model of the system in a deterministic setting. This is done by using random variables and polynomials to handle the effects of uncertainty. Method application results have a better robustness than traditional analysis

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)

Planning for robust reserve networks using uncertainty analysis

Science.gov (United States)

Moilanen, A.; Runge, M.C.; Elith, Jane; Tyre, A.; Carmel, Y.; Fegraus, E.; Wintle, B.A.; Burgman, M.; Ben-Haim, Y.

2006-01-01

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence?absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data?erroneous species presence?absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

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.

Evaluating prediction uncertainty

International Nuclear Information System (INIS)

McKay, M.D.

1995-03-01

The probability distribution of a model prediction is presented as a proper basis for evaluating the uncertainty in a model prediction that arises from uncertainty in input values. Determination of important model inputs and subsets of inputs is made through comparison of the prediction distribution with conditional prediction probability distributions. Replicated Latin hypercube sampling and variance ratios are used in estimation of the distributions and in construction of importance indicators. The assumption of a linear relation between model output and inputs is not necessary for the indicators to be effective. A sequential methodology which includes an independent validation step is applied in two analysis applications to select subsets of input variables which are the dominant causes of uncertainty in the model predictions. Comparison with results from methods which assume linearity shows how those methods may fail. Finally, suggestions for treating structural uncertainty for submodels are presented

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

Monte Carlo Calculation of Thermal Neutron Inelastic Scattering Cross Section Uncertainties by Sampling Perturbed Phonon Spectra

Science.gov (United States)

Holmes, Jesse Curtis

Nuclear data libraries provide fundamental reaction information required by nuclear system simulation codes. The inclusion of data covariances in these libraries allows the user to assess uncertainties in system response parameters as a function of uncertainties in the nuclear data. Formats and procedures are currently established for representing covariances for various types of reaction data in ENDF libraries. This covariance data is typically generated utilizing experimental measurements and empirical models, consistent with the method of parent data production. However, ENDF File 7 thermal neutron scattering library data is, by convention, produced theoretically through fundamental scattering physics model calculations. Currently, there is no published covariance data for ENDF File 7 thermal libraries. Furthermore, no accepted methodology exists for quantifying or representing uncertainty information associated with this thermal library data. The quality of thermal neutron inelastic scattering cross section data can be of high importance in reactor analysis and criticality safety applications. These cross sections depend on the material's structure and dynamics. The double-differential scattering law, S(alpha, beta), tabulated in ENDF File 7 libraries contains this information. For crystalline solids, S(alpha, beta) is primarily a function of the material's phonon density of states (DOS). Published ENDF File 7 libraries are commonly produced by calculation and processing codes, such as the LEAPR module of NJOY, which utilize the phonon DOS as the fundamental input for inelastic scattering calculations to directly output an S(alpha, beta) matrix. To determine covariances for the S(alpha, beta) data generated by this process, information about uncertainties in the DOS is required. The phonon DOS may be viewed as a probability density function of atomic vibrational energy states that exist in a material. Probable variation in the shape of this spectrum may be

Aspects of uncertainty analysis in accident consequence modeling

International Nuclear Information System (INIS)

Travis, C.C.; Hoffman, F.O.

1981-01-01

Mathematical models are frequently used to determine probable dose to man from an accidental release of radionuclides by a nuclear facility. With increased emphasis on the accuracy of these models, the incorporation of uncertainty analysis has become one of the most crucial and sensitive components in evaluating the significance of model predictions. In the present paper, we address three aspects of uncertainty in models used to assess the radiological impact to humans: uncertainties resulting from the natural variability in human biological parameters; the propagation of parameter variability by mathematical models; and comparison of model predictions to observational data

Data Quality Assessment of the Uncertainty Analysis Applied to the Greenhouse Gas Emissions of a Dairy Cow System

Directory of Open Access Journals (Sweden)

Chun-Youl Baek

2017-09-01

Full Text Available The results of an uncertainty analysis are achieved by the statistical information (standard error, type of probability distributions, and range of minimum and maximum of the selected input parameters. However, there are limitations in identifying sufficient data samples for the selected input parameters for statistical information in the field of life cycle assessment (LCA. Therefore, there is a strong need for a consistent screening procedure to identify the input parameters for use in uncertainty analysis in the area of LCA. The conventional procedure for identifying input parameters for the uncertainty analysis method includes assessing the data quality using the pedigree method and the contribution analysis of the LCA results. This paper proposes a simplified procedure for ameliorating the existing data quality assessment method, which can lead to an efficient uncertainly analysis of LCA results. The proposed method has two salient features: (i a simplified procedure based on contribution analysis followed by a data quality assessment for selecting the input parameters for the uncertainty analysis; and (ii a quantitative data quality assessment method is proposed, based on the pedigree method, that adopts the analytic hierarchy process (AHP method and quality function deployment (QFD. The effects of the uncertainty of the selected input parameters on the LCA results were assessed using the Monte Carlo simulation method. A case study of greenhouse gas (GHG emissions from a dairy cow system was used to demonstrate the applicability of the proposed procedure.

Determination of uncertainties in energy and exergy analysis of a power plant

International Nuclear Information System (INIS)

Ege, Ahmet; Şahin, Hacı Mehmet

2014-01-01

Highlights: • Energy and exergy efficiency uncertainties in a large thermal power plant examined. • Sensitivity analysis shows importance of basic measurements on efficiency analysis. • A quick and practical approach is provided for determining efficiency uncertainties. • Extreme case analysis characterizes maximum possible boundaries of uncertainties. • Uncertainty determination in a plant is a dynamic process with real data. - Abstract: In this study, energy and exergy efficiency uncertainties of a large scale lignite fired power plant cycle and various measurement parameter sensitivities were investigated for five different design power outputs (100%, 85%, 80%, 60% and 40%) and with real data of the plant. For that purpose a black box method was employed considering coal flow with Lower Heating Value (LHV) as a single input and electricity produced as a single output of the plant. The uncertainty of energy and exergy efficiency of the plant was evaluated with this method by applying sensitivity analysis depending on the effect of measurement parameters such as LHV, coal mass flow rate, cell generator output voltage/current. In addition, an extreme case analysis was investigated to determine the maximum range of the uncertainties. Results of the black box method showed that uncertainties varied between 1.82–1.98% for energy efficiency and 1.32–1.43% for exergy efficiency of the plant at an operating power level of 40–100% of full power. It was concluded that LHV determination was the most important uncertainty source of energy and exergy efficiency of the plant. The uncertainties of the extreme case analysis were determined between 2.30% and 2.36% for energy efficiency while 1.66% and 1.70% for exergy efficiency for 40–100% power output respectively. Proposed method was shown to be an approach for understanding major uncertainties as well as effects of some measurement parameters in a large scale thermal power plant

Best-estimate analysis and decision making under uncertainty

International Nuclear Information System (INIS)

Orechwa, Y.

2004-01-01

In many engineering analyses of system safety the traditional reliance on conservative evaluation model calculations is being replaced with so called best-estimate analysis. These best-estimate analyses differentiate themselves from the traditional conservative analyses through two ingredients, namely realistic models and an account of the residual uncertainty associated with the model calculations. Best-estimate analysis, in the context of this paper, refers to the numerical evaluation of system properties of interest in situations where direct confirmatory measurements are not feasible. A decision with regard to the safety of the system is then made based on the computed numerical values of the system properties of interest. These situations generally arise in the design of systems that require computed and generally nontrivial extrapolations from the available data. In the case of nuclear reactors, examples are criticality of spent fuel pools, neutronic parameters of new advanced designs where insufficient material is available for mockup critical experiments and, the large break loss of coolant accident (LOCA). In this paper the case of LOCA, is taken to discuss the best-estimate analysis and decision making. Central to decision making is information. Thus, of interest is the source, quantity and quality of the information obtained in a best-estimate analysis, and used to define the acceptance criteria and to formulate a decision rule. This in effect expands the problem from the calculation of a conservative margin to a predefined acceptance criterion, to the formulation of a consistent decision rule and the computation of a test statistic for application of the decision rule. The latter view is a necessary condition for developing risk informed decision rules, and, thus, the relation between design basis analysis criteria and probabilistic risk assessment criteria is key. The discussion is in the context of making a decision under uncertainty for a reactor

Uncertainty & sensitivity analysis of economic assessment of lactic acid production from crude glycerol - impact of price correlations

DEFF Research Database (Denmark)

Loureiro da Costa Lira Gargalo, Carina; Carvalho, Ana; Gernaey, Krist V.

2017-01-01

In this work, we investigate the impact of the expected price volatility and correlations on the overall economic assessment of lactic acid production from crude glycerol. In particular, the goals of this study are three-fold: (i) to understand how uncertainty in the inputs propagates to the model...... outputs; (ii) to understand the effect of the degree of pairwise correlation between input uncertainties on each other and on the outputs from the economic model (Net Present Value); and lastly, (iii) to estimate the first-order as well as independent sensitivity indices so as to identify which...... of the input uncertainties in the economic analysis affect the estimated NPV the most. To this end, we implemented algorithms in Matlab (R2015a) based on Monte Carlo sampling with permutation using Latin Hypercube Sampling with Iman Conover correlation control (Sin et al., 2009).The results have shown...

LOFT differential pressure uncertainty analysis

International Nuclear Information System (INIS)

Evans, R.P.; Biladeau, G.L.; Quinn, P.A.

1977-03-01

A performance analysis of the LOFT differential pressure (ΔP) measurement is presented. Along with completed descriptions of test programs and theoretical studies that have been conducted on the ΔP, specific sources of measurement uncertainty are identified, quantified, and combined to provide an assessment of the ability of this measurement to satisfy the SDD 1.4.1C (June 1975) requirement of measurement of differential pressure

Uncertainty importance analysis using parametric moment ratio functions.

Science.gov (United States)

Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

2014-02-01

This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts efficiently guide the analyst to achieve a targeted reduction on the model output mean and variance by operating on the variances of model inputs. The unbiased and progressive unbiased Monte Carlo estimators are also derived for the parametric mean and variance ratio functions, respectively. Only a set of samples is needed for implementing the proposed importance analysis by the proposed estimators, thus the computational cost is free of input dimensionality. An analytical test example with highly nonlinear behavior is introduced for illustrating the engineering significance of the proposed importance analysis technique and verifying the efficiency and convergence of the derived Monte Carlo estimators. Finally, the moment ratio function is applied to a planar 10-bar structure for achieving a targeted 50% reduction of the model output variance. © 2013 Society for Risk Analysis.

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Directory of Open Access Journals (Sweden)

Dengsheng Lu

2012-01-01

Full Text Available Landsat Thematic mapper (TM image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

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

Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel

Science.gov (United States)

Stephens, Julia; Hubbard, Erin; Walter, Joel; McElroy, Tyler

2016-01-01

This paper presents methods and results of a detailed measurement uncertainty analysis that was performed for the 8- by 6-foot Supersonic Wind Tunnel located at the NASA Glenn Research Center. The statistical methods and engineering judgments used to estimate elemental uncertainties are described. The Monte Carlo method of propagating uncertainty was selected to determine the uncertainty of calculated variables of interest. A detailed description of the Monte Carlo method as applied for this analysis is provided. Detailed uncertainty results for the uncertainty in average free stream Mach number as well as other variables of interest are provided. All results are presented as random (variation in observed values about a true value), systematic (potential offset between observed and true value), and total (random and systematic combined) uncertainty. The largest sources contributing to uncertainty are determined and potential improvement opportunities for the facility are investigated.

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 and sensitivity analysis of early exposure results with the MACCS Reactor Accident Consequence Model

International Nuclear Information System (INIS)

Helton, J.C.; Johnson, J.D.; McKay, M.D.; Shiver, A.W.; Sprung, J.L.

1995-01-01

Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the early health effects associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 34 imprecisely known input variables on the following reactor accident consequences are studied: number of early fatalities, number of cases of prodromal vomiting, population dose within 10 mi of the reactor, population dose within 1000 mi of the reactor, individual early fatality probability within 1 mi of the reactor, and maximum early fatality distance. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: scaling factor for horizontal dispersion, dry deposition velocity, inhalation protection factor for nonevacuees, groundshine shielding factor for nonevacuees, early fatality hazard function alpha value for bone marrow exposure, and scaling factor for vertical dispersion

Uncertainty analysis of multiple canister repository model by large-scale calculation

International Nuclear Information System (INIS)

Tsujimoto, K.; Okuda, H.; Ahn, J.

2007-01-01

A prototype uncertainty analysis has been made by using the multiple-canister radionuclide transport code, VR, for performance assessment for the high-level radioactive waste repository. Fractures in the host rock determine main conduit of groundwater, and thus significantly affect the magnitude of radionuclide release rates from the repository. In this study, the probability distribution function (PDF) for the number of connected canisters in the same fracture cluster that bears water flow has been determined in a Monte-Carlo fashion by running the FFDF code with assumed PDFs for fracture geometry. The uncertainty for the release rate of 237 Np from a hypothetical repository containing 100 canisters has been quantitatively evaluated by using the VR code with PDFs for the number of connected canisters and the near field rock porosity. The calculation results show that the mass transport is greatly affected by (1) the magnitude of the radionuclide source determined by the number of connected canisters by the fracture cluster, and (2) the canister concentration effect in the same fracture network. The results also show the two conflicting tendencies that the more fractures in the repository model space, the greater average value but the smaller uncertainty of the peak fractional release rate is. To perform a vast amount of calculation, we have utilized the Earth Simulator and SR8000. The multi-level hybrid programming method is applied in the optimization to exploit high performance of the Earth Simulator. The Latin Hypercube Sampling has been utilized to reduce the number of samplings in Monte-Carlo calculation. (authors)

Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

Energy Technology Data Exchange (ETDEWEB)

Groen, E.A., E-mail: Evelyne.Groen@gmail.com [Wageningen University, P.O. Box 338, Wageningen 6700 AH (Netherlands); Heijungs, R. [Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam 1081 HV (Netherlands); Leiden University, Einsteinweg 2, Leiden 2333 CC (Netherlands)

2017-01-15

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlations between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.

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)

Uncertainty analysis of a low flow model for the Rhine River

NARCIS (Netherlands)

Demirel, M.C.; Booij, Martijn J.

2011-01-01

It is widely recognized that hydrological models are subject to parameter uncertainty. However, little attention has been paid so far to the uncertainty in parameters of the data-driven models like weights in neural networks. This study aims at applying a structured uncertainty analysis to a

Application of uncertainty analysis in conceptual fusion reactor design

International Nuclear Information System (INIS)

Wu, T.; Maynard, C.W.

1979-01-01

The theories of sensitivity and uncertainty analysis are described and applied to a new conceptual tokamak fusion reactor design--NUWMAK. The responses investigated in this study include the tritium breeding ratio, first wall Ti dpa and gas productions, nuclear heating in the blanket, energy leakage to the magnet, and the dpa rate in the superconducting magnet aluminum stabilizer. The sensitivities and uncertainties of these responses are calculated. The cost/benefit feature of proposed integral measurements is also studied through the uncertainty reductions of these responses

Evaluation of a Class of Simple and Effective Uncertainty Methods for Sparse Samples of Random Variables and Functions

Energy Technology Data Exchange (ETDEWEB)

Romero, Vicente [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bonney, Matthew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Schroeder, Benjamin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Weirs, V. Gregory [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a class of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10^-4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.

LOFT uncertainty-analysis methodology

International Nuclear Information System (INIS)

Lassahn, G.D.

1983-01-01

The methodology used for uncertainty analyses of measurements in the Loss-of-Fluid Test (LOFT) nuclear-reactor-safety research program is described and compared with other methodologies established for performing uncertainty analyses

LOFT uncertainty-analysis methodology

International Nuclear Information System (INIS)

Lassahn, G.D.

1983-01-01

The methodology used for uncertainty analyses of measurements in the Loss-of-Fluid Test (LOFT) nuclear reactor safety research program is described and compared with other methodologies established for performing uncertainty analyses

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.

Uncertainty correlation in stochastic safety analysis of natural circulation decay heat removal of liquid metal reactor

International Nuclear Information System (INIS)

Takata, Takashi; Yamaguchi, Akira

2009-01-01

Since various uncertainties of input variables are involved and nonlinearly-correlated in the Best Estimate (BE) plant dynamics code, it is of importance to evaluate the importance of input uncertainty to the computational results and to estimate the accuracy of the confidence level of the results. In order to estimate the importance and the accuracy, the authors have applied the stochastic safety analysis procedure using the Latin Hypercube sampling method to Liquid Metal Reactor (LMR) natural circulation Decay Heat Removal (DHR) phenomenon in the present paper. 17 input variables are chosen for the analyses and 5 influential variables, which affect the maximum coolant temperature at the core in a short period of time (several tens seconds), are selected to investigate the importance by comparing with the full-scope parametric analysis. As a result, it has been demonstrated that a comparative small number of samples is sufficient enough to estimate the dominant input variable and the confidence level. Furthermore, the influence of the sampling method on the accuracy of the upper tolerance limit (confidence level of 95%) has been examined based on the Wilks' formula. (author)

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

Uncertainty and sensitivity analysis of biokinetic models for radiopharmaceuticals used in nuclear medicine

International Nuclear Information System (INIS)

Li, W. B.; Hoeschen, C.

2010-01-01

Mathematical models for kinetics of radiopharmaceuticals in humans were developed and are used to estimate the radiation absorbed dose for patients in nuclear medicine by the International Commission on Radiological Protection and the Medical Internal Radiation Dose (MIRD) Committee. However, due to the fact that the residence times used were derived from different subjects, partially even with different ethnic backgrounds, a large variation in the model parameters propagates to a high uncertainty of the dose estimation. In this work, a method was developed for analysing the uncertainty and sensitivity of biokinetic models that are used to calculate the residence times. The biokinetic model of 18 F-FDG (FDG) developed by the MIRD Committee was analysed by this developed method. The sources of uncertainty of all model parameters were evaluated based on the experiments. The Latin hypercube sampling technique was used to sample the parameters for model input. Kinetic modelling of FDG in humans was performed. Sensitivity of model parameters was indicated by combining the model input and output, using regression and partial correlation analysis. The transfer rate parameter of plasma to other tissue fast is the parameter with the greatest influence on the residence time of plasma. Optimisation of biokinetic data acquisition in the clinical practice by exploitation of the sensitivity of model parameters obtained in this study is discussed. (authors)

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.

Uncertainty Management and Sensitivity Analysis

DEFF Research Database (Denmark)

Rosenbaum, Ralph K.; Georgiadis, Stylianos; Fantke, Peter

2018-01-01

Uncertainty is always there and LCA is no exception to that. The presence of uncertainties of different types and from numerous sources in LCA results is a fact, but managing them allows to quantify and improve the precision of a study and the robustness of its conclusions. LCA practice sometimes...... suffers from an imbalanced perception of uncertainties, justifying modelling choices and omissions. Identifying prevalent misconceptions around uncertainties in LCA is a central goal of this chapter, aiming to establish a positive approach focusing on the advantages of uncertainty management. The main...... objectives of this chapter are to learn how to deal with uncertainty in the context of LCA, how to quantify it, interpret and use it, and how to communicate it. The subject is approached more holistically than just focusing on relevant statistical methods or purely mathematical aspects. This chapter...

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.

On-Site Processing and Subsampling of Surface Soil Samples for the Analysis of Explosives

National Research Council Canada - National Science Library

Hewitt, Alan D

2003-01-01

The on-site implementation of a sampling and analysis plan for the determination of explosives residues exposed a large uncertainty in our ability to quickly obtain representative subsamples from either large (>500 g...

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.

Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling

DEFF Research Database (Denmark)

Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan

2008-01-01

uncertainty estimation (GLUE) procedure based on Markov chain Monte Carlo sampling is applied in order to improve the performance of the methodology in estimating parameters and posterior output distributions. The description of the spatial variations of the hydrological processes is accounted for by defining......In recent years, there has been an increase in the application of distributed, physically-based and integrated hydrological models. Many questions regarding how to properly calibrate and validate distributed models and assess the uncertainty of the estimated parameters and the spatially......-site validation must complement the usual time validation. In this study, we develop, through an application, a comprehensive framework for multi-criteria calibration and uncertainty assessment of distributed physically-based, integrated hydrological models. A revised version of the generalized likelihood...

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

Defining distinct negative beliefs about uncertainty: validating the factor structure of the Intolerance of Uncertainty Scale.

Science.gov (United States)

Sexton, Kathryn A; Dugas, Michel J

2009-06-01

This study examined the factor structure of the English version of the Intolerance of Uncertainty Scale (IUS; French version: M. H. Freeston, J. Rhéaume, H. Letarte, M. J. Dugas, & R. Ladouceur, 1994; English version: K. Buhr & M. J. Dugas, 2002) using a substantially larger sample than has been used in previous studies. Nonclinical undergraduate students and adults from the community (M age = 23.74 years, SD = 6.36; 73.0% female and 27.0% male) who participated in 16 studies in the Anxiety Disorders Laboratory at Concordia University in Montreal, Canada were randomly assigned to 2 datasets. Exploratory factor analysis with the 1st sample (n = 1,230) identified 2 factors: the beliefs that "uncertainty has negative behavioral and self-referent implications" and that "uncertainty is unfair and spoils everything." This 2-factor structure provided a good fit to the data (Bentler-Bonett normed fit index = .96, comparative fit index = .97, standardized root-mean residual = .05, root-mean-square error of approximation = .07) upon confirmatory factor analysis with the 2nd sample (n = 1,221). Both factors showed similarly high correlations with pathological worry, and Factor 1 showed stronger correlations with generalized anxiety disorder analogue status, trait anxiety, somatic anxiety, and depressive symptomatology. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Quantifying phenomenological importance in best-estimate plus uncertainty analyses

International Nuclear Information System (INIS)

Martin, Robert P.

2009-01-01

This paper describes a general methodology for quantifying the importance of specific phenomenological elements to analysis measures evaluated from non-parametric best-estimate plus uncertainty evaluation methodologies. The principal objective of an importance analysis is to reveal those uncertainty contributors having the greatest influence on key analysis measures. This characterization supports the credibility of the uncertainty analysis, the applicability of the analytical tools, and even the generic evaluation methodology through the validation of the engineering judgments that guided the evaluation methodology development. A demonstration of the importance analysis is provided using data from a sample problem considered in the development of AREVA's Realistic LBLOCA methodology. The results are presented against the original large-break LOCA Phenomena Identification and Ranking Table developed by the Technical Program Group responsible for authoring the Code Scaling, Applicability and Uncertainty methodology. (author)

Uncertainty analysis of NDA waste measurements using computer simulations

International Nuclear Information System (INIS)

Blackwood, L.G.; Harker, Y.D.; Yoon, W.Y.; Meachum, T.R.

2000-01-01

Uncertainty assessments for nondestructive radioassay (NDA) systems for nuclear waste are complicated by factors extraneous to the measurement systems themselves. Most notably, characteristics of the waste matrix (e.g., homogeneity) and radioactive source material (e.g., particle size distribution) can have great effects on measured mass values. Under these circumstances, characterizing the waste population is as important as understanding the measurement system in obtaining realistic uncertainty values. When extraneous waste characteristics affect measurement results, the uncertainty results are waste-type specific. The goal becomes to assess the expected bias and precision for the measurement of a randomly selected item from the waste population of interest. Standard propagation-of-errors methods for uncertainty analysis can be very difficult to implement in the presence of significant extraneous effects on the measurement system. An alternative approach that naturally includes the extraneous effects is as follows: (1) Draw a random sample of items from the population of interest; (2) Measure the items using the NDA system of interest; (3) Establish the true quantity being measured using a gold standard technique; and (4) Estimate bias by deriving a statistical regression model comparing the measurements on the system of interest to the gold standard values; similar regression techniques for modeling the standard deviation of the difference values gives the estimated precision. Actual implementation of this method is often impractical. For example, a true gold standard confirmation measurement may not exist. A more tractable implementation is obtained by developing numerical models for both the waste material and the measurement system. A random sample of simulated waste containers generated by the waste population model serves as input to the measurement system model. This approach has been developed and successfully applied to assessing the quantity of

Sensitivity and uncertainty analysis for fission product decay heat calculations

International Nuclear Information System (INIS)

Rebah, J.; Lee, Y.K.; Nimal, J.C.; Nimal, B.; Luneville, L.; Duchemin, B.

1994-01-01

The calculated uncertainty in decay heat due to the uncertainty in basic nuclear data given in the CEA86 Library, is presented. Uncertainties in summation calculation arise from several sources: fission product yields, half-lives and average decay energies. The correlation between basic data is taken into account. The uncertainty analysis were obtained for thermal-neutron-induced fission of U235 and Pu239 in the case of burst fission and irradiation time. The calculated decay heat in this study is compared with experimental results and with new calculation using the JEF2 Library. (from authors) 6 figs., 19 refs

The influence of uncertainties of measurements in laboratory performance evaluation by intercomparison program in radionuclide analyses of environmental samples

International Nuclear Information System (INIS)

Tauhata, L.; Vianna, M.E.; Oliveira, A.E. de; Clain, A.F.; Ferreira, A.C.M.; Bernardes, E.M.

2000-01-01

The accuracy and precision of results of the radionuclide analyses in environmental samples are widely claimed internationally due to its consequences in the decision process coupled to evaluation of environmental pollution, impact, internal and external population exposure. These characteristics of measurement of the laboratories can be shown clearly using intercomparison data, due to the existence of a reference value and the need of three determinations for each analysis. In intercomparison studies accuracy in radionuclide assays in low-level environmental samples has usually been the main focus in performance evaluation and it can be estimated by taking into account the deviation between the experimental laboratory mean value and the reference value. The laboratory repeatability of measurements or their standard deviation is seldom included in performance evaluation. In order to show the influence of the uncertainties in performance evaluation of the laboratories, data of 22 intercomparison runs which distributed 790 spiked environmental samples to 20 Brazilian participant laboratories were compared, using the 'Normalised Standard Deviation' as statistical criteria for performance evaluation of U.S.EPA. It mainly takes into account the laboratory accuracy and the performance evaluation using the same data classified by normalised standard deviation modified by a weight reactor that includes the individual laboratory uncertainty. The results show a relative decrease in laboratory performance in each radionuclide assay: 1.8% for 65 Zn, 2.8% for 40 K, 3.4 for 60 Co, 3.7% for 134 Cs, 4.0% for 137 Cs, 4.4% for Th and U nat , 4.5% for 3 H, 6.3% for 133 Ba, 8.6% for 90 Sr, 10.6% for Gross Alpha, 10.9% for 106 Ru, 11.1% for 226 Ra, 11.5% for Gross Beta and 13.6% for 228 Ra. The changes in the parameters of the statistical distribution function were negligible and the distribution remained as Gaussian type for all radionuclides analysed. Data analyses in terms of

Introduction to risk and uncertainty in hydrosystem engineering

CERN Document Server

Goodarzi, Ehsan; Teang Shui, Lee

2013-01-01

Water engineers require knowledge of stochastic, frequency concepts, uncertainty analysis, risk assessment, and the processes that predict unexpected events. This book presents the basics of stochastic, risk and uncertainty analysis, and random sampling techniques in conjunction with straightforward examples which are solved step by step. In addition, appropriate Excel functions are included as an alternative to solve the examples, and two real case studies is presented in the last chapters of book.

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.

Radiocarbon Analysis to Calculate New End-Member Values for Biomass Burning Source Samples Specific to the Bay Area

Science.gov (United States)

Yoon, S.; Kirchstetter, T.; Fairley, D.; Sheesley, R. J.; Tang, X.

2017-12-01

Elemental carbon (EC), also known as black carbon or soot, is an important particulate air pollutant that contributes to climate forcing through absorption of solar radiation and to adverse human health impacts through inhalation. Both fossil fuel combustion and biomass burning, via residential firewood burning, agricultural burning, wild fires, and controlled burns, are significant sources of EC. Our ability to successfully control ambient EC concentrations requires understanding the contribution of these different emission sources. Radiocarbon (14C) analysis has been increasingly used as an apportionment tool to distinguish between EC from fossil fuel and biomass combustion sources. However, there are uncertainties associated with this method including: 1) uncertainty associated with the isolation of EC to be used for radiocarbon analysis (e.g., inclusion of organic carbon, blank contamination, recovery of EC, etc.) 2) uncertainty associated with the radiocarbon signature of the end member. The objective of this research project is to utilize laboratory experiments to evaluate some of these uncertainties, particularly for EC sources that significantly impact the San Francisco Bay Area. Source samples of EC only and a mix of EC and organic carbon (OC) were produced for this study to represent known emission sources and to approximate the mixing of EC and OC that would be present in the atmosphere. These samples include a combination of methane flame soot, various wood smoke samples (i.e. cedar, oak, sugar pine, pine at various ages, etc.), meat cooking, and smoldering cellulose smoke. EC fractions were isolated using a Sunset Laboratory's thermal optical transmittance carbon analyzer. For 14C analysis, samples were sent to Woods Hole Oceanographic Institution for isotope analysis using an accelerated mass spectrometry. End member values and uncertainties for the EC isolation utilizing this method will be reported.

Probabilistic accident consequence uncertainty analysis -- Early health effects uncertainty assessment. Volume 2: Appendices

Energy Technology Data Exchange (ETDEWEB)

Haskin, F.E. [Univ. of New Mexico, Albuquerque, NM (United States); Harper, F.T. [Sandia National Labs., Albuquerque, NM (United States); Goossens, L.H.J.; Kraan, B.C.P. [Delft Univ. of Technology (Netherlands)

1997-12-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA early health effects models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on early health effects, (4) short biographies of the experts, and (5) the aggregated results of their responses.

Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for internal dosimetry. Volume 2: Appendices

Energy Technology Data Exchange (ETDEWEB)

Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Harrison, J.D. [National Radiological Protection Board (United Kingdom); Harper, F.T. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States)

1998-04-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA internal dosimetry models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on internal dosimetry, (4) short biographies of the experts, and (5) the aggregated results of their responses.

The uncertainty in physical measurements an introduction to data analysis in the physics laboratory

CERN Document Server

Fornasini, Paolo

2008-01-01

All measurements of physical quantities are affected by uncertainty. Understanding the origin of uncertainty, evaluating its extent and suitably taking it into account in data analysis is essential for assessing the degree of accuracy of phenomenological relationships and physical laws in both scientific research and technological applications. The Uncertainty in Physical Measurements: An Introduction to Data Analysis in the Physics Laboratory presents an introduction to uncertainty and to some of the most common procedures of data analysis. This book will serve the reader well by filling the gap between tutorial textbooks and highly specialized monographs. The book is divided into three parts. The first part is a phenomenological introduction to measurement and uncertainty: properties of instruments, different causes and corresponding expressions of uncertainty, histograms and distributions, and unified expression of uncertainty. The second part contains an introduction to probability theory, random variable...

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

Conformity Assessment in Nuclear Material and Environmental Sample Analysis

International Nuclear Information System (INIS)

Aregbe, Y.; Jakopic, R.; Richter, S.; Venchiarutti, C.

2015-01-01

Safeguards conclusions are based to a large extent on comparison of measurement results between operator and safeguards laboratories. Measurement results must state traceability and uncertainties to be comparable. Recent workshops held at the IAEA and in the frame of the European Safeguards Research and Development Association (ESARDA), reviewed different approaches for Nuclear Material Balance Evaluation (MBE). Among those, the ''bottom-up'' approach requires assessment of operators and safeguards laboratories measurement systems and capabilities. Therefore, inter-laboratory comparisons (ILCs) with independent reference values provided for decades by JRC-IRMM, CEA/CETAMA and US DOE are instrumental to shed light on the current state of practice in measurements of nuclear material and environmental swipe samples. Participating laboratories are requested to report the measurement results with associated uncertainties, and have the possibility to benchmark those results against independent and traceable reference values. The measurement capability of both the IAEA Network of Analytical Laboratories (NWAL) and the nuclear operator's analytical services participating in ILCs can be assessed against the independent reference values as well as against internationally agreed quality goals, in compliance with ISO 13528:2005. The quality goals for nuclear material analysis are the relative combined standard uncertainties listed in the ITV2010. Concerning environmental swipe sample analysis, the IAEA defined measurement quality goals applied in conformity assessment. The paper reports examples from relevant inter-laboratory comparisons, looking at laboratory performance according to the purpose of the measurement and the possible use of the result in line with the IUPAC International Harmonized Protocol. Tendencies of laboratories to either overestimate and/or underestimate uncertainties are discussed using straightforward graphical tools to evaluate

Performance Assessment Uncertainty Analysis for Japan's HLW Program Feasibility Study (H12)

International Nuclear Information System (INIS)

BABA, T.; ISHIGURO, K.; ISHIHARA, Y.; SAWADA, A.; UMEKI, H.; WAKASUGI, K.; WEBB, ERIK K.

1999-01-01

Most HLW programs in the world recognize that any estimate of long-term radiological performance must be couched in terms of the uncertainties derived from natural variation, changes through time and lack of knowledge about the essential processes. The Japan Nuclear Cycle Development Institute followed a relatively standard procedure to address two major categories of uncertainty. First, a FEatures, Events and Processes (FEPs) listing, screening and grouping activity was pursued in order to define the range of uncertainty in system processes as well as possible variations in engineering design. A reference and many alternative cases representing various groups of FEPs were defined and individual numerical simulations performed for each to quantify the range of conceptual uncertainty. Second, parameter distributions were developed for the reference case to represent the uncertainty in the strength of these processes, the sequencing of activities and geometric variations. Both point estimates using high and low values for individual parameters as well as a probabilistic analysis were performed to estimate parameter uncertainty. A brief description of the conceptual model uncertainty analysis is presented. This paper focuses on presenting the details of the probabilistic parameter uncertainty assessment

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)

Methods and computer codes for probabilistic sensitivity and uncertainty analysis

International Nuclear Information System (INIS)

Vaurio, J.K.

1985-01-01

This paper describes the methods and applications experience with two computer codes that are now available from the National Energy Software Center at Argonne National Laboratory. The purpose of the SCREEN code is to identify a group of most important input variables of a code that has many (tens, hundreds) input variables with uncertainties, and do this without relying on judgment or exhaustive sensitivity studies. Purpose of the PROSA-2 code is to propagate uncertainties and calculate the distributions of interesting output variable(s) of a safety analysis code using response surface techniques, based on the same runs used for screening. Several applications are discussed, but the codes are generic, not tailored to any specific safety application code. They are compatible in terms of input/output requirements but also independent of each other, e.g., PROSA-2 can be used without first using SCREEN if a set of important input variables has first been selected by other methods. Also, although SCREEN can select cases to be run (by random sampling), a user can select cases by other methods if he so prefers, and still use the rest of SCREEN for identifying important input variables

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.

Analysis of actuator delay and its effect on uncertainty quantification for real-time hybrid simulation

Science.gov (United States)

Chen, Cheng; Xu, Weijie; Guo, Tong; Chen, Kai

2017-10-01

Uncertainties in structure properties can result in different responses in hybrid simulations. Quantification of the effect of these uncertainties would enable researchers to estimate the variances of structural responses observed from experiments. This poses challenges for real-time hybrid simulation (RTHS) due to the existence of actuator delay. Polynomial chaos expansion (PCE) projects the model outputs on a basis of orthogonal stochastic polynomials to account for influences of model uncertainties. In this paper, PCE is utilized to evaluate effect of actuator delay on the maximum displacement from real-time hybrid simulation of a single degree of freedom (SDOF) structure when accounting for uncertainties in structural properties. The PCE is first applied for RTHS without delay to determine the order of PCE, the number of sample points as well as the method for coefficients calculation. The PCE is then applied to RTHS with actuator delay. The mean, variance and Sobol indices are compared and discussed to evaluate the effects of actuator delay on uncertainty quantification for RTHS. Results show that the mean and the variance of the maximum displacement increase linearly and exponentially with respect to actuator delay, respectively. Sensitivity analysis through Sobol indices also indicates the influence of the single random variable decreases while the coupling effect increases with the increase of actuator delay.

Probabilistic Accident Consequence Uncertainty Analysis of the Food Chain Module in the COSYMA Package (invited paper)

International Nuclear Information System (INIS)

Brown, J.; Jones, J.A.

2000-01-01

This paper describes the uncertainty analysis of the food chain module of COSYMA and the uncertainty distributions on the input parameter values for the food chain model provided by the expert panels that were used for the analysis. Two expert panels were convened, covering the areas of soil and plant transfer processes and transfer to and through animals. The aggregated uncertainty distributions from the experts for the elicited variables were used in an uncertainty analysis of the food chain module of COSYMA. The main aim of the module analysis was to identify those parameters whose uncertainty makes large contributions to the overall uncertainty and so should be included in the overall analysis. (author)

Observation uncertainty in reversible Markov chains.

Science.gov (United States)

Metzner, Philipp; Weber, Marcus; Schütte, Christof

2010-09-01

In many applications one is interested in finding a simplified model which captures the essential dynamical behavior of a real life process. If the essential dynamics can be assumed to be (approximately) memoryless then a reasonable choice for a model is a Markov model whose parameters are estimated by means of Bayesian inference from an observed time series. We propose an efficient Monte Carlo Markov chain framework to assess the uncertainty of the Markov model and related observables. The derived Gibbs sampler allows for sampling distributions of transition matrices subject to reversibility and/or sparsity constraints. The performance of the suggested sampling scheme is demonstrated and discussed for a variety of model examples. The uncertainty analysis of functions of the Markov model under investigation is discussed in application to the identification of conformations of the trialanine molecule via Robust Perron Cluster Analysis (PCCA+) .

Considering sampling strategy and cross-section complexity for estimating the uncertainty of discharge measurements using the velocity-area method

Science.gov (United States)

Despax, Aurélien; Perret, Christian; Garçon, Rémy; Hauet, Alexandre; Belleville, Arnaud; Le Coz, Jérôme; Favre, Anne-Catherine

2016-02-01

Streamflow time series provide baseline data for many hydrological investigations. Errors in the data mainly occur through uncertainty in gauging (measurement uncertainty) and uncertainty in the determination of the stage-discharge relationship based on gaugings (rating curve uncertainty). As the velocity-area method is the measurement technique typically used for gaugings, it is fundamental to estimate its level of uncertainty. Different methods are available in the literature (ISO 748, Q + , IVE), all with their own limitations and drawbacks. Among the terms forming the combined relative uncertainty in measured discharge, the uncertainty component relating to the limited number of verticals often includes a large part of the relative uncertainty. It should therefore be estimated carefully. In ISO 748 standard, proposed values of this uncertainty component only depend on the number of verticals without considering their distribution with respect to the depth and velocity cross-sectional profiles. The Q + method is sensitive to a user-defined parameter while it is questionable whether the IVE method is applicable to stream-gaugings performed with a limited number of verticals. To address the limitations of existing methods, this paper presents a new methodology, called FLow Analog UnceRtainty Estimation (FLAURE), to estimate the uncertainty component relating to the limited number of verticals. High-resolution reference gaugings (with 31 and more verticals) are used to assess the uncertainty component through a statistical analysis. Instead of subsampling purely randomly the verticals of these reference stream-gaugings, a subsampling method is developed in a way that mimicks the behavior of a hydrometric technician. A sampling quality index (SQI) is suggested and appears to be a more explanatory variable than the number of verticals. This index takes into account the spacing between verticals and the variation of unit flow between two verticals. To compute the

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

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

New strategies for quantifying and propagating nuclear data uncertainty in CUSA

International Nuclear Information System (INIS)

Zhao, Qiang; Zhang, Chunyan; Hao, Chen; Li, Fu; Wang, Dongyong; Yu, Yan

2016-01-01

Highlights: • An efficient sampling method based on LHS combined with Cholesky decomposition conversion is proposed. • A code of generating multi-group covariance matrices has been developed. • The uncertainty and sensitivity results of CUSA agree well with TSUNAMI-1D. - Abstract: The uncertainties of nuclear cross sections are propagated to the key parameters of nuclear reactor core through transport calculation. The statistical sampling method can be used to quantify and propagate nuclear data uncertainty in nuclear reactor physics calculations. In order to use statistical sampling method two key technical problems, method of generating multi-group covariance matrices and sampling method, should be considered reasonably and efficiently. In this paper, a method of transforming nuclear cross section covariance matrix in multi-group form into users' group structures based on the flat-flux approximation has been studied in depth. And most notably, an efficient sampling method has been proposed, which is based on Latin Hypercube Sampling (LHS) combined with Cholesky decomposition conversion. Based on those method, two modules named T-COCCO and GUIDE have been developed and have been successfully added into the code for uncertainty and sensitivity analysis (CUSA). The new modules have been verified respectively. Numerical results for the TMI-1 pin-cell case are presented and compared to TSUNAMI-1D. The comparison of the results further support that the methods and the computational tool developed in this work can be used to conduct sensitivity and uncertainty analysis for nuclear cross sections.

New strategies for quantifying and propagating nuclear data uncertainty in CUSA

Energy Technology Data Exchange (ETDEWEB)

Zhao, Qiang; Zhang, Chunyan [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, College of Nuclear Science and Technology, Harbin Engineering University, Harbin (China); Hao, Chen, E-mail: haochen.heu@163.com [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, College of Nuclear Science and Technology, Harbin Engineering University, Harbin (China); Li, Fu [Institute of Nuclear and New Energy Technology(INET), Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing (China); Wang, Dongyong [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, College of Nuclear Science and Technology, Harbin Engineering University, Harbin (China); School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an (China); Yu, Yan [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, College of Nuclear Science and Technology, Harbin Engineering University, Harbin (China)

2016-10-15

Highlights: • An efficient sampling method based on LHS combined with Cholesky decomposition conversion is proposed. • A code of generating multi-group covariance matrices has been developed. • The uncertainty and sensitivity results of CUSA agree well with TSUNAMI-1D. - Abstract: The uncertainties of nuclear cross sections are propagated to the key parameters of nuclear reactor core through transport calculation. The statistical sampling method can be used to quantify and propagate nuclear data uncertainty in nuclear reactor physics calculations. In order to use statistical sampling method two key technical problems, method of generating multi-group covariance matrices and sampling method, should be considered reasonably and efficiently. In this paper, a method of transforming nuclear cross section covariance matrix in multi-group form into users' group structures based on the flat-flux approximation has been studied in depth. And most notably, an efficient sampling method has been proposed, which is based on Latin Hypercube Sampling (LHS) combined with Cholesky decomposition conversion. Based on those method, two modules named T-COCCO and GUIDE have been developed and have been successfully added into the code for uncertainty and sensitivity analysis (CUSA). The new modules have been verified respectively. Numerical results for the TMI-1 pin-cell case are presented and compared to TSUNAMI-1D. The comparison of the results further support that the methods and the computational tool developed in this work can be used to conduct sensitivity and uncertainty analysis for nuclear cross sections.

Large scale applicability of a Fully Adaptive Non-Intrusive Spectral Projection technique: Sensitivity and uncertainty analysis of a transient

International Nuclear Information System (INIS)

Perkó, Zoltán; Lathouwers, Danny; Kloosterman, Jan Leen; Hagen, Tim van der

2014-01-01

Highlights: • Grid and basis adaptive Polynomial Chaos techniques are presented for S and U analysis. • Dimensionality reduction and incremental polynomial order reduce computational costs. • An unprotected loss of flow transient is investigated in a Gas Cooled Fast Reactor. • S and U analysis is performed with MC and adaptive PC methods, for 42 input parameters. • PC accurately estimates means, variances, PDFs, sensitivities and uncertainties. - Abstract: Since the early years of reactor physics the most prominent sensitivity and uncertainty (S and U) analysis methods in the nuclear community have been adjoint based techniques. While these are very effective for pure neutronics problems due to the linearity of the transport equation, they become complicated when coupled non-linear systems are involved. With the continuous increase in computational power such complicated multi-physics problems are becoming progressively tractable, hence affordable and easily applicable S and U analysis tools also have to be developed in parallel. For reactor physics problems for which adjoint methods are prohibitive Polynomial Chaos (PC) techniques offer an attractive alternative to traditional random sampling based approaches. At TU Delft such PC methods have been studied for a number of years and this paper presents a large scale application of our Fully Adaptive Non-Intrusive Spectral Projection (FANISP) algorithm for performing the sensitivity and uncertainty analysis of a Gas Cooled Fast Reactor (GFR) Unprotected Loss Of Flow (ULOF) transient. The transient was simulated using the Cathare 2 code system and a fully detailed model of the GFR2400 reactor design that was investigated in the European FP7 GoFastR project. Several sources of uncertainty were taken into account amounting to an unusually high number of stochastic input parameters (42) and numerous output quantities were investigated. The results show consistently good performance of the applied adaptive PC

Incorporating uncertainty in RADTRAN 6.0 input files.

Energy Technology Data Exchange (ETDEWEB)

Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John (Alion Science and Technology)

2010-02-01

Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine is required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.

Analysis of submicrogram samples by INAA

Energy Technology Data Exchange (ETDEWEB)

Lindstrom, D J [National Aeronautics and Space Administration, Houston, TX (USA). Lyndon B. Johnson Space Center

1990-12-20

Procedure have been developed to increase the sensitivity of instrumental neutron activation analysis (INAA) so that cosmic-dust samples weighing only 10{sup -9}-10{sup -7} g are routinely analyzed for a sizable number of elements. The primary differences from standard techniques are: (1) irradiation of the samples is much more intense, (2) gamma ray assay of the samples is done using long counting times and large Ge detectors that are operated in an excellent low-background facility, (3) specially prepared glass standards are used, (4) samples are too small to be weighed routinely and concentrations must be obtained indirectly, (5) sample handling is much more difficult, and contamination of small samples with normally insignificant amounts of contaminants is difficult to prevent. In spite of the difficulties, INAA analyses have been done on 15 cosmic-dust particles and a large number of other stratospheric particles. Two-sigma detection limits for some elements are in the range of femtograms (10{sup -15} g), e.g. Co=11, Sc=0.9, Sm=0.2 A particle weighing just 0.2 ng was analyzed, obtaining abundances with relative analytical uncertainties of less than 10% for four elements (Fe, Co, Ni and Sc), which were sufficient to allow identification of the particle as chondritic interplanetary dust. Larger samples allow abundances of twenty or more elements to be obtained. (orig.).

Stochastic and epistemic uncertainty propagation in LCA

DEFF Research Database (Denmark)

Clavreul, Julie; Guyonnet, Dominique; Tonini, Davide

2013-01-01

of epistemic uncertainty representation using fuzzy intervals. The propagation methods used are the Monte Carlo analysis for probability distribution and an optimisation on alpha-cuts for fuzzy intervals. The proposed method (noted as Independent Random Set, IRS) generalizes the process of random sampling...... to probability distributions as well as fuzzy intervals, thus making the simultaneous use of both representations possible.The results highlight the fundamental difference between the probabilistic and possibilistic representations: while the Monte Carlo analysis generates a single probability distribution...... or expert judgement (epistemic uncertainty). The possibility theory has been developed over the last decades to address this problem. The objective of this study is to present a methodology that combines probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent...

A Peep into the Uncertainty-Complexity-Relevance Modeling Trilemma through Global Sensitivity and Uncertainty Analysis

Science.gov (United States)

Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.

2014-12-01

Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping

Evaluation of the uncertainty of environmental measurements of radioactivity

International Nuclear Information System (INIS)

Heydorn, K.

2003-01-01

Full text: The almost universal acceptance of the concept of uncertainty has led to its introduction into the ISO 17025 standard for general requirements to testing and calibration laboratories. This means that not only scientists, but also legislators, politicians, the general population - and perhaps even the press - expect to see all future results associated with an expression of their uncertainty. Results obtained by measurement of radioactivity have routinely been associated with an expression of their uncertainty, based on the so-called counting statistics. This is calculated together with the actual result on the assumption that the number of counts observed has a Poisson distribution with equal mean and variance. Most of the nuclear scientific community has therefore assumed that it already complied with the latest ISO 17025 requirements. Counting statistics, however, express only the variability observed among repeated measurements of the same sample under the same counting conditions, which is equivalent to the term repeatability used in quantitative analysis. Many other sources of uncertainty need to be taken into account before a statement of the uncertainty of the actual result can be made. As the first link in the traceability chain calibration is always an important uncertainty component in any kind of measurement. For radioactivity measurements in particular we find that counting geometry assumes the greatest importance, because it is often not possible to measure a standard and a control sample under exactly the same conditions. In the case of large samples we have additional uncertainty components associated with sample heterogeneity and its influence on self-absorption and counting efficiency. In low-level environmental measurements we have an additional risk of sample contamination, but the most important contribution to uncertainty is usually the representativity of the sample being analysed. For uniform materials this can be expressed by the

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.

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.

Uncertainties in elemental quantitative analysis by PIXE

International Nuclear Information System (INIS)

Montenegro, E.C.; Baptista, G.B.; Paschoa, A.S.; Barros Leite, C.V.

1979-01-01

The effects of the degree of non-uniformity of the particle beam, matrix composition and matrix thickness in a quantitative elemental analysis by particle induced X-ray emission (PIXE) are discussed and a criterion to evaluate the resulting degree of uncertainty in the mass determination by this method is established. (Auth.)

Quality in environmental science for policy: Assessing uncertainty as a component of policy analysis

International Nuclear Information System (INIS)

Maxim, Laura; Sluijs, Jeroen P. van der

2011-01-01

The sheer number of attempts to define and classify uncertainty reveals an awareness of its importance in environmental science for policy, though the nature of uncertainty is often misunderstood. The interdisciplinary field of uncertainty analysis is unstable; there are currently several incomplete notions of uncertainty leading to different and incompatible uncertainty classifications. One of the most salient shortcomings of present-day practice is that most of these classifications focus on quantifying uncertainty while ignoring the qualitative aspects that tend to be decisive in the interface between science and policy. Consequently, the current practices of uncertainty analysis contribute to increasing the perceived precision of scientific knowledge, but do not adequately address its lack of socio-political relevance. The 'positivistic' uncertainty analysis models (like those that dominate the fields of climate change modelling and nuclear or chemical risk assessment) have little social relevance, as they do not influence negotiations between stakeholders. From the perspective of the science-policy interface, the current practices of uncertainty analysis are incomplete and incorrectly focused. We argue that although scientific knowledge produced and used in a context of political decision-making embodies traditional scientific characteristics, it also holds additional properties linked to its influence on social, political, and economic relations. Therefore, the significance of uncertainty cannot be assessed based on quality criteria that refer to the scientific content only; uncertainty must also include quality criteria specific to the properties and roles of this scientific knowledge within political, social, and economic contexts and processes. We propose a conceptual framework designed to account for such substantive, contextual, and procedural criteria of knowledge quality. At the same time, the proposed framework includes and synthesizes the various

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.

Preliminary Uncertainty Analysis 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 (KAERI) developed on-line digital core protection and monitoring systems, called SCOPS and SCOMS as a part of SMART plant protection and monitoring system. SCOPS simplified the protection system by directly connecting the four RSPT signals to each core protection channel and eliminated the control element assembly calculator (CEAC) hardware. SCOMS adopted DPCM3D method in synthesizing core power distribution instead of Fourier expansion method being used in conventional PWRs. The DPCM3D method produces a synthetic 3-D power distribution by coupling a neutronics code and measured in-core detector signals. The overall uncertainty analysis methodology which is used statistically combining uncertainty components of SMART core protection and monitoring system was developed. In this paper, preliminary overall uncertainty factors for SCOPS/SCOMS of SMART initial core were evaluated by applying newly developed uncertainty analysis method

Uncertainties in criticality analysis which affect the storage and transportation of LWR fuel

International Nuclear Information System (INIS)

Napolitani, D.G.

1989-01-01

Satisfying the design criteria for subcriticality with uncertainties affects: the capacity of LWR storage arrays, maximum allowable enrichment, minimum allowable burnup and economics of various storage options. There are uncertainties due to: calculational method, data libraries, geometric limitations, modelling bias, the number and quality of benchmarks performed and mechanical uncertainties in the array. Yankee Atomic Electric Co. (YAEC) has developed and benchmarked methods to handle: high density storage rack designs, pin consolidation, low density moderation and burnup credit. The uncertainties associated with such criticality analysis are quantified on the basis of clean criticals, power reactor criticals and intercomparison of independent analysis methods

Uncertainty Characterization of Reactor Vessel Fracture Toughness

International Nuclear Information System (INIS)

Li, Fei; Modarres, Mohammad

2002-01-01

To perform fracture mechanics analysis of reactor vessel, fracture toughness (K Ic ) at various temperatures would be necessary. In a best estimate approach, K Ic uncertainties resulting from both lack of sufficient knowledge and randomness in some of the variables of K Ic must be characterized. Although it may be argued that there is only one type of uncertainty, which is lack of perfect knowledge about the subject under study, as a matter of practice K Ic uncertainties can be divided into two types: aleatory and epistemic. Aleatory uncertainty is related to uncertainty that is very difficult to reduce, if not impossible; epistemic uncertainty, on the other hand, can be practically reduced. Distinction between aleatory and epistemic uncertainties facilitates decision-making under uncertainty and allows for proper propagation of uncertainties in the computation process. Typically, epistemic uncertainties representing, for example, parameters of a model are sampled (to generate a 'snapshot', single-value of the parameters), but the totality of aleatory uncertainties is carried through the calculation as available. In this paper a description of an approach to account for these two types of uncertainties associated with K Ic has been provided. (authors)

The uncertainties in estimating measurement uncertainties

International Nuclear Information System (INIS)

Clark, J.P.; Shull, A.H.

1994-01-01

All measurements include some error. Whether measurements are used for accountability, environmental programs or process support, they are of little value unless accompanied by an estimate of the measurements uncertainty. This fact is often overlooked by the individuals who need measurements to make decisions. This paper will discuss the concepts of measurement, measurements errors (accuracy or bias and precision or random error), physical and error models, measurement control programs, examples of measurement uncertainty, and uncertainty as related to measurement quality. Measurements are comparisons of unknowns to knowns, estimates of some true value plus uncertainty; and are no better than the standards to which they are compared. Direct comparisons of unknowns that match the composition of known standards will normally have small uncertainties. In the real world, measurements usually involve indirect comparisons of significantly different materials (e.g., measuring a physical property of a chemical element in a sample having a matrix that is significantly different from calibration standards matrix). Consequently, there are many sources of error involved in measurement processes that can affect the quality of a measurement and its associated uncertainty. How the uncertainty estimates are determined and what they mean is as important as the measurement. The process of calculating the uncertainty of a measurement itself has uncertainties that must be handled correctly. Examples of chemistry laboratory measurement will be reviewed in this report and recommendations made for improving measurement uncertainties

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Energy Technology Data Exchange (ETDEWEB)

Ettehadtavakkol, Amin, E-mail: amin.ettehadtavakkol@ttu.edu [Texas Tech University (United States); Jablonowski, Christopher [Shell Exploration and Production Company (United States); Lake, Larry [University of Texas at Austin (United States)

2017-04-15

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

International Nuclear Information System (INIS)

Ettehadtavakkol, Amin; Jablonowski, Christopher; Lake, Larry

2017-01-01

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

Model parameter uncertainty analysis for annual field-scale P loss model

Science.gov (United States)

Phosphorous (P) loss models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. All P loss models, however, have an inherent amount of uncertainty associated with them. In this study, we conducted an uncertainty analysis with ...

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

SENSIT: a cross-section and design sensitivity and uncertainty analysis code

International Nuclear Information System (INIS)

Gerstl, S.A.W.

1980-01-01

SENSIT computes the sensitivity and uncertainty of a calculated integral response (such as a dose rate) due to input cross sections and their uncertainties. Sensitivity profiles are computed for neutron and gamma-ray reaction cross sections of standard multigroup cross section sets and for secondary energy distributions (SEDs) of multigroup scattering matrices. In the design sensitivity mode, SENSIT computes changes in an integral response due to design changes and gives the appropriate sensitivity coefficients. Cross section uncertainty analyses are performed for three types of input data uncertainties: cross-section covariance matrices for pairs of multigroup reaction cross sections, spectral shape uncertainty parameters for secondary energy distributions (integral SED uncertainties), and covariance matrices for energy-dependent response functions. For all three types of data uncertainties SENSIT computes the resulting variance and estimated standard deviation in an integral response of interest, on the basis of generalized perturbation theory. SENSIT attempts to be more comprehensive than earlier sensitivity analysis codes, such as SWANLAKE

Uncertainties in Earthquake Loss Analysis: A Case Study From Southern California

Science.gov (United States)

Mahdyiar, M.; Guin, J.

2005-12-01

Probabilistic earthquake hazard and loss analyses play important roles in many areas of risk management, including earthquake related public policy and insurance ratemaking. Rigorous loss estimation for portfolios of properties is difficult since there are various types of uncertainties in all aspects of modeling and analysis. It is the objective of this study to investigate the sensitivity of earthquake loss estimation to uncertainties in regional seismicity, earthquake source parameters, ground motions, and sites' spatial correlation on typical property portfolios in Southern California. Southern California is an attractive region for such a study because it has a large population concentration exposed to significant levels of seismic hazard. During the last decade, there have been several comprehensive studies of most regional faults and seismogenic sources. There have also been detailed studies on regional ground motion attenuations and regional and local site responses to ground motions. This information has been used by engineering seismologists to conduct regional seismic hazard and risk analysis on a routine basis. However, one of the more difficult tasks in such studies is the proper incorporation of uncertainties in the analysis. From the hazard side, there are uncertainties in the magnitudes, rates and mechanisms of the seismic sources and local site conditions and ground motion site amplifications. From the vulnerability side, there are considerable uncertainties in estimating the state of damage of buildings under different earthquake ground motions. From an analytical side, there are challenges in capturing the spatial correlation of ground motions and building damage, and integrating thousands of loss distribution curves with different degrees of correlation. In this paper we propose to address some of these issues by conducting loss analyses of a typical small portfolio in southern California, taking into consideration various source and ground

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

Uncertainty and sensitivity analysis applied to coupled code calculations for a VVER plant transient

International Nuclear Information System (INIS)

Langenbuch, S.; Krzykacz-Hausmann, B.; Schmidt, K. D.

2004-01-01

The development of coupled codes, combining thermal-hydraulic system codes and 3D neutron kinetics, is an important step to perform best-estimate plant transient calculations. It is generally agreed that the application of best-estimate methods should be supplemented by an uncertainty and sensitivity analysis to quantify the uncertainty of the results. The paper presents results from the application of the GRS uncertainty and sensitivity method for a VVER-440 plant transient, which was already studied earlier for the validation of coupled codes. For this application, the main steps of the uncertainty method are described. Typical results of the method applied to the analysis of the plant transient by several working groups using different coupled codes are presented and discussed The results demonstrate the capability of an uncertainty and sensitivity analysis. (authors)

Assessing the Uncertainty in QUANTEC's Dose–Response Relation of Lung and Spinal Cord With a Bootstrap Analysis

Energy Technology Data Exchange (ETDEWEB)

Wedenberg, Minna, E-mail: minna.wedenberg@raysearchlabs.com

2013-11-15

Purpose: To apply a statistical bootstrap analysis to assess the uncertainty in the dose–response relation for the endpoints pneumonitis and myelopathy reported in the QUANTEC review. Methods and Materials: The bootstrap method assesses the uncertainty of the estimated population-based dose-response relation due to sample variability, which reflects the uncertainty due to limited numbers of patients in the studies. A large number of bootstrap replicates of the original incidence data were produced by random sampling with replacement. The analysis requires only the dose, the number of patients, and the number of occurrences of the studied endpoint, for each study. Two dose–response models, a Poisson-based model and the Lyman model, were fitted to each bootstrap replicate using maximum likelihood. Results: The bootstrap analysis generates a family of curves representing the range of plausible dose–response relations, and the 95% bootstrap confidence intervals give an estimated upper and lower toxicity risk. The curve families for the 2 dose–response models overlap for doses included in the studies at hand but diverge beyond that, with the Lyman model suggesting a steeper slope. The resulting distributions of the model parameters indicate correlation and non-Gaussian distribution. For both data sets, the likelihood of the observed data was higher for the Lyman model in >90% of the bootstrap replicates. Conclusions: The bootstrap method provides a statistical analysis of the uncertainty in the estimated dose–response relation for myelopathy and pneumonitis. It suggests likely values of model parameter values, their confidence intervals, and how they interrelate for each model. Finally, it can be used to evaluate to what extent data supports one model over another. For both data sets considered here, the Lyman model was preferred over the Poisson-based model.

Uncertainty analysis in the task of individual monitoring data

International Nuclear Information System (INIS)

Molokanov, A.; Badjin, V.; Gasteva, G.; Antipin, E.

2003-01-01

Assessment of internal doses is an essential component of individual monitoring programmes for workers and consists of two stages: individual monitoring measurements and interpretation of the monitoring data in terms of annual intake and/or annual internal dose. The overall uncertainty in assessed dose is a combination of the uncertainties in these stages. An algorithm and a computer code were developed for estimating the uncertainties in these stages. An algorithm and a computer code were developed for estimating the uncertainty in the assessment of internal dose in the task of individual monitoring data interpretation. Two main influencing factors are analysed in this paper: the unknown time of the exposure and variability of bioassay measurements. The aim of this analysis is to show that the algorithm is applicable in designing an individual monitoring programme for workers so as to guarantee that the individual dose calculated from individual monitoring measurements does not exceed a required limit with a certain confidence probability. (author)

New trends in sample preparation techniques for environmental analysis.

Science.gov (United States)

Ribeiro, Cláudia; Ribeiro, Ana Rita; Maia, Alexandra S; Gonçalves, Virgínia M F; Tiritan, Maria Elizabeth

2014-01-01

Environmental samples include a wide variety of complex matrices, with low concentrations of analytes and presence of several interferences. Sample preparation is a critical step and the main source of uncertainties in the analysis of environmental samples, and it is usually laborious, high cost, time consuming, and polluting. In this context, there is increasing interest in developing faster, cost-effective, and environmentally friendly sample preparation techniques. Recently, new methods have been developed and optimized in order to miniaturize extraction steps, to reduce solvent consumption or become solventless, and to automate systems. This review attempts to present an overview of the fundamentals, procedure, and application of the most recently developed sample preparation techniques for the extraction, cleanup, and concentration of organic pollutants from environmental samples. These techniques include: solid phase microextraction, on-line solid phase extraction, microextraction by packed sorbent, dispersive liquid-liquid microextraction, and QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe).

Reduction and Uncertainty Analysis of Chemical Mechanisms Based on Local and Global Sensitivities

Science.gov (United States)

Esposito, Gaetano

identifying sources of uncertainty affecting relevant reaction pathways are usually addressed by resorting to Global Sensitivity Analysis (GSA) techniques. In particular, the most sensitive reactions controlling combustion phenomena are first identified using the Morris Method and then analyzed under the Random Sampling -- High Dimensional Model Representation (RS-HDMR) framework. The HDMR decomposition shows that 10% of the variance seen in the extinction strain rate of non-premixed flames is due to second-order effects between parameters, whereas the maximum concentration of acetylene, a key soot precursor, is affected by mostly only first-order contributions. Moreover, the analysis of the global sensitivity indices demonstrates that improving the accuracy of the reaction rates including the vinyl radical, C2H3, can drastically reduce the uncertainty of predicting targeted flame properties. Finally, the back-propagation of the experimental uncertainty of the extinction strain rate to the parameter space is also performed. This exercise, achieved by recycling the numerical solutions of the RS-HDMR, shows that some regions of the parameter space have a high probability of reproducing the experimental value of the extinction strain rate between its own uncertainty bounds. Therefore this study demonstrates that the uncertainty analysis of bulk flame properties can effectively provide information on relevant chemical reactions.

An introductory guide to uncertainty analysis in environmental and health risk assessment. Environmental Restoration Program

International Nuclear Information System (INIS)

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

1994-12-01

This report presents guidelines for evaluating uncertainty in mathematical equations and computer models applied to assess human health and environmental risk. Uncertainty analyses involve the propagation of uncertainty in model parameters and model structure to obtain confidence statements for the estimate of risk and identify the model components of dominant importance. Uncertainty analyses are required when there is no a priori knowledge about uncertainty in the risk estimate and when there is a chance that the failure to assess uncertainty may affect the selection of wrong options for risk reduction. Uncertainty analyses are effective when they are conducted in an iterative mode. When the uncertainty in the risk estimate is intolerable for decision-making, additional data are acquired for the dominant model components that contribute most to uncertainty. This process is repeated until the level of residual uncertainty can be tolerated. A analytical and numerical methods for error propagation are presented along with methods for identifying the most important contributors to uncertainty. Monte Carlo simulation with either Simple Random Sampling (SRS) or Latin Hypercube Sampling (LHS) is proposed as the most robust method for propagating uncertainty through either simple or complex models. A distinction is made between simulating a stochastically varying assessment endpoint (i.e., the distribution of individual risks in an exposed population) and quantifying uncertainty due to lack of knowledge about a fixed but unknown quantity (e.g., a specific individual, the maximally exposed individual, or the mean, median, or 95%-tile of the distribution of exposed individuals). Emphasis is placed on the need for subjective judgement to quantify uncertainty when relevant data are absent or incomplete

Impact of sampling frequency in the analysis of tropospheric ozone observations

Directory of Open Access Journals (Sweden)

M. Saunois

2012-08-01

Full Text Available Measurements of ozone vertical profiles are valuable for the evaluation of atmospheric chemistry models and contribute to the understanding of the processes controlling the distribution of tropospheric ozone. The longest record of ozone vertical profiles is provided by ozone sondes, which have a typical frequency of 4 to 12 profiles a month. Here we quantify the uncertainty introduced by low frequency sampling in the determination of means and trends. To do this, the high frequency MOZAIC (Measurements of OZone, water vapor, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft profiles over airports, such as Frankfurt, have been subsampled at two typical ozone sonde frequencies of 4 and 12 profiles per month. We found the lowest sampling uncertainty on seasonal means at 700 hPa over Frankfurt, with around 5% for a frequency of 12 profiles per month and 10% for a 4 profile-a-month frequency. However the uncertainty can reach up to 15 and 29% at the lowest altitude levels. As a consequence, the sampling uncertainty at the lowest frequency could be higher than the typical 10% accuracy of the ozone sondes and should be carefully considered for observation comparison and model evaluation. We found that the 95% confidence limit on the seasonal mean derived from the subsample created is similar to the sampling uncertainty and suggest to use it as an estimate of the sampling uncertainty. Similar results are found at six other Northern Hemisphere sites. We show that the sampling substantially impacts on the inter-annual variability and the trend derived over the period 1998–2008 both in magnitude and in sign throughout the troposphere. Also, a tropical case is discussed using the MOZAIC profiles taken over Windhoek, Namibia between 2005 and 2008. For this site, we found that the sampling uncertainty in the free troposphere is around 8 and 12% at 12 and 4 profiles a month respectively.

Information Theory for Correlation Analysis and Estimation of Uncertainty Reduction in Maps and Models

Directory of Open Access Journals (Sweden)

J. Florian Wellmann

2013-04-01

Full Text Available The quantification and analysis of uncertainties is important in all cases where maps and models of uncertain properties are the basis for further decisions. Once these uncertainties are identified, the logical next step is to determine how they can be reduced. Information theory provides a framework for the analysis of spatial uncertainties when different subregions are considered as random variables. In the work presented here, joint entropy, conditional entropy, and mutual information are applied for a detailed analysis of spatial uncertainty correlations. The aim is to determine (i which areas in a spatial analysis share information, and (ii where, and by how much, additional information would reduce uncertainties. As an illustration, a typical geological example is evaluated: the case of a subsurface layer with uncertain depth, shape and thickness. Mutual information and multivariate conditional entropies are determined based on multiple simulated model realisations. Even for this simple case, the measures not only provide a clear picture of uncertainties and their correlations but also give detailed insights into the potential reduction of uncertainties at each position, given additional information at a different location. The methods are directly applicable to other types of spatial uncertainty evaluations, especially where multiple realisations of a model simulation are analysed. In summary, the application of information theoretic measures opens up the path to a better understanding of spatial uncertainties, and their relationship to information and prior knowledge, for cases where uncertain property distributions are spatially analysed and visualised in maps and models.

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)

Characterization of subjective uncertainty in the 1996 performance assessment for the Waste Isolation Pilot Plant

International Nuclear Information System (INIS)

HELTON, JON CRAIG; MARTELL, MARY-ALENA; TIERNEY, MARTIN S.

2000-01-01

The 1996 performance assessment (PA) for the Waste Isolation Pilot Plant (WIPP) maintains a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the possible disruptions that could occur at the WIPP over the 10,000 yr regulatory period specified by the US Environmental Protection Agency (40 CFR 191,40 CFR 194) and subjective uncertainty arising from an inability to uniquely characterize many of the inputs required in the 1996 WIPP PA. The characterization of subjective uncertainty is discussed, including assignment of distributions, uncertain variables selected for inclusion in analysis, correlation control, sample size, statistical confidence on mean complementary cumulative distribution functions, generation of Latin hypercube samples, sensitivity analysis techniques, and scenarios involving stochastic and subjective uncertainty

Characterization of subjective uncertainty in the 1996 performance assessment for the Waste Isolation Pilot Plant

Energy Technology Data Exchange (ETDEWEB)

HELTON,JON CRAIG; MARTELL,MARY-ALENA; TIERNEY,MARTIN S.

2000-05-18

The 1996 performance assessment (PA) for the Waste Isolation Pilot Plant (WIPP) maintains a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the possible disruptions that could occur at the WIPP over the 10,000 yr regulatory period specified by the US Environmental Protection Agency (40 CFR 191,40 CFR 194) and subjective uncertainty arising from an inability to uniquely characterize many of the inputs required in the 1996 WIPP PA. The characterization of subjective uncertainty is discussed, including assignment of distributions, uncertain variables selected for inclusion in analysis, correlation control, sample size, statistical confidence on mean complementary cumulative distribution functions, generation of Latin hypercube samples, sensitivity analysis techniques, and scenarios involving stochastic and subjective uncertainty.

Propagating Water Quality Analysis Uncertainty Into Resource Management Decisions Through Probabilistic Modeling

Science.gov (United States)

Gronewold, A. D.; Wolpert, R. L.; Reckhow, K. H.

2007-12-01

Most probable number (MPN) and colony-forming-unit (CFU) are two estimates of fecal coliform bacteria concentration commonly used as measures of water quality in United States shellfish harvesting waters. The MPN is the maximum likelihood estimate (or MLE) of the true fecal coliform concentration based on counts of non-sterile tubes in serial dilution of a sample aliquot, indicating bacterial metabolic activity. The CFU is the MLE of the true fecal coliform concentration based on the number of bacteria colonies emerging on a growth plate after inoculation from a sample aliquot. Each estimating procedure has intrinsic variability and is subject to additional uncertainty arising from minor variations in experimental protocol. Several versions of each procedure (using different sized aliquots or different numbers of tubes, for example) are in common use, each with its own levels of probabilistic and experimental error and uncertainty. It has been observed empirically that the MPN procedure is more variable than the CFU procedure, and that MPN estimates are somewhat higher on average than CFU estimates, on split samples from the same water bodies. We construct a probabilistic model that provides a clear theoretical explanation for the observed variability in, and discrepancy between, MPN and CFU measurements. We then explore how this variability and uncertainty might propagate into shellfish harvesting area management decisions through a two-phased modeling strategy. First, we apply our probabilistic model in a simulation-based analysis of future water quality standard violation frequencies under alternative land use scenarios, such as those evaluated under guidelines of the total maximum daily load (TMDL) program. Second, we apply our model to water quality data from shellfish harvesting areas which at present are closed (either conditionally or permanently) to shellfishing, to determine if alternative laboratory analysis procedures might have led to different

A probabilistic model for deriving soil quality criteria based on secondary poisoning of top predators. I. Model description and uncertainty analysis.

Science.gov (United States)

Traas, T P; Luttik, R; Jongbloed, R H

1996-08-01

In previous studies, the risk of toxicant accumulation in food chains was used to calculate quality criteria for surface water and soil. A simple algorithm was used to calculate maximum permissable concentrations [MPC = no-observed-effect concentration/bioconcentration factor(NOEC/BCF)]. These studies were limited to simple food chains. This study presents a method to calculate MPCs for more complex food webs of predators. The previous method is expanded. First, toxicity data (NOECs) for several compounds were corrected for differences between laboratory animals and animals in the wild. Second, for each compound, it was assumed these NOECs were a sample of a log-logistic distribution of mammalian and avian NOECs. Third, bioaccumulation factors (BAFs) for major food items of predators were collected and were assumed to derive from different log-logistic distributions of BAFs. Fourth, MPCs for each compound were calculated using Monte Carlo sampling from NOEC and BAF distributions. An uncertainty analysis for cadmium was performed to identify the most uncertain parameters of the model. Model analysis indicated that most of the prediction uncertainty of the model can be ascribed to uncertainty of species sensitivity as expressed by NOECs. A very small proportion of model uncertainty is contributed by BAFs from food webs. Correction factors for the conversion of NOECs from laboratory conditions to the field have some influence on the final value of MPC5, but the total prediction uncertainty of the MPC is quite large. It is concluded that the uncertainty in species sensitivity is quite large. To avoid unethical toxicity testing with mammalian or avian predators, it cannot be avoided to use this uncertainty in the method proposed to calculate MPC distributions. The fifth percentile of the MPC is suggested as a safe value for top predators.

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

Uncertainties in Cancer Risk Coefficients for Environmental Exposure to Radionuclides. An Uncertainty Analysis for Risk Coefficients Reported in Federal Guidance Report No. 13

Energy Technology Data Exchange (ETDEWEB)

Pawel, David [U.S. Environmental Protection Agency; Leggett, Richard Wayne [ORNL; Eckerman, Keith F [ORNL; Nelson, Christopher [U.S. Environmental Protection Agency

2007-01-01

Federal Guidance Report No. 13 (FGR 13) provides risk coefficients for estimation of the risk of cancer due to low-level exposure to each of more than 800 radionuclides. Uncertainties in risk coefficients were quantified in FGR 13 for 33 cases (exposure to each of 11 radionuclides by each of three exposure pathways) on the basis of sensitivity analyses in which various combinations of plausible biokinetic, dosimetric, and radiation risk models were used to generate alternative risk coefficients. The present report updates the uncertainty analysis in FGR 13 for the cases of inhalation and ingestion of radionuclides and expands the analysis to all radionuclides addressed in that report. The analysis indicates that most risk coefficients for inhalation or ingestion of radionuclides are determined within a factor of 5 or less by current information. That is, application of alternate plausible biokinetic and dosimetric models and radiation risk models (based on the linear, no-threshold hypothesis with an adjustment for the dose and dose rate effectiveness factor) is unlikely to change these coefficients by more than a factor of 5. In this analysis the assessed uncertainty in the radiation risk model was found to be the main determinant of the uncertainty category for most risk coefficients, but conclusions concerning the relative contributions of risk and dose models to the total uncertainty in a risk coefficient may depend strongly on the method of assessing uncertainties in the risk model.

Calculating Confidence, Uncertainty, and Numbers of Samples When Using Statistical Sampling Approaches to Characterize and Clear Contaminated Areas

Energy Technology Data Exchange (ETDEWEB)

Piepel, Gregory F.; Matzke, Brett D.; Sego, Landon H.; Amidan, Brett G.

2013-04-27

for FNR > 0 2. qualitative data when the FNR > 0 but statistical sampling methods are used that assume the FNR = 0 3. quantitative data (e.g., contaminant concentrations expressed as CFU/cm2) when the FNR = 0 or when using statistical sampling methods that account for FNR > 0 4. quantitative data when the FNR > 0 but statistical sampling methods are used that assume the FNR = 0. For Situation 2, the hotspot sampling approach provides for stating with Z% confidence that a hotspot of specified shape and size with detectable contamination will be found. Also for Situation 2, the CJR approach provides for stating with X% confidence that at least Y% of the decision area does not contain detectable contamination. Forms of these statements for the other three situations are discussed in Section 2.2. Statistical methods that account for FNR > 0 currently only exist for the hotspot sampling approach with qualitative data (or quantitative data converted to qualitative data). This report documents the current status of methods and formulas for the hotspot and CJR sampling approaches. Limitations of these methods are identified. Extensions of the methods that are applicable when FNR = 0 to account for FNR > 0, or to address other limitations, will be documented in future revisions of this report if future funding supports the development of such extensions. For quantitative data, this report also presents statistical methods and formulas for 1. quantifying the uncertainty in measured sample results 2. estimating the true surface concentration corresponding to a surface sample 3. quantifying the uncertainty of the estimate of the true surface concentration. All of the methods and formulas discussed in the report were applied to example situations to illustrate application of the methods and interpretation of the results.

Code development of total sensitivity and uncertainty analysis for reactor physics calculations

International Nuclear Information System (INIS)

Wan, C.; Cao, L.; Wu, H.; Zu, T.; Shen, W.

2015-01-01

Sensitivity and uncertainty analysis are essential parts for reactor system to perform risk and policy analysis. In this study, total sensitivity and corresponding uncertainty analysis for responses of neutronics calculations have been accomplished and developed the S&U analysis code named UNICORN. The UNICORN code can consider the implicit effects of multigroup cross sections on the responses. The UNICORN code has been applied to typical pin-cell case in this paper, and can be proved correct by comparison the results with those of the TSUNAMI-1D code. (author)

Code development of total sensitivity and uncertainty analysis for reactor physics calculations

Energy Technology Data Exchange (ETDEWEB)

Wan, C.; Cao, L.; Wu, H.; Zu, T., E-mail: chenghuiwan@stu.xjtu.edu.cn, E-mail: caolz@mail.xjtu.edu.cn, E-mail: hongchun@mail.xjtu.edu.cn, E-mail: tiejun@mail.xjtu.edu.cn [Xi' an Jiaotong Univ., School of Nuclear Science and Technology, Xi' an (China); Shen, W., E-mail: Wei.Shen@cnsc-ccsn.gc.ca [Xi' an Jiaotong Univ., School of Nuclear Science and Technology, Xi' an (China); Canadian Nuclear Safety Commission, Ottawa, ON (Canada)

2015-07-01

Sensitivity and uncertainty analysis are essential parts for reactor system to perform risk and policy analysis. In this study, total sensitivity and corresponding uncertainty analysis for responses of neutronics calculations have been accomplished and developed the S&U analysis code named UNICORN. The UNICORN code can consider the implicit effects of multigroup cross sections on the responses. The UNICORN code has been applied to typical pin-cell case in this paper, and can be proved correct by comparison the results with those of the TSUNAMI-1D code. (author)

Evaluation of Uncertainties in the Determination of Phosphorus by RNAA

International Nuclear Information System (INIS)

Rick L. Paul

2000-01-01