WorldWideScience

Sample records for model uncertainties caused

  1. An Adaptation Dilemma Caused by Impacts-Modeling Uncertainty

    Science.gov (United States)

    Frieler, K.; Müller, C.; Elliott, J. W.; Heinke, J.; Arneth, A.; Bierkens, M. F.; Ciais, P.; Clark, D. H.; Deryng, D.; Doll, P. M.; Falloon, P.; Fekete, B. M.; Folberth, C.; Friend, A. D.; Gosling, S. N.; Haddeland, I.; Khabarov, N.; Lomas, M. R.; Masaki, Y.; Nishina, K.; Neumann, K.; Oki, T.; Pavlick, R.; Ruane, A. C.; Schmid, E.; Schmitz, C.; Stacke, T.; Stehfest, E.; Tang, Q.; Wisser, D.

    2013-12-01

    Ensuring future well-being for a growing population under either strong climate change or an aggressive mitigation strategy requires a subtle balance of potentially conflicting response measures. In the case of competing goals, uncertainty in impact estimates plays a central role when high confidence in achieving a primary objective (such as food security) directly implies an increased probability of uncertainty induced failure with regard to a competing target (such as climate protection). We use cross sectoral consistent multi-impact model simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP, www.isi-mip.org) to illustrate this uncertainty dilemma: RCP projections from 7 global crop, 11 hydrological, and 7 biomes models are combined to analyze irrigation and land use changes as possible responses to climate change and increasing crop demand due to population growth and economic development. We show that - while a no-regrets option with regard to climate protection - additional irrigation alone is not expected to balance the demand increase by 2050. In contrast, a strong expansion of cultivated land closes the projected production-demand gap in some crop models. However, it comes at the expense of a loss of natural carbon sinks of order 50%. Given the large uncertainty of state of the art crop model projections even these strong land use changes would not bring us ';on the safe side' with respect to food supply. In a world where increasing carbon emissions continue to shrink the overall solution space, we demonstrate that current impacts-modeling uncertainty is a luxury we cannot afford. ISI-MIP is intended to provide cross sectoral consistent impact projections for model intercomparison and improvement as well as cross-sectoral integration. The results presented here were generated within the first Fast-Track phase of the project covering global impact projections. The second phase will also include regional projections. It is the aim

  2. A robust Bayesian approach to modeling epistemic uncertainty in common-cause failure models

    International Nuclear Information System (INIS)

    Troffaes, Matthias C.M.; Walter, Gero; Kelly, Dana

    2014-01-01

    In a standard Bayesian approach to the alpha-factor model for common-cause failure, a precise Dirichlet prior distribution models epistemic uncertainty in the alpha-factors. This Dirichlet prior is then updated with observed data to obtain a posterior distribution, which forms the basis for further inferences. In this paper, we adapt the imprecise Dirichlet model of Walley to represent epistemic uncertainty in the alpha-factors. In this approach, epistemic uncertainty is expressed more cautiously via lower and upper expectations for each alpha-factor, along with a learning parameter which determines how quickly the model learns from observed data. For this application, we focus on elicitation of the learning parameter, and find that values in the range of 1 to 10 seem reasonable. The approach is compared with Kelly and Atwood's minimally informative Dirichlet prior for the alpha-factor model, which incorporated precise mean values for the alpha-factors, but which was otherwise quite diffuse. Next, we explore the use of a set of Gamma priors to model epistemic uncertainty in the marginal failure rate, expressed via a lower and upper expectation for this rate, again along with a learning parameter. As zero counts are generally less of an issue here, we find that the choice of this learning parameter is less crucial. Finally, we demonstrate how both epistemic uncertainty models can be combined to arrive at lower and upper expectations for all common-cause failure rates. Thereby, we effectively provide a full sensitivity analysis of common-cause failure rates, properly reflecting epistemic uncertainty of the analyst on all levels of the common-cause failure model

  3. The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

    Directory of Open Access Journals (Sweden)

    L. A. Lee

    2013-09-01

    Full Text Available Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN. Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.

  4. Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

    Science.gov (United States)

    Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

    2018-01-01

    Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection

  5. 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

  6. Model uncertainty and probability

    International Nuclear Information System (INIS)

    Parry, G.W.

    1994-01-01

    This paper discusses the issue of model uncertainty. The use of probability as a measure of an analyst's uncertainty as well as a means of describing random processes has caused some confusion, even though the two uses are representing different types of uncertainty with respect to modeling a system. The importance of maintaining the distinction between the two types is illustrated with a simple example

  7. Uncertainties in Nuclear Proliferation Modeling

    International Nuclear Information System (INIS)

    Kim, Chul Min; Yim, Man-Sung; Park, Hyeon Seok

    2015-01-01

    There have been various efforts in the research community to understand the determinants of nuclear proliferation and develop quantitative tools to predict nuclear proliferation events. Such systematic approaches have shown the possibility to provide warning for the international community to prevent nuclear proliferation activities. However, there are still large debates for the robustness of the actual effect of determinants and projection results. Some studies have shown that several factors can cause uncertainties in previous quantitative nuclear proliferation modeling works. This paper analyzes the uncertainties in the past approaches and suggests future works in the view of proliferation history, analysis methods, and variable selection. The research community still lacks the knowledge for the source of uncertainty in current models. Fundamental problems in modeling will remain even other advanced modeling method is developed. Before starting to develop fancy model based on the time dependent proliferation determinants' hypothesis, using graph theory, etc., it is important to analyze the uncertainty of current model to solve the fundamental problems of nuclear proliferation modeling. The uncertainty from different proliferation history coding is small. Serious problems are from limited analysis methods and correlation among the variables. Problems in regression analysis and survival analysis cause huge uncertainties when using the same dataset, which decreases the robustness of the result. Inaccurate variables for nuclear proliferation also increase the uncertainty. To overcome these problems, further quantitative research should focus on analyzing the knowledge suggested on the qualitative nuclear proliferation studies

  8. Essays on model uncertainty in financial models

    NARCIS (Netherlands)

    Li, Jing

    2018-01-01

    This dissertation studies model uncertainty, particularly in financial models. It consists of two empirical chapters and one theoretical chapter. The first empirical chapter (Chapter 2) classifies model uncertainty into parameter uncertainty and misspecification uncertainty. It investigates the

  9. Model uncertainty: Probabilities for models?

    International Nuclear Information System (INIS)

    Winkler, R.L.

    1994-01-01

    Like any other type of uncertainty, model uncertainty should be treated in terms of probabilities. The question is how to do this. The most commonly-used approach has a drawback related to the interpretation of the probabilities assigned to the models. If we step back and look at the big picture, asking what the appropriate focus of the model uncertainty question should be in the context of risk and decision analysis, we see that a different probabilistic approach makes more sense, although it raise some implementation questions. Current work that is underway to address these questions looks very promising

  10. 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...

  11. Uncertainties in repository modeling

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, J.R.

    1996-12-31

    The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling.

  12. Uncertainties in repository modeling

    International Nuclear Information System (INIS)

    Wilson, J.R.

    1996-01-01

    The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling

  13. Flood modelling : Parameterisation and inflow uncertainty

    NARCIS (Netherlands)

    Mukolwe, M.M.; Di Baldassarre, G.; Werner, M.; Solomatine, D.P.

    2014-01-01

    This paper presents an analysis of uncertainty in hydraulic modelling of floods, focusing on the inaccuracy caused by inflow errors and parameter uncertainty. In particular, the study develops a method to propagate the uncertainty induced by, firstly, application of a stage–discharge rating curve

  14. Reusable launch vehicle model uncertainties impact analysis

    Science.gov (United States)

    Chen, Jiaye; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

    2018-03-01

    Reusable launch vehicle(RLV) has the typical characteristics of complex aerodynamic shape and propulsion system coupling, and the flight environment is highly complicated and intensely changeable. So its model has large uncertainty, which makes the nominal system quite different from the real system. Therefore, studying the influences caused by the uncertainties on the stability of the control system is of great significance for the controller design. In order to improve the performance of RLV, this paper proposes the approach of analyzing the influence of the model uncertainties. According to the typical RLV, the coupling dynamic and kinematics models are built. Then different factors that cause uncertainties during building the model are analyzed and summed up. After that, the model uncertainties are expressed according to the additive uncertainty model. Choosing the uncertainties matrix's maximum singular values as the boundary model, and selecting the uncertainties matrix's norm to show t how much the uncertainty factors influence is on the stability of the control system . The simulation results illustrate that the inertial factors have the largest influence on the stability of the system, and it is necessary and important to take the model uncertainties into consideration before the designing the controller of this kind of aircraft( like RLV, etc).

  15. A commentary on model uncertainty

    International Nuclear Information System (INIS)

    Apostolakis, G.

    1994-01-01

    A framework is proposed for the identification of model and parameter uncertainties in risk assessment models. Two cases are distinguished; in the first case, a set of mutually exclusive and exhaustive hypotheses (models) can be formulated, while, in the second, only one reference model is available. The relevance of this formulation to decision making and the communication of uncertainties is discussed

  16. Chemical model reduction under uncertainty

    KAUST Repository

    Najm, Habib; Galassi, R. Malpica; Valorani, M.

    2016-01-01

    We outline a strategy for chemical kinetic model reduction under uncertainty. We present highlights of our existing deterministic model reduction strategy, and describe the extension of the formulation to include parametric uncertainty in the detailed mechanism. We discuss the utility of this construction, as applied to hydrocarbon fuel-air kinetics, and the associated use of uncertainty-aware measures of error between predictions from detailed and simplified models.

  17. Chemical model reduction under uncertainty

    KAUST Repository

    Najm, Habib

    2016-01-05

    We outline a strategy for chemical kinetic model reduction under uncertainty. We present highlights of our existing deterministic model reduction strategy, and describe the extension of the formulation to include parametric uncertainty in the detailed mechanism. We discuss the utility of this construction, as applied to hydrocarbon fuel-air kinetics, and the associated use of uncertainty-aware measures of error between predictions from detailed and simplified models.

  18. 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

  19. Model uncertainty in safety assessment

    International Nuclear Information System (INIS)

    Pulkkinen, U.; Huovinen, T.

    1996-01-01

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

  20. Model uncertainty in safety assessment

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-01-01

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

  1. Some remarks on modeling uncertainties

    International Nuclear Information System (INIS)

    Ronen, Y.

    1983-01-01

    Several topics related to the question of modeling uncertainties are considered. The first topic is related to the use of the generalized bias operator method for modeling uncertainties. The method is expanded to a more general form of operators. The generalized bias operator is also used in the inverse problem and applied to determine the anisotropic scattering law. The last topic discussed is related to the question of the limit to accuracy and how to establish its value. (orig.) [de

  2. Bayesian uncertainty analyses of probabilistic risk models

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1989-01-01

    Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

  3. Representing and managing uncertainty in qualitative ecological models

    NARCIS (Netherlands)

    Nuttle, T.; Bredeweg, B.; Salles, P.; Neumann, M.

    2009-01-01

    Ecologists and decision makers need ways to understand systems, test ideas, and make predictions and explanations about systems. However, uncertainty about causes and effects of processes and parameter values is pervasive in models of ecological systems. Uncertainty associated with incomplete

  4. Uncertainties in radioecological assessment models

    International Nuclear Information System (INIS)

    Hoffman, F.O.; Miller, C.W.; Ng, Y.C.

    1983-01-01

    Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because models are inexact representations of real systems. The major sources of this uncertainty are related to bias in model formulation and imprecision in parameter estimation. The magnitude of uncertainty is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, health risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible. 41 references, 4 figures, 4 tables

  5. A Bayesian approach to model uncertainty

    International Nuclear Information System (INIS)

    Buslik, A.

    1994-01-01

    A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given

  6. Numerical modeling of economic uncertainty

    DEFF Research Database (Denmark)

    Schjær-Jacobsen, Hans

    2007-01-01

    Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Focusing on discounted cash flow analysis numerical results are presented, comparisons...... are made between alternative modeling methods, and characteristics of the methods are discussed....

  7. Uncertainty Assessment in Urban Storm Water Drainage Modelling

    DEFF Research Database (Denmark)

    Thorndahl, Søren

    The object of this paper is to make an overall description of the author's PhD study, concerning uncertainties in numerical urban storm water drainage models. Initially an uncertainty localization and assessment of model inputs and parameters as well as uncertainties caused by different model...

  8. Model uncertainty in growth empirics

    NARCIS (Netherlands)

    Prüfer, P.

    2008-01-01

    This thesis applies so-called Bayesian model averaging (BMA) to three different economic questions substantially exposed to model uncertainty. Chapter 2 addresses a major issue of modern development economics: the analysis of the determinants of pro-poor growth (PPG), which seeks to combine high

  9. Uncertainty modeling and decision support

    International Nuclear Information System (INIS)

    Yager, Ronald R.

    2004-01-01

    We first formulate the problem of decision making under uncertainty. The importance of the representation of our knowledge about the uncertainty in formulating a decision process is pointed out. We begin with a brief discussion of the case of probabilistic uncertainty. Next, in considerable detail, we discuss the case of decision making under ignorance. For this case the fundamental role of the attitude of the decision maker is noted and its subjective nature is emphasized. Next the case in which a Dempster-Shafer belief structure is used to model our knowledge of the uncertainty is considered. Here we also emphasize the subjective choices the decision maker must make in formulating a decision function. The case in which the uncertainty is represented by a fuzzy measure (monotonic set function) is then investigated. We then return to the Dempster-Shafer belief structure and show its relationship to the fuzzy measure. This relationship allows us to get a deeper understanding of the formulation the decision function used Dempster- Shafer framework. We discuss how this deeper understanding allows a decision analyst to better make the subjective choices needed in the formulation of the decision function

  10. Uncertainty in hydrological change modelling

    DEFF Research Database (Denmark)

    Seaby, Lauren Paige

    applied at the grid scale. Flux and state hydrological outputs which integrate responses over time and space showed more sensitivity to precipitation mean spatial biases and less so on extremes. In the investigated catchments, the projected change of groundwater levels and basin discharge between current......Hydrological change modelling methodologies generally use climate models outputs to force hydrological simulations under changed conditions. There are nested sources of uncertainty throughout this methodology, including choice of climate model and subsequent bias correction methods. This Ph.......D. study evaluates the uncertainty of the impact of climate change in hydrological simulations given multiple climate models and bias correction methods of varying complexity. Three distribution based scaling methods (DBS) were developed and benchmarked against a more simplistic and commonly used delta...

  11. Uncertainty quantification for environmental models

    Science.gov (United States)

    Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

    2012-01-01

    Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10

  12. Chemical model reduction under uncertainty

    KAUST Repository

    Malpica Galassi, Riccardo

    2017-03-06

    A general strategy for analysis and reduction of uncertain chemical kinetic models is presented, and its utility is illustrated in the context of ignition of hydrocarbon fuel–air mixtures. The strategy is based on a deterministic analysis and reduction method which employs computational singular perturbation analysis to generate simplified kinetic mechanisms, starting from a detailed reference mechanism. We model uncertain quantities in the reference mechanism, namely the Arrhenius rate parameters, as random variables with prescribed uncertainty factors. We propagate this uncertainty to obtain the probability of inclusion of each reaction in the simplified mechanism. We propose probabilistic error measures to compare predictions from the uncertain reference and simplified models, based on the comparison of the uncertain dynamics of the state variables, where the mixture entropy is chosen as progress variable. We employ the construction for the simplification of an uncertain mechanism in an n-butane–air mixture homogeneous ignition case, where a 176-species, 1111-reactions detailed kinetic model for the oxidation of n-butane is used with uncertainty factors assigned to each Arrhenius rate pre-exponential coefficient. This illustration is employed to highlight the utility of the construction, and the performance of a family of simplified models produced depending on chosen thresholds on importance and marginal probabilities of the reactions.

  13. Uncertainty propagation in urban hydrology water quality modelling

    NARCIS (Netherlands)

    Torres Matallana, Arturo; Leopold, U.; Heuvelink, G.B.M.

    2016-01-01

    Uncertainty is often ignored in urban hydrology modelling. Engineering practice typically ignores uncertainties and uncertainty propagation. This can have large impacts, such as the wrong dimensioning of urban drainage systems and the inaccurate estimation of pollution in the environment caused

  14. Uncertainty, God, and scrupulosity: Uncertainty salience and priming God concepts interact to cause greater fears of sin.

    Science.gov (United States)

    Fergus, Thomas A; Rowatt, Wade C

    2015-03-01

    Difficulties tolerating uncertainty are considered central to scrupulosity, a moral/religious presentation of obsessive-compulsive disorder (OCD). We examined whether uncertainty salience (i.e., exposure to a state of uncertainty) caused fears of sin and fears of God, as well as whether priming God concepts affected the impact of uncertainty salience on those fears. An internet sample of community adults (N = 120) who endorsed holding a belief in God or a higher power were randomly assigned to an experimental manipulation of (1) salience (uncertainty or insecurity) and (2) prime (God concepts or neutral). As predicted, participants who received the uncertainty salience and God concept priming reported the greatest fears of sin. There were no mean-level differences in the other conditions. The effect was not attributable to religiosity and the manipulations did not cause negative affect. We used a nonclinical sample recruited from the internet. These results support cognitive-behavioral models suggesting that religious uncertainty is important to scrupulosity. Implications of these results for future research are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. On the relationship between aerosol model uncertainty and radiative forcing uncertainty.

    Science.gov (United States)

    Lee, Lindsay A; Reddington, Carly L; Carslaw, Kenneth S

    2016-05-24

    The largest uncertainty in the historical radiative forcing of climate is caused by the interaction of aerosols with clouds. Historical forcing is not a directly measurable quantity, so reliable assessments depend on the development of global models of aerosols and clouds that are well constrained by observations. However, there has been no systematic assessment of how reduction in the uncertainty of global aerosol models will feed through to the uncertainty in the predicted forcing. We use a global model perturbed parameter ensemble to show that tight observational constraint of aerosol concentrations in the model has a relatively small effect on the aerosol-related uncertainty in the calculated forcing between preindustrial and present-day periods. One factor is the low sensitivity of present-day aerosol to natural emissions that determine the preindustrial aerosol state. However, the major cause of the weak constraint is that the full uncertainty space of the model generates a large number of model variants that are equally acceptable compared to present-day aerosol observations. The narrow range of aerosol concentrations in the observationally constrained model gives the impression of low aerosol model uncertainty. However, these multiple "equifinal" models predict a wide range of forcings. To make progress, we need to develop a much deeper understanding of model uncertainty and ways to use observations to constrain it. Equifinality in the aerosol model means that tuning of a small number of model processes to achieve model-observation agreement could give a misleading impression of model robustness.

  16. Some illustrative examples of model uncertainty

    International Nuclear Information System (INIS)

    Bier, V.M.

    1994-01-01

    In this paper, we first discuss the view of model uncertainty proposed by Apostolakis. We then present several illustrative examples related to model uncertainty, some of which are not well handled by this formalism. Thus, Apostolakis' approach seems to be well suited to describing some types of model uncertainty, but not all. Since a comprehensive approach for characterizing and quantifying model uncertainty is not yet available, it is hoped that the examples presented here will service as a springboard for further discussion

  17. Modelling of Transport Projects Uncertainties

    DEFF Research Database (Denmark)

    Salling, Kim Bang; Leleur, Steen

    2009-01-01

    This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating...... to supplement Optimism Bias and the associated Reference Class Forecasting (RCF) technique with a new technique that makes use of a scenario-grid. We tentatively introduce and refer to this as Reference Scenario Forecasting (RSF). The final RSF output from the CBA-DK model consists of a set of scenario......-based graphs which function as risk-related decision support for the appraised transport infrastructure project....

  18. Uncertainty and its propagation in dynamics models

    International Nuclear Information System (INIS)

    Devooght, J.

    1994-01-01

    The purpose of this paper is to bring together some characteristics due to uncertainty when we deal with dynamic models and therefore to propagation of uncertainty. The respective role of uncertainty and inaccuracy is examined. A mathematical formalism based on Chapman-Kolmogorov equation allows to define a open-quotes subdynamicsclose quotes where the evolution equation takes the uncertainty into account. The problem of choosing or combining models is examined through a loss function associated to a decision

  19. Modelling of Transport Projects Uncertainties

    DEFF Research Database (Denmark)

    Salling, Kim Bang; Leleur, Steen

    2012-01-01

    This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating...... to supplement Optimism Bias and the associated Reference Class Forecasting (RCF) technique with a new technique that makes use of a scenario-grid. We tentatively introduce and refer to this as Reference Scenario Forecasting (RSF). The final RSF output from the CBA-DK model consists of a set of scenario......-based graphs which functions as risk-related decision support for the appraised transport infrastructure project. The presentation of RSF is demonstrated by using an appraisal case concerning a new airfield in the capital of Greenland, Nuuk....

  20. Uncertainty and validation. Effect of model complexity on uncertainty estimates

    International Nuclear Information System (INIS)

    Elert, M.

    1996-09-01

    In the Model Complexity subgroup of BIOMOVS II, models of varying complexity have been applied to the problem of downward transport of radionuclides in soils. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: Cs-137, Sr-90 and I-129. The intention was to give a detailed specification of the parameters required by different kinds of model, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models used range in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. The complex models needed to consider all aspects of radionuclide transport in a soil with a variable hydrology are often impractical to use in safety assessments. Instead simpler models, often box models, are preferred. The comparison of predictions made with the complex models and the simple models for this scenario show that the predictions in many cases are very similar, e g in the predictions of the evolution of the root zone concentration. However, in other cases differences of many orders of magnitude can appear. One example is the prediction of the flux to the groundwater of radionuclides being transported through the soil column. Some issues that have come to focus in this study: There are large differences in the predicted soil hydrology and as a consequence also in the radionuclide transport, which suggests that there are large uncertainties in the calculation of effective precipitation and evapotranspiration. The approach used for modelling the water transport in the root zone has an impact on the predictions of the decline in root

  1. Uncertainty and validation. Effect of model complexity on uncertainty estimates

    Energy Technology Data Exchange (ETDEWEB)

    Elert, M. [Kemakta Konsult AB, Stockholm (Sweden)] [ed.

    1996-09-01

    In the Model Complexity subgroup of BIOMOVS II, models of varying complexity have been applied to the problem of downward transport of radionuclides in soils. A scenario describing a case of surface contamination of a pasture soil was defined. Three different radionuclides with different environmental behavior and radioactive half-lives were considered: Cs-137, Sr-90 and I-129. The intention was to give a detailed specification of the parameters required by different kinds of model, together with reasonable values for the parameter uncertainty. A total of seven modelling teams participated in the study using 13 different models. Four of the modelling groups performed uncertainty calculations using nine different modelling approaches. The models used range in complexity from analytical solutions of a 2-box model using annual average data to numerical models coupling hydrology and transport using data varying on a daily basis. The complex models needed to consider all aspects of radionuclide transport in a soil with a variable hydrology are often impractical to use in safety assessments. Instead simpler models, often box models, are preferred. The comparison of predictions made with the complex models and the simple models for this scenario show that the predictions in many cases are very similar, e g in the predictions of the evolution of the root zone concentration. However, in other cases differences of many orders of magnitude can appear. One example is the prediction of the flux to the groundwater of radionuclides being transported through the soil column. Some issues that have come to focus in this study: There are large differences in the predicted soil hydrology and as a consequence also in the radionuclide transport, which suggests that there are large uncertainties in the calculation of effective precipitation and evapotranspiration. The approach used for modelling the water transport in the root zone has an impact on the predictions of the decline in root

  2. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

  3. Model Uncertainty for Bilinear Hysteretic Systems

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Thoft-Christensen, Palle

    1984-01-01

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

  4. Wastewater treatment modelling: dealing with uncertainties

    DEFF Research Database (Denmark)

    Belia, E.; Amerlinck, Y.; Benedetti, L.

    2009-01-01

    This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation...

  5. 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...

  6. Study on Uncertainty and Contextual Modelling

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2007-01-01

    Roč. 1, č. 1 (2007), s. 12-15 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Knowledge * contextual modelling * temporal modelling * uncertainty * knowledge management Subject RIV: BD - Theory of Information

  7. Model uncertainties in top-quark physics

    CERN Document Server

    Seidel, Markus

    2014-01-01

    The ATLAS and CMS collaborations at the Large Hadron Collider (LHC) are studying the top quark in pp collisions at 7 and 8 TeV. Due to the large integrated luminosity, precision measurements of production cross-sections and properties are often limited by systematic uncertainties. An overview of the modeling uncertainties for simulated events is given in this report.

  8. Assessing Groundwater Model Uncertainty for the Central Nevada Test Area

    International Nuclear Information System (INIS)

    Pohll, Greg; Pohlmann, Karl; Hassan, Ahmed; Chapman, Jenny; Mihevc, Todd

    2002-01-01

    The purpose of this study is to quantify the flow and transport model uncertainty for the Central Nevada Test Area (CNTA). Six parameters were identified as uncertain, including the specified head boundary conditions used in the flow model, the spatial distribution of the underlying welded tuff unit, effective porosity, sorption coefficients, matrix diffusion coefficient, and the geochemical release function which describes nuclear glass dissolution. The parameter uncertainty was described by assigning prior statistical distributions for each of these parameters. Standard Monte Carlo techniques were used to sample from the parameter distributions to determine the full prediction uncertainty. Additional analysis is performed to determine the most cost-beneficial characterization activities. The maximum radius of the tritium and strontium-90 contaminant boundary was used as the output metric for evaluation of prediction uncertainty. The results indicate that combining all of the uncertainty in the parameters listed above propagates to a prediction uncertainty in the maximum radius of the contaminant boundary of 234 to 308 m and 234 to 302 m, for tritium and strontium-90, respectively. Although the uncertainty in the input parameters is large, the prediction uncertainty in the contaminant boundary is relatively small. The relatively small prediction uncertainty is primarily due to the small transport velocities such that large changes in the uncertain input parameters causes small changes in the contaminant boundary. This suggests that the model is suitable in terms of predictive capability for the contaminant boundary delineation

  9. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  10. Stratospheric changes caused by geoengineering applications: potential repercussions and uncertainties

    Science.gov (United States)

    Kenzelmann, P.; Weisenstein, D.; Peter, T.; Luo, B. P.; Rozanov, E.; Fueglistaler, S.; Thomason, L. W.

    2009-04-01

    , larger injections might be required than previously assumed. Rasch et al. (2008) showed that smaller particles would be advantageous in terms of cooling the surface. However, with a continuous injection of sulphur dioxide into to lower tropical stratosphere aerosol size distributions with mode radii larger than 0.5 microns are likely to form. An additional complication is that the sedimenting particles tend to heat the tropical tropopause region and as a consequence the entry mixing ratio of water vapour increases. For the extreme scenario of 10 Mt/year injection SOCOL predicts an enhancement of the water vapour entry mixing ratio by more than 1 ppmv. This is predicted to have a significant impact on the radiative forcing and the total ozone, because of enhanced heterogeneous reactions and because the increased water vapour intensifies the hydrogen and chlorine catalysed ozone destruction cycles. The intense warming of the lower stratosphere further intensifies the catalytic ozone destruction cycles. Furthermore, the stratospheric circulation is predicted to change due to the strong heating of the lower stratosphere. As a consequence of the intensified meridional temperature gradient the polar vortices are strengthened with enhanced formation of polar stratospheric clouds and ozone depletion. The ozone loss due to changed stratospheric dynamic is four times larger than the ozone loss caused by the increase of aerosol surface for heterogeneous reactions, which would postpone the recovery of the ozone hole even more as already pointed out by Tilmes et al. [2008]. At the same time the uncertainties involved in the different modelling steps are tremendous. Model validation, by comparing model runs of the 1991 Mt. Pinatubo eruption with observations, reveals that the temperature increase in the lower stratosphere and the tropopause region is probably overestimated by SOCOL. Other CCMs show similar behaviour. This lets us conclude that with the present modelling tools we are

  11. Quantification of uncertainties of modeling and simulation

    International Nuclear Information System (INIS)

    Ma Zhibo; Yin Jianwei

    2012-01-01

    The principles of Modeling and Simulation (M and S) is interpreted by a functional relation, from which the total uncertainties of M and S are identified and sorted to three parts considered to vary along with the conceptual models' parameters. According to the idea of verification and validation, the space of the parameters is parted to verified and applied domains, uncertainties in the verified domain are quantified by comparison between numerical and standard results, and those in the applied domain are quantified by a newly developed extrapolating method. Examples are presented to demonstrate and qualify the ideas aimed to build a framework to quantify the uncertainties of M and S. (authors)

  12. Application of a Novel Dose-Uncertainty Model for Dose-Uncertainty Analysis in Prostate Intensity-Modulated Radiotherapy

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  13. Comment on ‘Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling’ by J. A. Lyn, M. H. Ramsey, A. P. Damant and R. Wood

    NARCIS (Netherlands)

    Geelhoed, B.

    2009-01-01

    Recently, Lyn et al. (Analyst, 2007, 132, 1231) compared two ways of estimating the standard uncertainty of sampling pistachio nuts for aflatoxins – a modelling method and an empirical method. Their case study used robust analysis of variance (RANOVA) to derive the uncertainty estimates,

  14. Empirical Bayesian inference and model uncertainty

    International Nuclear Information System (INIS)

    Poern, K.

    1994-01-01

    This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability

  15. Analysis of uncertainty in modeling perceived risks

    International Nuclear Information System (INIS)

    Melnyk, R.; Sandquist, G.M.

    2005-01-01

    Expanding on a mathematical model developed for quantifying and assessing perceived risks, the distribution functions, variances, and uncertainties associated with estimating the model parameters are quantified. The analytical model permits the identification and assignment of any number of quantifiable risk perception factors that can be incorporated within standard risk methodology. Those risk perception factors associated with major technical issues are modeled using lognormal probability density functions to span the potentially large uncertainty variations associated with these risk perceptions. The model quantifies the logic of public risk perception and provides an effective means for measuring and responding to perceived risks. (authors)

  16. Paradoxical effects of compulsive perseveration : Sentence repetition causes semantic uncertainty

    NARCIS (Netherlands)

    Giele, Catharina L.; van den Hout, Marcel A.; Engelhard, Iris M.; Dek, Eliane C P

    2014-01-01

    Many patients with obsessive compulsive disorder (OCD) perform perseverative checking behavior to reduce uncertainty, but studies have shown that this ironically increases uncertainty. Some patients also tend to perseveratively repeat sentences. The aim of this study was to examine whether sentence

  17. Assessing uncertainty in mechanistic models

    Science.gov (United States)

    Edwin J. Green; David W. MacFarlane; Harry T. Valentine

    2000-01-01

    Concern over potential global change has led to increased interest in the use of mechanistic models for predicting forest growth. The rationale for this interest is that empirical models may be of limited usefulness if environmental conditions change. Intuitively, we expect that mechanistic models, grounded as far as possible in an understanding of the biology of tree...

  18. Model Uncertainty Quantification Methods In Data Assimilation

    Science.gov (United States)

    Pathiraja, S. D.; Marshall, L. A.; Sharma, A.; Moradkhani, H.

    2017-12-01

    Data Assimilation involves utilising observations to improve model predictions in a seamless and statistically optimal fashion. Its applications are wide-ranging; from improving weather forecasts to tracking targets such as in the Apollo 11 mission. The use of Data Assimilation methods in high dimensional complex geophysical systems is an active area of research, where there exists many opportunities to enhance existing methodologies. One of the central challenges is in model uncertainty quantification; the outcome of any Data Assimilation study is strongly dependent on the uncertainties assigned to both observations and models. I focus on developing improved model uncertainty quantification methods that are applicable to challenging real world scenarios. These include developing methods for cases where the system states are only partially observed, where there is little prior knowledge of the model errors, and where the model error statistics are likely to be highly non-Gaussian.

  19. Modeling of uncertainties in statistical inverse problems

    International Nuclear Information System (INIS)

    Kaipio, Jari

    2008-01-01

    In all real world problems, the models that tie the measurements to the unknowns of interest, are at best only approximations for reality. While moderate modeling and approximation errors can be tolerated with stable problems, inverse problems are a notorious exception. Typical modeling errors include inaccurate geometry, unknown boundary and initial data, properties of noise and other disturbances, and simply the numerical approximations of the physical models. In principle, the Bayesian approach to inverse problems, in which all uncertainties are modeled as random variables, is capable of handling these uncertainties. Depending on the type of uncertainties, however, different strategies may be adopted. In this paper we give an overview of typical modeling errors and related strategies within the Bayesian framework.

  20. Uncertainty modeling process for semantic technology

    Directory of Open Access Journals (Sweden)

    Rommel N. Carvalho

    2016-08-01

    Full Text Available The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST, a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

  1. Chemical model reduction under uncertainty

    KAUST Repository

    Malpica Galassi, Riccardo; Valorani, Mauro; Najm, Habib N.; Safta, Cosmin; Khalil, Mohammad; Ciottoli, Pietro P.

    2017-01-01

    A general strategy for analysis and reduction of uncertain chemical kinetic models is presented, and its utility is illustrated in the context of ignition of hydrocarbon fuel–air mixtures. The strategy is based on a deterministic analysis

  2. 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

  3. Modeling Uncertainty in Climate Change: A Multi-Model Comparison

    Energy Technology Data Exchange (ETDEWEB)

    Gillingham, Kenneth; Nordhaus, William; Anthoff, David; Blanford, Geoffrey J.; Bosetti, Valentina; Christensen, Peter; McJeon, Haewon C.; Reilly, J. M.; Sztorc, Paul

    2015-10-01

    The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity and estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insight on tail events.

  4. Estimating Coastal Digital Elevation Model (DEM) Uncertainty

    Science.gov (United States)

    Amante, C.; Mesick, S.

    2017-12-01

    Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.

  5. An evaluation of uncertainties in radioecological models

    International Nuclear Information System (INIS)

    Hoffmann, F.O.; Little, C.A.; Miller, C.W.; Dunning, D.E. Jr.; Rupp, E.M.; Shor, R.W.; Schaeffer, D.L.; Baes, C.F. III

    1978-01-01

    The paper presents results of analyses for seven selected parameters commonly used in environmental radiological assessment models, assuming that the available data are representative of the true distribution of parameter values and that their respective distributions are lognormal. Estimates of the most probable, median, mean, and 99th percentile for each parameter are fiven and compared to U.S. NRC default values. The regulatory default values are generally greater than the median values for the selected parameters, but some are associated with percentiles significantly less than the 50th. The largest uncertainties appear to be associated with aquatic bioaccumulation factors for fresh water fish. Approximately one order of magnitude separates median values and values of the 99th percentile. The uncertainty is also estimated for the annual dose rate predicted by a multiplicative chain model for the transport of molecular iodine-131 via the air-pasture-cow-milk-child's thyroid pathway. The value for the 99th percentile is ten times larger than the median value of the predicted dose normalized for a given air concentration of 131 I 2 . About 72% of the uncertainty in this model is contributed by the dose conversion factor and the milk transfer coefficient. Considering the difficulties in obtaining a reliable quantification of the true uncertainties in model predictions, methods for taking these uncertainties into account when determining compliance with regulatory statutes are discussed. (orig./HP) [de

  6. Uncertainty quantification in wind farm flow models

    DEFF Research Database (Denmark)

    Murcia Leon, Juan Pablo

    uncertainties through a model chain are presented and applied to several wind energy related problems such as: annual energy production estimation, wind turbine power curve estimation, wake model calibration and validation, and estimation of lifetime equivalent fatigue loads on a wind turbine. Statistical...

  7. Uncertainty in biology a computational modeling approach

    CERN Document Server

    Gomez-Cabrero, David

    2016-01-01

    Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.  This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.  This book is intended for graduate stude...

  8. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  9. Uncertainties

    Indian Academy of Sciences (India)

    To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be ...

  10. Including model uncertainty in risk-informed decision making

    International Nuclear Information System (INIS)

    Reinert, Joshua M.; Apostolakis, George E.

    2006-01-01

    Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the risk achievement worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ΔCDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision. A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important. The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study

  11. Uncertainty in reactive transport geochemical modelling

    International Nuclear Information System (INIS)

    Oedegaard-Jensen, A.; Ekberg, C.

    2005-01-01

    Full text of publication follows: Geochemical modelling is one way of predicting the transport of i.e. radionuclides in a rock formation. In a rock formation there will be fractures in which water and dissolved species can be transported. The composition of the water and the rock can either increase or decrease the mobility of the transported entities. When doing simulations on the mobility or transport of different species one has to know the exact water composition, the exact flow rates in the fracture and in the surrounding rock, the porosity and which minerals the rock is composed of. The problem with simulations on rocks is that the rock itself it not uniform i.e. larger fractures in some areas and smaller in other areas which can give different water flows. The rock composition can be different in different areas. In additions to this variance in the rock there are also problems with measuring the physical parameters used in a simulation. All measurements will perturb the rock and this perturbation will results in more or less correct values of the interesting parameters. The analytical methods used are also encumbered with uncertainties which in this case are added to the uncertainty from the perturbation of the analysed parameters. When doing simulation the effect of the uncertainties must be taken into account. As the computers are getting faster and faster the complexity of simulated systems are increased which also increase the uncertainty in the results from the simulations. In this paper we will show how the uncertainty in the different parameters will effect the solubility and mobility of different species. Small uncertainties in the input parameters can result in large uncertainties in the end. (authors)

  12. Parametric uncertainty in optical image modeling

    Science.gov (United States)

    Potzick, James; Marx, Egon; Davidson, Mark

    2006-10-01

    Optical photomask feature metrology and wafer exposure process simulation both rely on optical image modeling for accurate results. While it is fair to question the accuracies of the available models, model results also depend on several input parameters describing the object and imaging system. Errors in these parameter values can lead to significant errors in the modeled image. These parameters include wavelength, illumination and objective NA's, magnification, focus, etc. for the optical system, and topography, complex index of refraction n and k, etc. for the object. In this paper each input parameter is varied over a range about its nominal value and the corresponding images simulated. Second order parameter interactions are not explored. Using the scenario of the optical measurement of photomask features, these parametric sensitivities are quantified by calculating the apparent change of the measured linewidth for a small change in the relevant parameter. Then, using reasonable values for the estimated uncertainties of these parameters, the parametric linewidth uncertainties can be calculated and combined to give a lower limit to the linewidth measurement uncertainty for those parameter uncertainties.

  13. Optical Model and Cross Section Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Herman,M.W.; Pigni, M.T.; Dietrich, F.S.; Oblozinsky, P.

    2009-10-05

    Distinct minima and maxima in the neutron total cross section uncertainties were observed in model calculations using spherical optical potential. We found this oscillating structure to be a general feature of quantum mechanical wave scattering. Specifically, we analyzed neutron interaction with 56Fe from 1 keV up to 65 MeV, and investigated physical origin of the minima.We discuss their potential importance for practical applications as well as the implications for the uncertainties in total and absorption cross sections.

  14. Uncertainty quantification and stochastic modeling with Matlab

    CERN Document Server

    Souza de Cursi, Eduardo

    2015-01-01

    Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does no

  15. Representing uncertainty on model analysis plots

    Directory of Open Access Journals (Sweden)

    Trevor I. Smith

    2016-09-01

    Full Text Available Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model. Unfortunately, Bao’s original presentation of the model plot did not include a way to represent uncertainty in these measurements. I present details of a method to add error bars to model plots by expanding the work of Sommer and Lindell. I also provide a template for generating model plots with error bars.

  16. UNCERTAINTIES IN GALACTIC CHEMICAL EVOLUTION MODELS

    International Nuclear Information System (INIS)

    Côté, Benoit; Ritter, Christian; Herwig, Falk; O’Shea, Brian W.; Pignatari, Marco; Jones, Samuel; Fryer, Chris L.

    2016-01-01

    We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M ⊙ formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions, along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model

  17. Economic policy uncertainty index and economic activity: what causes what?

    Directory of Open Access Journals (Sweden)

    Ivana Lolić

    2017-01-01

    Full Text Available This paper is a follow-up on the Economic Policy Uncertainty (EPU index, developed in 2011 by Baker, Bloom, and Davis. The principal idea of the EPU index is to quantify the level of uncertainty in an economic system, based on three separate pillars: news media, number of federal tax code provisions expiring in the following years, and disagreement amongst professional forecasters on future tendencies of relevant macroeconomic variables. Although the original EPU index was designed and published for the US economy, it had instantly caught the attention of numerous academics and was rapidly introduced in 15 countries worldwide. Extensive academic debate has been triggered on the importance of economic uncertainty relating to the intensity and persistence of the recent crisis. Despite the intensive (mostly politically-motivated debate, formal scientific confirmation of causality running from the EPU index to economic activity has not followed. Moreover, empirical literature has completely failed to conduct formal econometric testing of the Granger causality between the two mentioned phenomena. This paper provides an estimation of the Toda-Yamamoto causality test between the EPU index and economic activity in the USA and several European countries. The results do not provide a general conclusion: causality seems to run in both directions only for the USA, while only in one direction for France and Germany. Having taken into account the Great Recession of 2008, the main result does not change, therefore casting doubt on the index methodology and overall media bias.

  18. Uncertainty visualisation in the Model Web

    Science.gov (United States)

    Gerharz, L. E.; Autermann, C.; Hopmann, H.; Stasch, C.; Pebesma, E.

    2012-04-01

    Visualisation of geospatial data as maps is a common way to communicate spatially distributed information. If temporal and furthermore uncertainty information are included in the data, efficient visualisation methods are required. For uncertain spatial and spatio-temporal data, numerous visualisation methods have been developed and proposed, but only few tools for visualisation of data in a standardised way exist. Furthermore, usually they are realised as thick clients, and lack functionality of handling data coming from web services as it is envisaged in the Model Web. We present an interactive web tool for visualisation of uncertain spatio-temporal data developed in the UncertWeb project. The client is based on the OpenLayers JavaScript library. OpenLayers provides standard map windows and navigation tools, i.e. pan, zoom in/out, to allow interactive control for the user. Further interactive methods are implemented using jStat, a JavaScript library for statistics plots developed in UncertWeb, and flot. To integrate the uncertainty information into existing standards for geospatial data, the Uncertainty Markup Language (UncertML) was applied in combination with OGC Observations&Measurements 2.0 and JavaScript Object Notation (JSON) encodings for vector and NetCDF for raster data. The client offers methods to visualise uncertain vector and raster data with temporal information. Uncertainty information considered for the tool are probabilistic and quantified attribute uncertainties which can be provided as realisations or samples, full probability distributions functions and statistics. Visualisation is supported for uncertain continuous and categorical data. In the client, the visualisation is realised using a combination of different methods. Based on previously conducted usability studies, a differentiation between expert (in statistics or mapping) and non-expert users has been indicated as useful. Therefore, two different modes are realised together in the tool

  19. Uncertainty Quantification in Geomagnetic Field Modeling

    Science.gov (United States)

    Chulliat, A.; Nair, M. C.; Alken, P.; Meyer, B.; Saltus, R.; Woods, A.

    2017-12-01

    Geomagnetic field models are mathematical descriptions of the various sources of the Earth's magnetic field, and are generally obtained by solving an inverse problem. They are widely used in research to separate and characterize field sources, but also in many practical applications such as aircraft and ship navigation, smartphone orientation, satellite attitude control, and directional drilling. In recent years, more sophisticated models have been developed, thanks to the continuous availability of high quality satellite data and to progress in modeling techniques. Uncertainty quantification has become an integral part of model development, both to assess the progress made and to address specific users' needs. Here we report on recent advances made by our group in quantifying the uncertainty of geomagnetic field models. We first focus on NOAA's World Magnetic Model (WMM) and the International Geomagnetic Reference Field (IGRF), two reference models of the main (core) magnetic field produced every five years. We describe the methods used in quantifying the model commission error as well as the omission error attributed to various un-modeled sources such as magnetized rocks in the crust and electric current systems in the atmosphere and near-Earth environment. A simple error model was derived from this analysis, to facilitate usage in practical applications. We next report on improvements brought by combining a main field model with a high resolution crustal field model and a time-varying, real-time external field model, like in NOAA's High Definition Geomagnetic Model (HDGM). The obtained uncertainties are used by the directional drilling industry to mitigate health, safety and environment risks.

  20. Incorporating model parameter uncertainty into inverse treatment planning

    International Nuclear Information System (INIS)

    Lian Jun; Xing Lei

    2004-01-01

    Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment

  1. Realising the Uncertainty Enabled Model Web

    Science.gov (United States)

    Cornford, D.; Bastin, L.; Pebesma, E. J.; Williams, M.; Stasch, C.; Jones, R.; Gerharz, L.

    2012-12-01

    The FP7 funded UncertWeb project aims to create the "uncertainty enabled model web". The central concept here is that geospatial models and data resources are exposed via standard web service interfaces, such as the Open Geospatial Consortium (OGC) suite of encodings and interface standards, allowing the creation of complex workflows combining both data and models. The focus of UncertWeb is on the issue of managing uncertainty in such workflows, and providing the standards, architecture, tools and software support necessary to realise the "uncertainty enabled model web". In this paper we summarise the developments in the first two years of UncertWeb, illustrating several key points with examples taken from the use case requirements that motivate the project. Firstly we address the issue of encoding specifications. We explain the usage of UncertML 2.0, a flexible encoding for representing uncertainty based on a probabilistic approach. This is designed to be used within existing standards such as Observations and Measurements (O&M) and data quality elements of ISO19115 / 19139 (geographic information metadata and encoding specifications) as well as more broadly outside the OGC domain. We show profiles of O&M that have been developed within UncertWeb and how UncertML 2.0 is used within these. We also show encodings based on NetCDF and discuss possible future directions for encodings in JSON. We then discuss the issues of workflow construction, considering discovery of resources (both data and models). We discuss why a brokering approach to service composition is necessary in a world where the web service interfaces remain relatively heterogeneous, including many non-OGC approaches, in particular the more mainstream SOAP and WSDL approaches. We discuss the trade-offs between delegating uncertainty management functions to the service interfaces themselves and integrating the functions in the workflow management system. We describe two utility services to address

  2. Parametric uncertainty modeling for robust control

    DEFF Research Database (Denmark)

    Rasmussen, K.H.; Jørgensen, Sten Bay

    1999-01-01

    The dynamic behaviour of a non-linear process can often be approximated with a time-varying linear model. In the presented methodology the dynamics is modeled non-conservatively as parametric uncertainty in linear lime invariant models. The obtained uncertainty description makes it possible...... to perform robustness analysis on a control system using the structured singular value. The idea behind the proposed method is to fit a rational function to the parameter variation. The parameter variation can then be expressed as a linear fractional transformation (LFT), It is discussed how the proposed...... point changes. It is shown that a diagonal PI control structure provides robust performance towards variations in feed flow rate or feed concentrations. However including both liquid and vapor flow delays robust performance specifications cannot be satisfied with this simple diagonal control structure...

  3. 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

  4. 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

  5. Physical and Model Uncertainty for Fatigue Design of Composite Material

    DEFF Research Database (Denmark)

    Toft, Henrik Stensgaard; Sørensen, John Dalsgaard

    The main aim of the present report is to establish stochastic models for the uncertainties related to fatigue design of composite materials. The uncertainties considered are the physical uncertainty related to the static and fatigue strength and the model uncertainty related to Miners rule...

  6. Probabilistic Radiological Performance Assessment Modeling and Uncertainty

    Science.gov (United States)

    Tauxe, J.

    2004-12-01

    A generic probabilistic radiological Performance Assessment (PA) model is presented. The model, built using the GoldSim systems simulation software platform, concerns contaminant transport and dose estimation in support of decision making with uncertainty. Both the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE) require assessments of potential future risk to human receptors of disposal of LLW. Commercially operated LLW disposal facilities are licensed by the NRC (or agreement states), and the DOE operates such facilities for disposal of DOE-generated LLW. The type of PA model presented is probabilistic in nature, and hence reflects the current state of knowledge about the site by using probability distributions to capture what is expected (central tendency or average) and the uncertainty (e.g., standard deviation) associated with input parameters, and propagating through the model to arrive at output distributions that reflect expected performance and the overall uncertainty in the system. Estimates of contaminant release rates, concentrations in environmental media, and resulting doses to human receptors well into the future are made by running the model in Monte Carlo fashion, with each realization representing a possible combination of input parameter values. Statistical summaries of the results can be compared to regulatory performance objectives, and decision makers are better informed of the inherently uncertain aspects of the model which supports their decision-making. While this information may make some regulators uncomfortable, they must realize that uncertainties which were hidden in a deterministic analysis are revealed in a probabilistic analysis, and the chance of making a correct decision is now known rather than hoped for. The model includes many typical features and processes that would be part of a PA, but is entirely fictitious. This does not represent any particular site and is meant to be a generic example. A

  7. Intrinsic Uncertainties in Modeling Complex Systems.

    Energy Technology Data Exchange (ETDEWEB)

    Cooper, Curtis S; Bramson, Aaron L.; Ames, Arlo L.

    2014-09-01

    Models are built to understand and predict the behaviors of both natural and artificial systems. Because it is always necessary to abstract away aspects of any non-trivial system being modeled, we know models can potentially leave out important, even critical elements. This reality of the modeling enterprise forces us to consider the prospective impacts of those effects completely left out of a model - either intentionally or unconsidered. Insensitivity to new structure is an indication of diminishing returns. In this work, we represent a hypothetical unknown effect on a validated model as a finite perturba- tion whose amplitude is constrained within a control region. We find robustly that without further constraints, no meaningful bounds can be placed on the amplitude of a perturbation outside of the control region. Thus, forecasting into unsampled regions is a very risky proposition. We also present inherent difficulties with proper time discretization of models and representing in- herently discrete quantities. We point out potentially worrisome uncertainties, arising from math- ematical formulation alone, which modelers can inadvertently introduce into models of complex systems. Acknowledgements This work has been funded under early-career LDRD project #170979, entitled "Quantify- ing Confidence in Complex Systems Models Having Structural Uncertainties", which ran from 04/2013 to 09/2014. We wish to express our gratitude to the many researchers at Sandia who con- tributed ideas to this work, as well as feedback on the manuscript. In particular, we would like to mention George Barr, Alexander Outkin, Walt Beyeler, Eric Vugrin, and Laura Swiler for provid- ing invaluable advice and guidance through the course of the project. We would also like to thank Steven Kleban, Amanda Gonzales, Trevor Manzanares, and Sarah Burwell for their assistance in managing project tasks and resources.

  8. Modeling of uncertainties in biochemical reactions.

    Science.gov (United States)

    Mišković, Ljubiša; Hatzimanikatis, Vassily

    2011-02-01

    Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to

  9. 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

  10. Uncertainty Analysis on the Health Risk Caused by a Radiological Terror

    International Nuclear Information System (INIS)

    Jeong, Hyojoon; Hwang, Wontae; Kim, Eunhan; Han, Moonhee

    2010-01-01

    Atmospheric dispersion modeling and uncertainty analysis were carried out support the decision making in case of radiological emergency events. The risk caused by inhalation is highly dependent on air concentrations for radionuclides, therefore air monitoring and dispersion modeling have to be performed carefully to reduce the uncertainty of the health risk assessment for the public. When an intentional release of radioactive materials occurs in an urban area, air quality for radioactive materials in the environment is of great importance to take action for countermeasures and environmental risk assessments. Atmospheric modeling is part of the decision making tasks and that it is particularly important for emergency managers as they often need to take actions quickly on very inadequate information. In this study, we assume an 137 Cs explosion of 50 TBq using RDDs in the metropolitan area of Soul, South Korea. California Puff Model is used to calculate an atmospheric dispersion and transport for 137 Cs, and environmental risk analysis is performed using the Monte Carlo method

  11. Model uncertainty from a regulatory point of view

    International Nuclear Information System (INIS)

    Abramson, L.R.

    1994-01-01

    This paper discusses model uncertainty in the larger context of knowledge and random uncertainty. It explores some regulatory implications of model uncertainty and argues that, from a regulator's perspective, a conservative approach must be taken. As a consequence of this perspective, averaging over model results is ruled out

  12. Uncertainty associated with selected environmental transport models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-11-01

    A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation

  13. Methodology for characterizing modeling and discretization uncertainties in computational simulation

    Energy Technology Data Exchange (ETDEWEB)

    ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.

    2000-03-01

    This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.

  14. A novel dose uncertainty model and its application for dose verification

    International Nuclear Information System (INIS)

    Jin Hosang; Chung Heetaek; Liu Chihray; Palta, Jatinder; Suh, Tae-Suk; Kim, Siyong

    2005-01-01

    Based on statistical approach, a novel dose uncertainty model was introduced considering both nonspatial and spatial dose deviations. Non-space-oriented uncertainty is mainly caused by dosimetric uncertainties, and space-oriented dose uncertainty is the uncertainty caused by all spatial displacements. Assuming these two parts are independent, dose difference between measurement and calculation is a linear combination of nonspatial and spatial dose uncertainties. Two assumptions were made: (1) the relative standard deviation of nonspatial dose uncertainty is inversely proportional to the dose standard deviation σ, and (2) the spatial dose uncertainty is proportional to the gradient of dose. The total dose uncertainty is a quadratic sum of the nonspatial and spatial uncertainties. The uncertainty model provides the tolerance dose bound for comparison between calculation and measurement. In the statistical uncertainty model based on a Gaussian distribution, a confidence level of 3σ theoretically confines 99.74% of measurements within the bound. By setting the confidence limit, the tolerance bound for dose comparison can be made analogous to that of existing dose comparison methods (e.g., a composite distribution analysis, a γ test, a χ evaluation, and a normalized agreement test method). However, the model considers the inherent dose uncertainty characteristics of the test points by taking into account the space-specific history of dose accumulation, while the previous methods apply a single tolerance criterion to the points, although dose uncertainty at each point is significantly different from others. Three types of one-dimensional test dose distributions (a single large field, a composite flat field made by two identical beams, and three-beam intensity-modulated fields) were made to verify the robustness of the model. For each test distribution, the dose bound predicted by the uncertainty model was compared with simulated measurements. The simulated

  15. Implications of model uncertainty for the practice of risk assessment

    International Nuclear Information System (INIS)

    Laskey, K.B.

    1994-01-01

    A model is a representation of a system that can be used to answer questions about the system's behavior. The term model uncertainty refers to problems in which there is no generally agreed upon, validated model that can be used as a surrogate for the system itself. Model uncertainty affects both the methodology appropriate for building models and how models should be used. This paper discusses representations of model uncertainty, methodologies for exercising and interpreting models in the presence of model uncertainty, and the appropriate use of fallible models for policy making

  16. Development of Property Models with Uncertainty Estimate for Process Design under Uncertainty

    DEFF Research Database (Denmark)

    Hukkerikar, Amol; Sarup, Bent; Abildskov, Jens

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

  17. Classification and moral evaluation of uncertainties in engineering modeling.

    Science.gov (United States)

    Murphy, Colleen; Gardoni, Paolo; Harris, Charles E

    2011-09-01

    Engineers must deal with risks and uncertainties as a part of their professional work and, in particular, uncertainties are inherent to engineering models. Models play a central role in engineering. Models often represent an abstract and idealized version of the mathematical properties of a target. Using models, engineers can investigate and acquire understanding of how an object or phenomenon will perform under specified conditions. This paper defines the different stages of the modeling process in engineering, classifies the various sources of uncertainty that arise in each stage, and discusses the categories into which these uncertainties fall. The paper then considers the way uncertainty and modeling are approached in science and the criteria for evaluating scientific hypotheses, in order to highlight the very different criteria appropriate for the development of models and the treatment of the inherent uncertainties in engineering. Finally, the paper puts forward nine guidelines for the treatment of uncertainty in engineering modeling.

  18. 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.

  19. Climate change decision-making: Model & parameter uncertainties explored

    Energy Technology Data Exchange (ETDEWEB)

    Dowlatabadi, H.; Kandlikar, M.; Linville, C.

    1995-12-31

    A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.

  20. How to: understanding SWAT model uncertainty relative to measured results

    Science.gov (United States)

    Watershed models are being relied upon to contribute to most policy-making decisions of watershed management, and the demand for an accurate accounting of complete model uncertainty is rising. Generalized likelihood uncertainty estimation (GLUE) is a widely used method for quantifying uncertainty i...

  1. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario....

  2. Uncertainties and quantification of common cause failure rates and probabilities for system analyses

    International Nuclear Information System (INIS)

    Vaurio, Jussi K.

    2005-01-01

    Simultaneous failures of multiple components due to common causes at random times are modelled by constant multiple-failure rates. A procedure is described for quantification of common cause failure (CCF) basic event probabilities for system models using plant-specific and multiple-plant failure-event data. Methodology is presented for estimating CCF-rates from event data contaminated with assessment uncertainties. Generalised impact vectors determine the moments for the rates of individual systems or plants. These moments determine the effective numbers of events and observation times to be input to a Bayesian formalism to obtain plant-specific posterior CCF-rates. The rates are used to determine plant-specific common cause event probabilities for the basic events of explicit fault tree models depending on test intervals, test schedules and repair policies. Three methods are presented to determine these probabilities such that the correct time-average system unavailability can be obtained with single fault tree quantification. Recommended numerical values are given and examples illustrate different aspects of the methodology

  3. Detailed modeling of the statistical uncertainty of Thomson scattering measurements

    International Nuclear Information System (INIS)

    Morton, L A; Parke, E; Hartog, D J Den

    2013-01-01

    The uncertainty of electron density and temperature fluctuation measurements is determined by statistical uncertainty introduced by multiple noise sources. In order to quantify these uncertainties precisely, a simple but comprehensive model was made of the noise sources in the MST Thomson scattering system and of the resulting variance in the integrated scattered signals. The model agrees well with experimental and simulated results. The signal uncertainties are then used by our existing Bayesian analysis routine to find the most likely electron temperature and density, with confidence intervals. In the model, photonic noise from scattered light and plasma background light is multiplied by the noise enhancement factor (F) of the avalanche photodiode (APD). Electronic noise from the amplifier and digitizer is added. The amplifier response function shapes the signal and induces correlation in the noise. The data analysis routine fits a characteristic pulse to the digitized signals from the amplifier, giving the integrated scattered signals. A finite digitization rate loses information and can cause numerical integration error. We find a formula for the variance of the scattered signals in terms of the background and pulse amplitudes, and three calibration constants. The constants are measured easily under operating conditions, resulting in accurate estimation of the scattered signals' uncertainty. We measure F ≈ 3 for our APDs, in agreement with other measurements for similar APDs. This value is wavelength-independent, simplifying analysis. The correlated noise we observe is reproduced well using a Gaussian response function. Numerical integration error can be made negligible by using an interpolated characteristic pulse, allowing digitization rates as low as the detector bandwidth. The effect of background noise is also determined

  4. Modelling of data uncertainties on hybrid computers

    Energy Technology Data Exchange (ETDEWEB)

    Schneider, Anke (ed.)

    2016-06-15

    The codes d{sup 3}f and r{sup 3}t are well established for modelling density-driven flow and nuclide transport in the far field of repositories for hazardous material in deep geological formations. They are applicable in porous media as well as in fractured rock or mudstone, for modelling salt- and heat transport as well as a free groundwater surface. Development of the basic framework of d{sup 3}f and r{sup 3}t had begun more than 20 years ago. Since that time significant advancements took place in the requirements for safety assessment as well as for computer hardware development. The period of safety assessment for a repository of high-level radioactive waste was extended to 1 million years, and the complexity of the models is steadily growing. Concurrently, the demands on accuracy increase. Additionally, model and parameter uncertainties become more and more important for an increased understanding of prediction reliability. All this leads to a growing demand for computational power that requires a considerable software speed-up. An effective way to achieve this is the use of modern, hybrid computer architectures which requires basically the set-up of new data structures and a corresponding code revision but offers a potential speed-up by several orders of magnitude. The original codes d{sup 3}f and r{sup 3}t were applications of the software platform UG /BAS 94/ whose development had begun in the early nineteennineties. However, UG had recently been advanced to the C++ based, substantially revised version UG4 /VOG 13/. To benefit also in the future from state-of-the-art numerical algorithms and to use hybrid computer architectures, the codes d{sup 3}f and r{sup 3}t were transferred to this new code platform. Making use of the fact that coupling between different sets of equations is natively supported in UG4, d{sup 3}f and r{sup 3}t were combined to one conjoint code d{sup 3}f++. A direct estimation of uncertainties for complex groundwater flow models with the

  5. Uncertainty and Preference Modelling for Multiple Criteria Vehicle Evaluation

    Directory of Open Access Journals (Sweden)

    Qiuping Yang

    2010-12-01

    Full Text Available A general framework for vehicle assessment is proposed based on both mass survey information and the evidential reasoning (ER approach. Several methods for uncertainty and preference modeling are developed within the framework, including the measurement of uncertainty caused by missing information, the estimation of missing information in original surveys, the use of nonlinear functions for data mapping, and the use of nonlinear functions as utility function to combine distributed assessments into a single index. The results of the investigation show that various measures can be used to represent the different preferences of decision makers towards the same feedback from respondents. Based on the ER approach, credible and informative analysis can be conducted through the complete understanding of the assessment problem in question and the full exploration of available information.

  6. Uncertainty calculation in transport models and forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Prato, Carlo Giacomo

    Transport projects and policy evaluations are often based on transport model output, i.e. traffic flows and derived effects. However, literature has shown that there is often a considerable difference between forecasted and observed traffic flows. This difference causes misallocation of (public...... implemented by using an approach based on stochastic techniques (Monte Carlo simulation and Bootstrap re-sampling) or scenario analysis combined with model sensitivity tests. Two transport models are used as case studies: the Næstved model and the Danish National Transport Model. 3 The first paper...... in a four-stage transport model related to different variable distributions (to be used in a Monte Carlo simulation procedure), assignment procedures and levels of congestion, at both the link and the network level. The analysis used as case study the Næstved model, referring to the Danish town of Næstved2...

  7. Uncertainty in a spatial evacuation model

    Science.gov (United States)

    Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De

    2017-08-01

    Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.

  8. Characterization uncertainty and its effects on models and performance

    International Nuclear Information System (INIS)

    Rautman, C.A.; Treadway, A.H.

    1991-01-01

    Geostatistical simulation is being used to develop multiple geologic models of rock properties at the proposed Yucca Mountain repository site. Because each replicate model contains the same known information, and is thus essentially indistinguishable statistically from others, the differences between models may be thought of as representing the uncertainty in the site description. The variability among performance measures, such as ground water travel time, calculated using these replicate models therefore quantifies the uncertainty in performance that arises from uncertainty in site characterization

  9. Identification and communication of uncertainties of phenomenological models in PSA

    International Nuclear Information System (INIS)

    Pulkkinen, U.; Simola, K.

    2001-11-01

    This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)

  10. Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

    Science.gov (United States)

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

    2018-05-01

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

  11. Uncertainty analysis in estimating Japanese ingestion of global fallout Cs-137 using health risk evaluation model

    International Nuclear Information System (INIS)

    Shimada, Yoko; Morisawa, Shinsuke

    1998-01-01

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

  12. Imprecision and Uncertainty in the UFO Database Model.

    Science.gov (United States)

    Van Gyseghem, Nancy; De Caluwe, Rita

    1998-01-01

    Discusses how imprecision and uncertainty are dealt with in the UFO (Uncertainty and Fuzziness in an Object-oriented) database model. Such information is expressed by means of possibility distributions, and modeled by means of the proposed concept of "role objects." The role objects model uncertain, tentative information about objects,…

  13. Multi-scenario modelling of uncertainty in stochastic chemical systems

    International Nuclear Information System (INIS)

    Evans, R. David; Ricardez-Sandoval, Luis A.

    2014-01-01

    Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation. -- Highlights: •A method to model uncertainty on stochastic systems was developed. •The method is based on the Chemical Master Equation. •Uncertainty in an isomerization reaction and a gene regulation network was modelled. •Effects were significant and dependent on the uncertain input and reaction system. •The model was computationally more efficient than Kinetic Monte Carlo

  14. 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.

  15. Uncertainty models applied to the substation planning

    Energy Technology Data Exchange (ETDEWEB)

    Fontoura Filho, Roberto N [ELETROBRAS, Rio de Janeiro, RJ (Brazil); Aires, Joao Carlos O; Tortelly, Debora L.S. [Light Servicos de Eletricidade S.A., Rio de Janeiro, RJ (Brazil)

    1994-12-31

    The selection of the reinforcements for a power system expansion becomes a difficult task on an environment of uncertainties. These uncertainties can be classified according to their sources as exogenous and endogenous. The first one is associated to the elements of the generation, transmission and distribution systems. The exogenous uncertainly is associated to external aspects, as the financial resources, the time spent to build the installations, the equipment price and the load level. The load uncertainly is extremely sensible to the behaviour of the economic conditions. Although the impossibility to take out completely the uncertainty , the endogenous one can be convenient treated and the exogenous uncertainly can be compensated. This paper describes an uncertainty treatment methodology and a practical application to a group of substations belonging to LIGHT company, the Rio de Janeiro electric utility. The equipment performance uncertainty is treated by adopting a probabilistic approach. The uncertainly associated to the load increase is considered by using technical analysis of scenarios and choice criteria based on the Decision Theory. On this paper it was used the Savage Method and the Fuzzy Set Method, in order to select the best middle term reinforcements plan. (author) 7 refs., 4 figs., 6 tabs.

  16. Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology

    Science.gov (United States)

    Rivera, Diego; Rivas, Yessica; Godoy, Alex

    2015-02-01

    Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s-1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.

  17. 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

  18. Uncertainty and endogenous technical change in climate policy models

    International Nuclear Information System (INIS)

    Baker, Erin; Shittu, Ekundayo

    2008-01-01

    Until recently endogenous technical change and uncertainty have been modeled separately in climate policy models. In this paper, we review the emerging literature that considers both these elements together. Taken as a whole the literature indicates that explicitly including uncertainty has important quantitative and qualitative impacts on optimal climate change technology policy. (author)

  19. Appropriatie spatial scales to achieve model output uncertainty goals

    NARCIS (Netherlands)

    Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun

    2008-01-01

    Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between

  20. Uncertainty

    International Nuclear Information System (INIS)

    Silva, T.A. da

    1988-01-01

    The comparison between the uncertainty method recommended by International Atomic Energy Agency (IAEA) and the and the International Weight and Measure Commitee (CIPM) are showed, for the calibration of clinical dosimeters in the secondary standard Dosimetry Laboratory (SSDL). (C.G.C.) [pt

  1. 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...

  2. Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

    Directory of Open Access Journals (Sweden)

    Jae-Han Park

    2012-06-01

    Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  3. Spatial uncertainty model for visual features using a Kinect™ sensor.

    Science.gov (United States)

    Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong

    2012-01-01

    This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  4. CHARACTERIZING AND PROPAGATING MODELING UNCERTAINTIES IN PHOTOMETRICALLY DERIVED REDSHIFT DISTRIBUTIONS

    International Nuclear Information System (INIS)

    Abrahamse, Augusta; Knox, Lloyd; Schmidt, Samuel; Thorman, Paul; Anthony Tyson, J.; Zhan Hu

    2011-01-01

    The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from multi-band imaging surveys. The accuracy of the photometric redshifts measured in these surveys depends not only on the quality of the flux data, but also on a number of modeling assumptions that enter into both the training set and spectral energy distribution (SED) fitting methods of photometric redshift estimation. In this work we focus on the latter, considering two types of modeling uncertainties: uncertainties in the SED template set and uncertainties in the magnitude and type priors used in a Bayesian photometric redshift estimation method. We find that SED template selection effects dominate over magnitude prior errors. We introduce a method for parameterizing the resulting ignorance of the redshift distributions, and for propagating these uncertainties to uncertainties in cosmological parameters.

  5. A Bayesian approach for quantification of model uncertainty

    International Nuclear Information System (INIS)

    Park, Inseok; Amarchinta, Hemanth K.; Grandhi, Ramana V.

    2010-01-01

    In most engineering problems, more than one model can be created to represent an engineering system's behavior. Uncertainty is inevitably involved in selecting the best model from among the models that are possible. Uncertainty in model selection cannot be ignored, especially when the differences between the predictions of competing models are significant. In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework. The adjustment factor approach is used to propagate model uncertainty into prediction of a system response. A nonlinear vibration system is used to demonstrate the processes for implementing the adjustment factor approach. Finally, the methodology is applied on the engineering benefits of a laser peening process, and a confidence band for residual stresses is established to indicate the reliability of model prediction.

  6. Modeling uncertainty in requirements engineering decision support

    Science.gov (United States)

    Feather, Martin S.; Maynard-Zhang, Pedrito; Kiper, James D.

    2005-01-01

    One inherent characteristic of requrements engineering is a lack of certainty during this early phase of a project. Nevertheless, decisions about requirements must be made in spite of this uncertainty. Here we describe the context in which we are exploring this, and some initial work to support elicitation of uncertain requirements, and to deal with the combination of such information from multiple stakeholders.

  7. Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

    Science.gov (United States)

    Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

    2016-12-01

    Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate

  8. Reservoir management under geological uncertainty using fast model update

    NARCIS (Netherlands)

    Hanea, R.; Evensen, G.; Hustoft, L.; Ek, T.; Chitu, A.; Wilschut, F.

    2015-01-01

    Statoil is implementing "Fast Model Update (FMU)," an integrated and automated workflow for reservoir modeling and characterization. FMU connects all steps and disciplines from seismic depth conversion to prediction and reservoir management taking into account relevant reservoir uncertainty. FMU

  9. Sustainable infrastructure system modeling under uncertainties and dynamics

    Science.gov (United States)

    Huang, Yongxi

    potential risks caused by feedstock seasonality and demand uncertainty. Facility spatiality, time variation of feedstock yields, and demand uncertainty are integrated into a two-stage stochastic programming (SP) framework. In the study of Transitional Energy System Modeling under Uncertainty, a multistage stochastic dynamic programming is established to optimize the process of building and operating fuel production facilities during the transition. Dynamics due to the evolving technologies and societal changes and uncertainty due to demand fluctuations are the major issues to be addressed.

  10. 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.

  11. 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)

  12. 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.

  13. Modelling geological uncertainty for mine planning

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, M

    1980-07-01

    Geosimplan is an operational gaming approach used in testing a proposed mining strategy against uncertainty in geological disturbance. Geoplan is a technique which facilitates the preparation of summary analyses to give an impression of size, distribution and quality of reserves, and to assist in calculation of year by year output estimates. Geoplan concentrates on variations in seam properties and the interaction between geological information and marketing and output requirements.

  14. IAEA CRP on HTGR Uncertainties in Modeling: Assessment of Phase I Lattice to Core Model Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Rouxelin, Pascal Nicolas [Idaho National Lab. (INL), Idaho Falls, ID (United States); Strydom, Gerhard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2016-09-01

    Best-estimate plus uncertainty analysis of reactors is replacing the traditional conservative (stacked uncertainty) method for safety and licensing analysis. To facilitate uncertainty analysis applications, a comprehensive approach and methodology must be developed and applied. High temperature gas cooled reactors (HTGRs) have several features that require techniques not used in light-water reactor analysis (e.g., coated-particle design and large graphite quantities at high temperatures). The International Atomic Energy Agency has therefore launched the Coordinated Research Project on HTGR Uncertainty Analysis in Modeling to study uncertainty propagation in the HTGR analysis chain. The benchmark problem defined for the prismatic design is represented by the General Atomics Modular HTGR 350. The main focus of this report is the compilation and discussion of the results obtained for various permutations of Exercise I 2c and the use of the cross section data in Exercise II 1a of the prismatic benchmark, which is defined as the last and first steps of the lattice and core simulation phases, respectively. The report summarizes the Idaho National Laboratory (INL) best estimate results obtained for Exercise I 2a (fresh single-fuel block), Exercise I 2b (depleted single-fuel block), and Exercise I 2c (super cell) in addition to the first results of an investigation into the cross section generation effects for the super-cell problem. The two dimensional deterministic code known as the New ESC based Weighting Transport (NEWT) included in the Standardized Computer Analyses for Licensing Evaluation (SCALE) 6.1.2 package was used for the cross section evaluation, and the results obtained were compared to the three dimensional stochastic SCALE module KENO VI. The NEWT cross section libraries were generated for several permutations of the current benchmark super-cell geometry and were then provided as input to the Phase II core calculation of the stand alone neutronics Exercise

  15. Development of a Prototype Model-Form Uncertainty Knowledge Base

    Science.gov (United States)

    Green, Lawrence L.

    2016-01-01

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

  16. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the ‘most likely’ di...

  17. A Model-Free Definition of Increasing Uncertainty

    NARCIS (Netherlands)

    Grant, S.; Quiggin, J.

    2001-01-01

    We present a definition of increasing uncertainty, independent of any notion of subjective probabilities, or of any particular model of preferences.Our notion of an elementary increase in the uncertainty of any act corresponds to the addition of an 'elementary bet' which increases consumption by a

  18. Improved Wave-vessel Transfer Functions by Uncertainty Modelling

    DEFF Research Database (Denmark)

    Nielsen, Ulrik Dam; Fønss Bach, Kasper; Iseki, Toshio

    2016-01-01

    This paper deals with uncertainty modelling of wave-vessel transfer functions used to calculate or predict wave-induced responses of a ship in a seaway. Although transfer functions, in theory, can be calculated to exactly reflect the behaviour of the ship when exposed to waves, uncertainty in inp...

  19. Uncertainties in environmental radiological assessment models and their implications

    International Nuclear Information System (INIS)

    Hoffman, F.O.; Miller, C.W.

    1983-01-01

    Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because these models are inexact representations of real systems. The major sources of this uncertainty are related to biases in model formulation and parameter estimation. The best approach for estimating the actual extent of over- or underprediction is model validation, a procedure that requires testing over the range of the intended realm of model application. Other approaches discussed are the use of screening procedures, sensitivity and stochastic analyses, and model comparison. The magnitude of uncertainty in model predictions is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. Estimates are made of the relative magnitude of uncertainty for situations requiring predictions of individual and collective risks for both chronic and acute releases of radionuclides. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible

  20. Bayesian models for comparative analysis integrating phylogenetic uncertainty

    Directory of Open Access Journals (Sweden)

    Villemereuil Pierre de

    2012-06-01

    Full Text Available Abstract Background Uncertainty in comparative analyses can come from at least two sources: a phylogenetic uncertainty in the tree topology or branch lengths, and b uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow and inflated significance in hypothesis testing (e.g. p-values will be too small. Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible

  1. Bayesian models for comparative analysis integrating phylogenetic uncertainty

    Science.gov (United States)

    2012-01-01

    Background Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for

  2. Sensitivity and uncertainty analyses for performance assessment modeling

    International Nuclear Information System (INIS)

    Doctor, P.G.

    1988-08-01

    Sensitivity and uncertainty analyses methods for computer models are being applied in performance assessment modeling in the geologic high level radioactive waste repository program. The models used in performance assessment tend to be complex physical/chemical models with large numbers of input variables. There are two basic approaches to sensitivity and uncertainty analyses: deterministic and statistical. The deterministic approach to sensitivity analysis involves numerical calculation or employs the adjoint form of a partial differential equation to compute partial derivatives; the uncertainty analysis is based on Taylor series expansions of the input variables propagated through the model to compute means and variances of the output variable. The statistical approach to sensitivity analysis involves a response surface approximation to the model with the sensitivity coefficients calculated from the response surface parameters; the uncertainty analysis is based on simulation. The methods each have strengths and weaknesses. 44 refs

  3. 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

  4. Statistical Uncertainty Quantification of Physical Models during Reflood of LBLOCA

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Deog Yeon; Seul, Kwang Won; Woo, Sweng Woong [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

    2015-05-15

    The use of the best-estimate (BE) computer codes in safety analysis for loss-of-coolant accident (LOCA) is the major trend in many countries to reduce the significant conservatism. A key feature of this BE evaluation requires the licensee to quantify the uncertainty of the calculations. So, it is very important how to determine the uncertainty distribution before conducting the uncertainty evaluation. Uncertainty includes those of physical model and correlation, plant operational parameters, and so forth. The quantification process is often performed mainly by subjective expert judgment or obtained from reference documents of computer code. In this respect, more mathematical methods are needed to reasonably determine the uncertainty ranges. The first uncertainty quantification are performed with the various increments for two influential uncertainty parameters to get the calculated responses and their derivatives. The different data set with two influential uncertainty parameters for FEBA tests, are chosen applying more strict criteria for selecting responses and their derivatives, which may be considered as the user’s effect in the CIRCÉ applications. Finally, three influential uncertainty parameters are considered to study the effect on the number of uncertainty parameters due to the limitation of CIRCÉ method. With the determined uncertainty ranges, uncertainty evaluations for FEBA tests are performed to check whether the experimental responses such as the cladding temperature or pressure drop are inside the limits of calculated uncertainty bounds. A confirmation step will be performed to evaluate the quality of the information in the case of the different reflooding PERICLES experiments. The uncertainty ranges of physical model in MARS-KS thermal-hydraulic code during the reflooding were quantified by CIRCÉ method using FEBA experiment tests, instead of expert judgment. Also, through the uncertainty evaluation for FEBA and PERICLES tests, it was confirmed

  5. 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

  6. Modeling theoretical uncertainties in phenomenological analyses for particle physics

    Energy Technology Data Exchange (ETDEWEB)

    Charles, Jerome [CNRS, Aix-Marseille Univ, Universite de Toulon, CPT UMR 7332, Marseille Cedex 9 (France); Descotes-Genon, Sebastien [CNRS, Univ. Paris-Sud, Universite Paris-Saclay, Laboratoire de Physique Theorique (UMR 8627), Orsay Cedex (France); Niess, Valentin [CNRS/IN2P3, UMR 6533, Laboratoire de Physique Corpusculaire, Aubiere Cedex (France); Silva, Luiz Vale [CNRS, Univ. Paris-Sud, Universite Paris-Saclay, Laboratoire de Physique Theorique (UMR 8627), Orsay Cedex (France); Univ. Paris-Sud, CNRS/IN2P3, Universite Paris-Saclay, Groupe de Physique Theorique, Institut de Physique Nucleaire, Orsay Cedex (France); J. Stefan Institute, Jamova 39, P. O. Box 3000, Ljubljana (Slovenia)

    2017-04-15

    The determination of the fundamental parameters of the Standard Model (and its extensions) is often limited by the presence of statistical and theoretical uncertainties. We present several models for the latter uncertainties (random, nuisance, external) in the frequentist framework, and we derive the corresponding p values. In the case of the nuisance approach where theoretical uncertainties are modeled as biases, we highlight the important, but arbitrary, issue of the range of variation chosen for the bias parameters. We introduce the concept of adaptive p value, which is obtained by adjusting the range of variation for the bias according to the significance considered, and which allows us to tackle metrology and exclusion tests with a single and well-defined unified tool, which exhibits interesting frequentist properties. We discuss how the determination of fundamental parameters is impacted by the model chosen for theoretical uncertainties, illustrating several issues with examples from quark flavor physics. (orig.)

  7. A sliding mode observer for hemodynamic characterization under modeling uncertainties

    KAUST Repository

    Zayane, Chadia; Laleg-Kirati, Taous-Meriem

    2014-01-01

    This paper addresses the case of physiological states reconstruction in a small region of the brain under modeling uncertainties. The misunderstood coupling between the cerebral blood volume and the oxygen extraction fraction has lead to a partial

  8. Uncertainty modelling of critical column buckling for reinforced ...

    Indian Academy of Sciences (India)

    for columns, having major importance to a building's safety, are considered stability limits. ... Various research works have been carried out for uncertainty analysis in ... need appropriate material models, advanced structural simulation tools.

  9. Uncertainty of Modal Parameters Estimated by ARMA Models

    DEFF Research Database (Denmark)

    Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders

    1990-01-01

    In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore......, it is shown that the model errors may also contribute significantly to the uncertainty....

  10. Innovative supply chain optimization models with multiple uncertainty factors

    DEFF Research Database (Denmark)

    Choi, Tsan Ming; Govindan, Kannan; Li, Xiang

    2017-01-01

    Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate...... to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models...

  11. Impact of dose-distribution uncertainties on rectal ntcp modeling I: Uncertainty estimates

    International Nuclear Information System (INIS)

    Fenwick, John D.; Nahum, Alan E.

    2001-01-01

    A trial of nonescalated conformal versus conventional radiotherapy treatment of prostate cancer has been carried out at the Royal Marsden NHS Trust (RMH) and Institute of Cancer Research (ICR), demonstrating a significant reduction in the rate of rectal bleeding reported for patients treated using the conformal technique. The relationship between planned rectal dose-distributions and incidences of bleeding has been analyzed, showing that the rate of bleeding falls significantly as the extent of the rectal wall receiving a planned dose-level of more than 57 Gy is reduced. Dose-distributions delivered to the rectal wall over the course of radiotherapy treatment inevitably differ from planned distributions, due to sources of uncertainty such as patient setup error, rectal wall movement and variation in the absolute rectal wall surface area. In this paper estimates of the differences between planned and treated rectal dose-distribution parameters are obtained for the RMH/ICR nonescalated conformal technique, working from a distribution of setup errors observed during the RMH/ICR trial, movement data supplied by Lebesque and colleagues derived from repeat CT scans, and estimates of rectal circumference variations extracted from the literature. Setup errors and wall movement are found to cause only limited systematic differences between mean treated and planned rectal dose-distribution parameter values, but introduce considerable uncertainties into the treated values of some dose-distribution parameters: setup errors lead to 22% and 9% relative uncertainties in the highly dosed fraction of the rectal wall and the wall average dose, respectively, with wall movement leading to 21% and 9% relative uncertainties. Estimates obtained from the literature of the uncertainty in the absolute surface area of the distensible rectal wall are of the order of 13%-18%. In a subsequent paper the impact of these uncertainties on analyses of the relationship between incidences of bleeding

  12. Modelling and propagation of uncertainties in the German Risk Study

    International Nuclear Information System (INIS)

    Hofer, E.; Krzykacz, B.

    1982-01-01

    Risk assessments are generally subject to uncertainty considerations. This is because of the various estimates that are involved. The paper points out those estimates in the so-called phase A of the German Risk Study, for which uncertainties were quantified. It explains the probabilistic models applied in the assessment to their impact on the findings of the study. Finally the resulting subjective confidence intervals of the study results are presented and their sensitivity to these probabilistic models is investigated

  13. Modelling ecosystem service flows under uncertainty with stochiastic SPAN

    Science.gov (United States)

    Johnson, Gary W.; Snapp, Robert R.; Villa, Ferdinando; Bagstad, Kenneth J.

    2012-01-01

    Ecosystem service models are increasingly in demand for decision making. However, the data required to run these models are often patchy, missing, outdated, or untrustworthy. Further, communication of data and model uncertainty to decision makers is often either absent or unintuitive. In this work, we introduce a systematic approach to addressing both the data gap and the difficulty in communicating uncertainty through a stochastic adaptation of the Service Path Attribution Networks (SPAN) framework. The SPAN formalism assesses ecosystem services through a set of up to 16 maps, which characterize the services in a study area in terms of flow pathways between ecosystems and human beneficiaries. Although the SPAN algorithms were originally defined deterministically, we present them here in a stochastic framework which combines probabilistic input data with a stochastic transport model in order to generate probabilistic spatial outputs. This enables a novel feature among ecosystem service models: the ability to spatially visualize uncertainty in the model results. The stochastic SPAN model can analyze areas where data limitations are prohibitive for deterministic models. Greater uncertainty in the model inputs (including missing data) should lead to greater uncertainty expressed in the model’s output distributions. By using Bayesian belief networks to fill data gaps and expert-provided trust assignments to augment untrustworthy or outdated information, we can account for uncertainty in input data, producing a model that is still able to run and provide information where strictly deterministic models could not. Taken together, these attributes enable more robust and intuitive modelling of ecosystem services under uncertainty.

  14. Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model

    Science.gov (United States)

    Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.

    2012-12-01

    Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root

  15. The validation of evacuation simulation models through the analysis of behavioural uncertainty

    International Nuclear Information System (INIS)

    Lovreglio, Ruggiero; Ronchi, Enrico; Borri, Dino

    2014-01-01

    Both experimental and simulation data on fire evacuation are influenced by a component of uncertainty caused by the impact of the unexplained variance in human behaviour, namely behavioural uncertainty (BU). Evacuation model validation studies should include the study of this type of uncertainty during the comparison of experiments and simulation results. An evacuation model validation procedure is introduced in this paper to study the impact of BU. This methodology is presented through a case study for the comparison between repeated experimental data and simulation results produced by FDS+Evac, an evacuation model for the simulation of human behaviour in fire, which makes use of distribution laws. - Highlights: • Validation of evacuation models is investigated. • Quantitative evaluation of behavioural uncertainty is performed. • A validation procedure is presented through an evacuation case study

  16. Model-specification uncertainty in future forest pest outbreak.

    Science.gov (United States)

    Boulanger, Yan; Gray, David R; Cooke, Barry J; De Grandpré, Louis

    2016-04-01

    Climate change will modify forest pest outbreak characteristics, although there are disagreements regarding the specifics of these changes. A large part of this variability may be attributed to model specifications. As a case study, we developed a consensus model predicting spruce budworm (SBW, Choristoneura fumiferana [Clem.]) outbreak duration using two different predictor data sets and six different correlative methods. The model was used to project outbreak duration and the uncertainty associated with using different data sets and correlative methods (=model-specification uncertainty) for 2011-2040, 2041-2070 and 2071-2100, according to three forcing scenarios (RCP 2.6, RCP 4.5 and RCP 8.5). The consensus model showed very high explanatory power and low bias. The model projected a more important northward shift and decrease in outbreak duration under the RCP 8.5 scenario. However, variation in single-model projections increases with time, making future projections highly uncertain. Notably, the magnitude of the shifts in northward expansion, overall outbreak duration and the patterns of outbreaks duration at the southern edge were highly variable according to the predictor data set and correlative method used. We also demonstrated that variation in forcing scenarios contributed only slightly to the uncertainty of model projections compared with the two sources of model-specification uncertainty. Our approach helped to quantify model-specification uncertainty in future forest pest outbreak characteristics. It may contribute to sounder decision-making by acknowledging the limits of the projections and help to identify areas where model-specification uncertainty is high. As such, we further stress that this uncertainty should be strongly considered when making forest management plans, notably by adopting adaptive management strategies so as to reduce future risks. © 2015 Her Majesty the Queen in Right of Canada Global Change Biology © 2015 Published by John

  17. Uncertainty analysis for a field-scale P loss model

    Science.gov (United States)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predic...

  18. Meteorological uncertainty of atmospheric dispersion model results (MUD)

    Energy Technology Data Exchange (ETDEWEB)

    Havskov Soerensen, J.; Amstrup, B.; Feddersen, H. [Danish Meteorological Institute, Copenhagen (Denmark)] [and others

    2013-08-15

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

  19. Meteorological uncertainty of atmospheric dispersion model results (MUD)

    International Nuclear Information System (INIS)

    Havskov Soerensen, J.; Amstrup, B.; Feddersen, H.

    2013-08-01

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for long-range atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can be derived. (Author)

  20. 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

  1. 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

  2. Partitioning uncertainty in streamflow projections under nonstationary model conditions

    Science.gov (United States)

    Chawla, Ila; Mujumdar, P. P.

    2018-02-01

    Assessing the impacts of Land Use (LU) and climate change on future streamflow projections is necessary for efficient management of water resources. However, model projections are burdened with significant uncertainty arising from various sources. Most of the previous studies have considered climate models and scenarios as major sources of uncertainty, but uncertainties introduced by land use change and hydrologic model assumptions are rarely investigated. In this paper an attempt is made to segregate the contribution from (i) general circulation models (GCMs), (ii) emission scenarios, (iii) land use scenarios, (iv) stationarity assumption of the hydrologic model, and (v) internal variability of the processes, to overall uncertainty in streamflow projections using analysis of variance (ANOVA) approach. Generally, most of the impact assessment studies are carried out with unchanging hydrologic model parameters in future. It is, however, necessary to address the nonstationarity in model parameters with changing land use and climate. In this paper, a regression based methodology is presented to obtain the hydrologic model parameters with changing land use and climate scenarios in future. The Upper Ganga Basin (UGB) in India is used as a case study to demonstrate the methodology. The semi-distributed Variable Infiltration Capacity (VIC) model is set-up over the basin, under nonstationary conditions. Results indicate that model parameters vary with time, thereby invalidating the often-used assumption of model stationarity. The streamflow in UGB under the nonstationary model condition is found to reduce in future. The flows are also found to be sensitive to changes in land use. Segregation results suggest that model stationarity assumption and GCMs along with their interactions with emission scenarios, act as dominant sources of uncertainty. This paper provides a generalized framework for hydrologists to examine stationarity assumption of models before considering them

  3. Representing Uncertainty on Model Analysis Plots

    Science.gov (United States)

    Smith, Trevor I.

    2016-01-01

    Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model.…

  4. Compilation of information on uncertainties involved in deposition modeling

    International Nuclear Information System (INIS)

    Lewellen, W.S.; Varma, A.K.; Sheng, Y.P.

    1985-04-01

    The current generation of dispersion models contains very simple parameterizations of deposition processes. The analysis here looks at the physical mechanisms governing these processes in an attempt to see if more valid parameterizations are available and what level of uncertainty is involved in either these simple parameterizations or any more advanced parameterization. The report is composed of three parts. The first, on dry deposition model sensitivity, provides an estimate of the uncertainty existing in current estimates of the deposition velocity due to uncertainties in independent variables such as meteorological stability, particle size, surface chemical reactivity and canopy structure. The range of uncertainty estimated for an appropriate dry deposition velocity for a plume generated by a nuclear power plant accident is three orders of magnitude. The second part discusses the uncertainties involved in precipitation scavenging rates for effluents resulting from a nuclear reactor accident. The conclusion is that major uncertainties are involved both as a result of the natural variability of the atmospheric precipitation process and due to our incomplete understanding of the underlying process. The third part involves a review of the important problems associated with modeling the interaction between the atmosphere and a forest. It gives an indication of the magnitude of the problem involved in modeling dry deposition in such environments. Separate analytics have been done for each section and are contained in the EDB

  5. Sensitivities and uncertainties of modeled ground temperatures in mountain environments

    Directory of Open Access Journals (Sweden)

    S. Gubler

    2013-08-01

    Full Text Available Model evaluation is often performed at few locations due to the lack of spatially distributed data. Since the quantification of model sensitivities and uncertainties can be performed independently from ground truth measurements, these analyses are suitable to test the influence of environmental variability on model evaluation. In this study, the sensitivities and uncertainties of a physically based mountain permafrost model are quantified within an artificial topography. The setting consists of different elevations and exposures combined with six ground types characterized by porosity and hydraulic properties. The analyses are performed for a combination of all factors, that allows for quantification of the variability of model sensitivities and uncertainties within a whole modeling domain. We found that model sensitivities and uncertainties vary strongly depending on different input factors such as topography or different soil types. The analysis shows that model evaluation performed at single locations may not be representative for the whole modeling domain. For example, the sensitivity of modeled mean annual ground temperature to ground albedo ranges between 0.5 and 4 °C depending on elevation, aspect and the ground type. South-exposed inclined locations are more sensitive to changes in ground albedo than north-exposed slopes since they receive more solar radiation. The sensitivity to ground albedo increases with decreasing elevation due to shorter duration of the snow cover. The sensitivity in the hydraulic properties changes considerably for different ground types: rock or clay, for instance, are not sensitive to uncertainties in the hydraulic properties, while for gravel or peat, accurate estimates of the hydraulic properties significantly improve modeled ground temperatures. The discretization of ground, snow and time have an impact on modeled mean annual ground temperature (MAGT that cannot be neglected (more than 1 °C for several

  6. UNCERTAINTY SUPPLY CHAIN MODEL AND TRANSPORT IN ITS DEPLOYMENTS

    Directory of Open Access Journals (Sweden)

    Fabiana Lucena Oliveira

    2014-05-01

    Full Text Available This article discusses the Model Uncertainty of Supply Chain, and proposes a matrix with their transportation modes best suited to their chains. From the detailed analysis of the matrix of uncertainty, it is suggested transportation modes best suited to the management of these chains, so that transport is the most appropriate optimization of the gains previously proposed by the original model, particularly when supply chains are distant from suppliers of raw materials and / or supplies.Here we analyze in detail Agile Supply Chains, which is a result of Uncertainty Supply Chain Model, with special attention to Manaus Industrial Center. This research was done at Manaus Industrial Pole, which is a model of industrial agglomerations, based in Manaus, State of Amazonas (Brazil, which contemplates different supply chains and strategies sharing same infrastructure of transport, handling and storage and clearance process and uses inbound for suppliers of raw material.  The state of art contemplates supply chain management, uncertainty supply chain model, agile supply chains, Manaus Industrial Center (MIC and Brazilian legislation, as a business case, and presents concepts and features, of each one. The main goal is to present and discuss how transport is able to support Uncertainty Supply Chain Model, in order to complete management model. The results obtained confirms the hypothesis of integrated logistics processes are able to guarantee attractivity for industrial agglomerations, and open discussions when the suppliers are far from the manufacturer center, in a logistics management.

  7. Uncertainty quantification in Rothermel's Model using an efficient sampling method

    Science.gov (United States)

    Edwin Jimenez; M. Yousuff Hussaini; Scott L. Goodrick

    2007-01-01

    The purpose of the present work is to quantify parametric uncertainty in Rothermel’s wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a nonlinear system of equations that relates environmental variables (input parameter...

  8. Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

    Science.gov (United States)

    Young, Cristobal; Holsteen, Katherine

    2017-01-01

    Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

  9. Impact of inherent meteorology uncertainty on air quality model predictions

    Science.gov (United States)

    It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

  10. Uncertainty management in integrated modelling, the IMAGE case

    International Nuclear Information System (INIS)

    Van der Sluijs, J.P.

    1995-01-01

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

  11. Robustness for slope stability modelling under deep uncertainty

    Science.gov (United States)

    Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

    2015-04-01

    Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.

  12. Uncertainty and Complexity in Mathematical Modeling

    Science.gov (United States)

    Cannon, Susan O.; Sanders, Mark

    2017-01-01

    Modeling is an effective tool to help students access mathematical concepts. Finding a math teacher who has not drawn a fraction bar or pie chart on the board would be difficult, as would finding students who have not been asked to draw models and represent numbers in different ways. In this article, the authors will discuss: (1) the properties of…

  13. Model Uncertainty and Exchange Rate Forecasting

    NARCIS (Netherlands)

    Kouwenberg, R.; Markiewicz, A.; Verhoeks, R.; Zwinkels, R.C.J.

    2017-01-01

    Exchange rate models with uncertain and incomplete information predict that investors focus on a small set of fundamentals that changes frequently over time. We design a model selection rule that captures the current set of fundamentals that best predicts the exchange rate. Out-of-sample tests show

  14. Evaluation of uncertainties in selected environmental dispersion models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-01-01

    Compliance with standards of radiation dose to the general public has necessitated the use of dispersion models to predict radionuclide concentrations in the environment due to releases from nuclear facilities. Because these models are only approximations of reality and because of inherent variations in the input parameters used in these models, their predictions are subject to uncertainty. Quantification of this uncertainty is necessary to assess the adequacy of these models for use in determining compliance with protection standards. This paper characterizes the capabilities of several dispersion models to predict accurately pollutant concentrations in environmental media. Three types of models are discussed: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations

  15. Immersive Data Comprehension: Visualizing Uncertainty in Measurable Models

    Directory of Open Access Journals (Sweden)

    Pere eBrunet

    2015-09-01

    Full Text Available Recent advances in 3D scanning technologies have opened new possibilities in a broad range of applications includingcultural heritage, medicine, civil engineering and urban planning. Virtual Reality systems can provide new tools toprofessionals that want to understand acquired 3D models. In this paper, we review the concept of data comprehension with an emphasis on visualization and inspection tools on immersive setups. We claim that in most application fields, data comprehension requires model measurements which in turn should be based on the explicit visualization of uncertainty. As 3D digital representations are not faithful, information on their fidelity at local level should be included in the model itself as uncertainty bounds. We propose the concept of Measurable 3D Models as digital models that explicitly encode local uncertainty bounds related to their quality. We claim that professionals and experts can strongly benefit from immersive interaction through new specific, fidelity-aware measurement tools which can facilitate 3D data comprehension. Since noise and processing errors are ubiquitous in acquired datasets, we discuss the estimation, representation and visualization of data uncertainty. We show that, based on typical user requirements in Cultural Heritage and other domains, application-oriented measuring tools in 3D models must consider uncertainty and local error bounds. We also discuss the requirements of immersive interaction tools for the comprehension of huge 3D and nD datasets acquired from real objects.

  16. Numerical Modelling of Structures with Uncertainties

    Directory of Open Access Journals (Sweden)

    Kahsin Maciej

    2017-04-01

    Full Text Available The nature of environmental interactions, as well as large dimensions and complex structure of marine offshore objects, make designing, building and operation of these objects a great challenge. This is the reason why a vast majority of investment cases of this type include structural analysis, performed using scaled laboratory models and complemented by extended computer simulations. The present paper focuses on FEM modelling of the offshore wind turbine supporting structure. Then problem is studied using the modal analysis, sensitivity analysis, as well as the design of experiment (DOE and response surface model (RSM methods. The results of modal analysis based simulations were used for assessing the quality of the FEM model against the data measured during the experimental modal analysis of the scaled laboratory model for different support conditions. The sensitivity analysis, in turn, has provided opportunities for assessing the effect of individual FEM model parameters on the dynamic response of the examined supporting structure. The DOE and RSM methods allowed to determine the effect of model parameter changes on the supporting structure response.

  17. Quantifying and Visualizing Uncertainties in Molecular Models

    OpenAIRE

    Rasheed, Muhibur; Clement, Nathan; Bhowmick, Abhishek; Bajaj, Chandrajit

    2015-01-01

    Computational molecular modeling and visualization has seen significant progress in recent years with sev- eral molecular modeling and visualization software systems in use today. Nevertheless the molecular biology community lacks techniques and tools for the rigorous analysis, quantification and visualization of the associated errors in molecular structure and its associated properties. This paper attempts at filling this vacuum with the introduction of a systematic statistical framework whe...

  18. An educational model for ensemble streamflow simulation and uncertainty analysis

    Directory of Open Access Journals (Sweden)

    A. AghaKouchak

    2013-02-01

    Full Text Available This paper presents the hands-on modeling toolbox, HBV-Ensemble, designed as a complement to theoretical hydrology lectures, to teach hydrological processes and their uncertainties. The HBV-Ensemble can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity. HBV-Ensemble was administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of uncertainty in hydrological modeling.

  19. Effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model output

    Science.gov (United States)

    Jacquin, A. P.

    2012-04-01

    This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the

  20. Enhancing uncertainty tolerance in the modelling creep of ligaments

    International Nuclear Information System (INIS)

    Taha, M M Reda; Lucero, J

    2006-01-01

    The difficulty in performing biomechanical tests and the scarcity of biomechanical experimental databases necessitate extending the current knowledge base to allow efficient modelling using limited data sets. This study suggests a framework to reduce uncertainties in biomechanical systems using limited data sets. The study also shows how sparse data and epistemic input can be exploited using fuzzy logic to represent biomechanical relations. An example application to model collagen fibre recruitment in the medial collateral ligaments during time-dependent deformation under cyclic loading (creep) is presented. The study suggests a quality metric that can be employed to observe and enhance uncertainty tolerance in the modelling process

  1. Integration of inaccurate data into model building and uncertainty assessment

    Energy Technology Data Exchange (ETDEWEB)

    Coleou, Thierry

    1998-12-31

    Model building can be seen as integrating numerous measurements and mapping through data points considered as exact. As the exact data set is usually sparse, using additional non-exact data improves the modelling and reduces the uncertainties. Several examples of non-exact data are discussed and a methodology to honor them in a single pass, along with the exact data is presented. This automatic procedure is valid for both ``base case`` model building and stochastic simulations for uncertainty analysis. 5 refs., 3 figs.

  2. Uncertainty the soul of modeling, probability & statistics

    CERN Document Server

    Briggs, William

    2016-01-01

    This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...

  3. Estimation and uncertainty of reversible Markov models.

    Science.gov (United States)

    Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank

    2015-11-07

    Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0.

  4. River meander modeling and confronting uncertainty.

    Energy Technology Data Exchange (ETDEWEB)

    Posner, Ari J. (University of Arizona Tucson, AZ)

    2011-05-01

    This study examines the meandering phenomenon as it occurs in media throughout terrestrial, glacial, atmospheric, and aquatic environments. Analysis of the minimum energy principle, along with theories of Coriolis forces (and random walks to explain the meandering phenomenon) found that these theories apply at different temporal and spatial scales. Coriolis forces might induce topological changes resulting in meandering planforms. The minimum energy principle might explain how these forces combine to limit the sinuosity to depth and width ratios that are common throughout various media. The study then compares the first order analytical solutions for flow field by Ikeda, et al. (1981) and Johannesson and Parker (1989b). Ikeda's et al. linear bank erosion model was implemented to predict the rate of bank erosion in which the bank erosion coefficient is treated as a stochastic variable that varies with physical properties of the bank (e.g., cohesiveness, stratigraphy, or vegetation density). The developed model was used to predict the evolution of meandering planforms. Then, the modeling results were analyzed and compared to the observed data. Since the migration of a meandering channel consists of downstream translation, lateral expansion, and downstream or upstream rotations several measures are formulated in order to determine which of the resulting planforms is closest to the experimental measured one. Results from the deterministic model highly depend on the calibrated erosion coefficient. Since field measurements are always limited, the stochastic model yielded more realistic predictions of meandering planform evolutions. Due to the random nature of bank erosion coefficient, the meandering planform evolution is a stochastic process that can only be accurately predicted by a stochastic model.

  5. Eigenspace perturbations for structural uncertainty estimation of turbulence closure models

    Science.gov (United States)

    Jofre, Lluis; Mishra, Aashwin; Iaccarino, Gianluca

    2017-11-01

    With the present state of computational resources, a purely numerical resolution of turbulent flows encountered in engineering applications is not viable. Consequently, investigations into turbulence rely on various degrees of modeling. Archetypal amongst these variable resolution approaches would be RANS models in two-equation closures, and subgrid-scale models in LES. However, owing to the simplifications introduced during model formulation, the fidelity of all such models is limited, and therefore the explicit quantification of the predictive uncertainty is essential. In such scenario, the ideal uncertainty estimation procedure must be agnostic to modeling resolution, methodology, and the nature or level of the model filter. The procedure should be able to give reliable prediction intervals for different Quantities of Interest, over varied flows and flow conditions, and at diametric levels of modeling resolution. In this talk, we present and substantiate the Eigenspace perturbation framework as an uncertainty estimation paradigm that meets these criteria. Commencing from a broad overview, we outline the details of this framework at different modeling resolution. Thence, using benchmark flows, along with engineering problems, the efficacy of this procedure is established. This research was partially supported by NNSA under the Predictive Science Academic Alliance Program (PSAAP) II, and by DARPA under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo).

  6. Uncertainty Analysis of Resistance Tests in Ata Nutku Ship Model Testing Laboratory of Istanbul Technical University

    Directory of Open Access Journals (Sweden)

    Cihad DELEN

    2015-12-01

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

  7. Modeling multibody systems with uncertainties. Part II: Numerical applications

    International Nuclear Information System (INIS)

    Sandu, Corina; Sandu, Adrian; Ahmadian, Mehdi

    2006-01-01

    This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects .In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties

  8. Modeling multibody systems with uncertainties. Part II: Numerical applications

    Energy Technology Data Exchange (ETDEWEB)

    Sandu, Corina, E-mail: csandu@vt.edu; Sandu, Adrian; Ahmadian, Mehdi [Virginia Polytechnic Institute and State University, Mechanical Engineering Department (United States)

    2006-04-15

    This study applies generalized polynomial chaos theory to model complex nonlinear multibody dynamic systems operating in the presence of parametric and external uncertainty. Theoretical and computational aspects of this methodology are discussed in the companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part I: Theoretical and Computational Aspects .In this paper we illustrate the methodology on selected test cases. The combined effects of parametric and forcing uncertainties are studied for a quarter car model. The uncertainty distributions in the system response in both time and frequency domains are validated against Monte-Carlo simulations. Results indicate that polynomial chaos is more efficient than Monte Carlo and more accurate than statistical linearization. The results of the direct collocation approach are similar to the ones obtained with the Galerkin approach. A stochastic terrain model is constructed using a truncated Karhunen-Loeve expansion. The application of polynomial chaos to differential-algebraic systems is illustrated using the constrained pendulum problem. Limitations of the polynomial chaos approach are studied on two different test problems, one with multiple attractor points, and the second with a chaotic evolution and a nonlinear attractor set. The overall conclusion is that, despite its limitations, generalized polynomial chaos is a powerful approach for the simulation of multibody dynamic systems with uncertainties.

  9. Uncertainty propagation through dynamic models of assemblies of mechanical structures

    International Nuclear Information System (INIS)

    Daouk, Sami

    2016-01-01

    When studying the behaviour of mechanical systems, mathematical models and structural parameters are usually considered deterministic. Return on experience shows however that these elements are uncertain in most cases, due to natural variability or lack of knowledge. Therefore, quantifying the quality and reliability of the numerical model of an industrial assembly remains a major question in low-frequency dynamics. The purpose of this thesis is to improve the vibratory design of bolted assemblies through setting up a dynamic connector model that takes account of different types and sources of uncertainty on stiffness parameters, in a simple, efficient and exploitable in industrial context. This work has been carried out in the framework of the SICODYN project, led by EDF R and D, that aims to characterise and quantify, numerically and experimentally, the uncertainties in the dynamic behaviour of bolted industrial assemblies. Comparative studies of several numerical methods of uncertainty propagation demonstrate the advantage of using the Lack-Of-Knowledge theory. An experimental characterisation of uncertainties in bolted structures is performed on a dynamic test rig and on an industrial assembly. The propagation of many small and large uncertainties through different dynamic models of mechanical assemblies leads to the assessment of the efficiency of the Lack-Of-Knowledge theory and its applicability in an industrial environment. (author)

  10. Can agent based models effectively reduce fisheries management implementation uncertainty?

    Science.gov (United States)

    Drexler, M.

    2016-02-01

    Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

  11. Evaluation of Spatial Uncertainties In Modeling of Cadastral Systems

    Science.gov (United States)

    Fathi, Morteza; Teymurian, Farideh

    2013-04-01

    Cadastre plays an essential role in sustainable development especially in developing countries like Iran. A well-developed Cadastre results in transparency of estates tax system, transparency of data of estate, reduction of action before the courts and effective management of estates and natural sources and environment. Multipurpose Cadastre through gathering of other related data has a vital role in civil, economic and social programs and projects. Iran is being performed Cadastre for many years but success in this program is subject to correct geometric and descriptive data of estates. Since there are various sources of data with different accuracy and precision in Iran, some difficulties and uncertainties are existed in modeling of geometric part of Cadastre such as inconsistency between data in deeds and Cadastral map which cause some troubles in execution of cadastre and result in losing national and natural source, rights of nation. Now there is no uniform and effective technical method for resolving such conflicts. This article describes various aspects of such conflicts in geometric part of cadastre and suggests a solution through some modeling tools of GIS.

  12. Identifying influences on model uncertainty: an application using a forest carbon budget model

    Science.gov (United States)

    James E. Smith; Linda S. Heath

    2001-01-01

    Uncertainty is an important consideration for both developers and users of environmental simulation models. Establishing quantitative estimates of uncertainty for deterministic models can be difficult when the underlying bases for such information are scarce. We demonstrate an application of probabilistic uncertainty analysis that provides for refinements in...

  13. Estimation of a multivariate mean under model selection uncertainty

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2014-05-01

    Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty.  When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.

  14. Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1).

    Science.gov (United States)

    Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan; Gernaey, Krist V

    2009-01-01

    The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (S(NH)) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (S(NO)) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (micro(A)) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. eta(g) (anoxic growth rate correction factor) and eta(h) (anoxic hydrolysis rate correction factor), becomes less important when a S(NO) controller manipulating an external carbon source addition is implemented.

  15. A possibilistic uncertainty model in classical reliability theory

    International Nuclear Information System (INIS)

    De Cooman, G.; Capelle, B.

    1994-01-01

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

  16. Wind energy: Overcoming inadequate wind and modeling uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Kane, Vivek

    2010-09-15

    'Green Energy' is the call of the day, and significance of Wind Energy can never be overemphasized. But the key question here is - What if the wind resources are inadequate? Studies reveal that the probability of finding favorable wind at a given place on land is only 15%. Moreover, there are inherent uncertainties associated with wind business. Can we overcome inadequate wind resources? Can we scientifically quantify uncertainty and model it to make business sense? This paper proposes a solution, by way of break-through Wind Technologies, combined with advanced tools for Financial Modeling, enabling vital business decisions.

  17. Dealing with uncertainty in modeling intermittent water supply

    Science.gov (United States)

    Lieb, A. M.; Rycroft, C.; Wilkening, J.

    2015-12-01

    Intermittency in urban water supply affects hundreds of millions of people in cities around the world, impacting water quality and infrastructure. Building on previous work to dynamically model the transient flows in water distribution networks undergoing frequent filling and emptying, we now consider the hydraulic implications of uncertain input data. Water distribution networks undergoing intermittent supply are often poorly mapped, and household metering frequently ranges from patchy to nonexistent. In the face of uncertain pipe material, pipe slope, network connectivity, and outflow, we investigate how uncertainty affects dynamical modeling results. We furthermore identify which parameters exert the greatest influence on uncertainty, helping to prioritize data collection.

  18. Chemical kinetic model uncertainty minimization through laminar flame speed measurements

    Science.gov (United States)

    Park, Okjoo; Veloo, Peter S.; Sheen, David A.; Tao, Yujie; Egolfopoulos, Fokion N.; Wang, Hai

    2016-01-01

    Laminar flame speed measurements were carried for mixture of air with eight C3-4 hydrocarbons (propene, propane, 1,3-butadiene, 1-butene, 2-butene, iso-butene, n-butane, and iso-butane) at the room temperature and ambient pressure. Along with C1-2 hydrocarbon data reported in a recent study, the entire dataset was used to demonstrate how laminar flame speed data can be utilized to explore and minimize the uncertainties in a reaction model for foundation fuels. The USC Mech II kinetic model was chosen as a case study. The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (D.A. Sheen and H. Wang, Combust. Flame 2011, 158, 2358–2374) was employed to constrain the model uncertainty for laminar flame speed predictions. Results demonstrate that a reaction model constrained only by the laminar flame speed values of methane/air flames notably reduces the uncertainty in the predictions of the laminar flame speeds of C3 and C4 alkanes, because the key chemical pathways of all of these flames are similar to each other. The uncertainty in model predictions for flames of unsaturated C3-4 hydrocarbons remain significant without considering fuel specific laminar flames speeds in the constraining target data set, because the secondary rate controlling reaction steps are different from those in the saturated alkanes. It is shown that the constraints provided by the laminar flame speeds of the foundation fuels could reduce notably the uncertainties in the predictions of laminar flame speeds of C4 alcohol/air mixtures. Furthermore, it is demonstrated that an accurate prediction of the laminar flame speed of a particular C4 alcohol/air mixture is better achieved through measurements for key molecular intermediates formed during the pyrolysis and oxidation of the parent fuel. PMID:27890938

  19. Linear models in the mathematics of uncertainty

    CERN Document Server

    Mordeson, John N; Clark, Terry D; Pham, Alex; Redmond, Michael A

    2013-01-01

    The purpose of this book is to present new mathematical techniques for modeling global issues. These mathematical techniques are used to determine linear equations between a dependent variable and one or more independent variables in cases where standard techniques such as linear regression are not suitable. In this book, we examine cases where the number of data points is small (effects of nuclear warfare), where the experiment is not repeatable (the breakup of the former Soviet Union), and where the data is derived from expert opinion (how conservative is a political party). In all these cases the data  is difficult to measure and an assumption of randomness and/or statistical validity is questionable.  We apply our methods to real world issues in international relations such as  nuclear deterrence, smart power, and cooperative threat reduction. We next apply our methods to issues in comparative politics such as successful democratization, quality of life, economic freedom, political stability, and fail...

  20. Uncertainty Quantification for Large-Scale Ice Sheet Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ghattas, Omar [Univ. of Texas, Austin, TX (United States)

    2016-02-05

    This report summarizes our work to develop advanced forward and inverse solvers and uncertainty quantification capabilities for a nonlinear 3D full Stokes continental-scale ice sheet flow model. The components include: (1) forward solver: a new state-of-the-art parallel adaptive scalable high-order-accurate mass-conservative Newton-based 3D nonlinear full Stokes ice sheet flow simulator; (2) inverse solver: a new adjoint-based inexact Newton method for solution of deterministic inverse problems governed by the above 3D nonlinear full Stokes ice flow model; and (3) uncertainty quantification: a novel Hessian-based Bayesian method for quantifying uncertainties in the inverse ice sheet flow solution and propagating them forward into predictions of quantities of interest such as ice mass flux to the ocean.

  1. Sensitivity of wildlife habitat models to uncertainties in GIS data

    Science.gov (United States)

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

    1992-01-01

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

  2. Decomposing the uncertainty in climate impact projections of Dynamic Vegetation Models: a test with the forest models LANDCLIM and FORCLIM

    Science.gov (United States)

    Cailleret, Maxime; Snell, Rebecca; von Waldow, Harald; Kotlarski, Sven; Bugmann, Harald

    2015-04-01

    Different levels of uncertainty should be considered in climate impact projections by Dynamic Vegetation Models (DVMs), particularly when it comes to managing climate risks. Such information is useful to detect the key processes and uncertainties in the climate model - impact model chain and may be used to support recommendations for future improvements in the simulation of both climate and biological systems. In addition, determining which uncertainty source is dominant is an important aspect to recognize the limitations of climate impact projections by a multi-model ensemble mean approach. However, to date, few studies have clarified how each uncertainty source (baseline climate data, greenhouse gas emission scenario, climate model, and DVM) affects the projection of ecosystem properties. Focusing on one greenhouse gas emission scenario, we assessed the uncertainty in the projections of a forest landscape model (LANDCLIM) and a stand-scale forest gap model (FORCLIM) that is caused by linking climate data with an impact model. LANDCLIM was used to assess the uncertainty in future landscape properties of the Visp valley in Switzerland that is due to (i) the use of different 'baseline' climate data (gridded data vs. data from weather stations), and (ii) differences in climate projections among 10 GCM-RCM chains. This latter point was also considered for the projections of future forest properties by FORCLIM at several sites along an environmental gradient in Switzerland (14 GCM-RCM chains), for which we also quantified the uncertainty caused by (iii) the model chain specific statistical properties of the climate time-series, and (iv) the stochasticity of the demographic processes included in the model, e.g., the annual number of saplings that establish, or tree mortality. Using methods of variance decomposition analysis, we found that (i) The use of different baseline climate data strongly impacts the prediction of forest properties at the lowest and highest, but

  3. Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

    Science.gov (United States)

    Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.

    2014-01-01

    Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

  4. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

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

    2001-01-01

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

  5. Bayesian uncertainty quantification in linear models for diffusion MRI.

    Science.gov (United States)

    Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans

    2018-03-29

    Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Modeling Multiple Causes of Carcinogenesis

    Energy Technology Data Exchange (ETDEWEB)

    Jones, T D

    1999-01-24

    An array of epidemiological results and databases on test animal indicate that risk of cancer and atherosclerosis can be up- or down-regulated by diet through a range of 200%. Other factors contribute incrementally and include the natural terrestrial environment and various human activities that jointly produce complex exposures to endotoxin-producing microorganisms, ionizing radiations, and chemicals. Ordinary personal habits and simple physical irritants have been demonstrated to affect the immune response and risk of disease. There tends to be poor statistical correlation of long-term risk with single agent exposures incurred throughout working careers. However, Agency recommendations for control of hazardous exposures to humans has been substance-specific instead of contextually realistic even though there is consistent evidence for common mechanisms of toxicological and carcinogenic action. That behavior seems to be best explained by molecular stresses from cellular oxygen metabolism and phagocytosis of antigenic invasion as well as breakdown of normal metabolic compounds associated with homeostatic- and injury-related renewal of cells. There is continually mounting evidence that marrow stroma, comprised largely of monocyte-macrophages and fibroblasts, is important to phagocytic and cytokinetic response, but the complex action of the immune process is difficult to infer from first-principle logic or biomarkers of toxic injury. The many diverse database studies all seem to implicate two important processes, i.e., the univalent reduction of molecular oxygen and breakdown of aginuine, an amino acid, by hydrolysis or digestion of protein which is attendant to normal antigen-antibody action. This behavior indicates that protection guidelines and risk coefficients should be context dependent to include reference considerations of the composite action of parameters that mediate oxygen metabolism. A logic of this type permits the realistic common-scale modeling of

  7. Uncertainty Quantification in Control Problems for Flocking Models

    Directory of Open Access Journals (Sweden)

    Giacomo Albi

    2015-01-01

    Full Text Available The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

  8. Reducing uncertainty based on model fitness: Application to a ...

    African Journals Online (AJOL)

    A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially ... The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model.

  9. Geostatistical modeling of groundwater properties and assessment of their uncertainties

    International Nuclear Information System (INIS)

    Honda, Makoto; Yamamoto, Shinya; Sakurai, Hideyuki; Suzuki, Makoto; Sanada, Hiroyuki; Matsui, Hiroya; Sugita, Yutaka

    2010-01-01

    The distribution of groundwater properties is important for understanding of the deep underground hydrogeological environments. This paper proposes a geostatistical system for modeling the groundwater properties which have a correlation with the ground resistivity data obtained from widespread and exhaustive survey. That is, the methodology for the integration of resistivity data measured by various methods and the methodology for modeling the groundwater properties using the integrated resistivity data has been developed. The proposed system has also been validated using the data obtained in the Horonobe Underground Research Laboratory project. Additionally, the quantification of uncertainties in the estimated model has been tried by numerical simulations based on the data. As a result, the uncertainties of the proposal model have been estimated lower than other traditional model's. (author)

  10. Evaluating the uncertainty of input quantities in measurement models

    Science.gov (United States)

    Possolo, Antonio; Elster, Clemens

    2014-06-01

    The Guide to the Expression of Uncertainty in Measurement (GUM) gives guidance about how values and uncertainties should be assigned to the input quantities that appear in measurement models. This contribution offers a concrete proposal for how that guidance may be updated in light of the advances in the evaluation and expression of measurement uncertainty that were made in the course of the twenty years that have elapsed since the publication of the GUM, and also considering situations that the GUM does not yet contemplate. Our motivation is the ongoing conversation about a new edition of the GUM. While generally we favour a Bayesian approach to uncertainty evaluation, we also recognize the value that other approaches may bring to the problems considered here, and focus on methods for uncertainty evaluation and propagation that are widely applicable, including to cases that the GUM has not yet addressed. In addition to Bayesian methods, we discuss maximum-likelihood estimation, robust statistical methods, and measurement models where values of nominal properties play the same role that input quantities play in traditional models. We illustrate these general-purpose techniques in concrete examples, employing data sets that are realistic but that also are of conveniently small sizes. The supplementary material available online lists the R computer code that we have used to produce these examples (stacks.iop.org/Met/51/3/339/mmedia). Although we strive to stay close to clause 4 of the GUM, which addresses the evaluation of uncertainty for input quantities, we depart from it as we review the classes of measurement models that we believe are generally useful in contemporary measurement science. We also considerably expand and update the treatment that the GUM gives to Type B evaluations of uncertainty: reviewing the state-of-the-art, disciplined approach to the elicitation of expert knowledge, and its encapsulation in probability distributions that are usable in

  11. Nuclear Physical Uncertainties in Modeling X-Ray Bursts

    Science.gov (United States)

    Regis, Eric; Amthor, A. Matthew

    2017-09-01

    Type I x-ray bursts occur when a neutron star accretes material from the surface of another star in a compact binary star system. For certain accretion rates and material compositions, much of the nuclear material is burned in short, explosive bursts. Using a one-dimensional stellar model, Kepler, and a comprehensive nuclear reaction rate library, ReacLib, we have simulated chains of type I x-ray bursts. Unfortunately, there are large remaining uncertainties in the nuclear reaction rates involved, since many of the isotopes reacting are unstable and have not yet been studied experimentally. Some individual reactions, when varied within their estimated uncertainty, alter the light curves dramatically. This limits our ability to understand the structure of the neutron star. Previous studies have looked at the effects of individual reaction rate uncertainties. We have applied a Monte Carlo method ``-simultaneously varying a set of reaction rates'' -in order to probe the expected uncertainty in x-ray burst behaviour due to the total uncertainty in all nuclear reaction rates. Furthermore, we aim to discover any nonlinear effects due to the coupling between different reaction rates. Early results show clear non-linear effects. This research was made possible by NSF-DUE Grant 1317446, BUScholars Program.

  12. Robust nonlinear control of nuclear reactors under model uncertainty

    International Nuclear Information System (INIS)

    Park, Moon Ghu

    1993-02-01

    A nonlinear model-based control method is developed for the robust control of a nuclear reactor. The nonlinear plant model is used to design a unique control law which covers a wide operating range. The robustness is a crucial factor for the fully automatic control of reactor power due to time-varying, uncertain parameters, and state estimation error, or unmodeled dynamics. A variable structure control (VSC) method is introduced which consists of an adaptive performance specification (fime control) after the tracking error reaches the narrow boundary-layer by a time-optimal control (coarse control). Variable structure control is a powerful method for nonlinear system controller design which has inherent robustness to parameter variations or external disturbances using the known uncertainty bounds, and it requires very low computational efforts. In spite of its desirable properties, conventional VSC presents several important drawbacks that limit its practical applicability. One of the most undesirable phenomena is chattering, which implies extremely high control activity and may excite high-frequency unmodeled dynamics. This problem is due to the neglected actuator time-delay or sampling effects. The problem was partially remedied by replacing chattering control by a smooth control inter-polation in a boundary layer neighnboring a time-varying sliding surface. But, for the nuclear reactor systems which has very fast dynamic response, the sampling effect may destroy the narrow boundary layer when a large uncertainty bound is used. Due to the very short neutron life time, large uncertainty bound leads to the high gain in feedback control. To resolve this problem, a derivative feedback is introduced that gives excellent performance by reducing the uncertainty bound. The stability of tracking error dynamics is guaranteed by the second method of Lyapunov using the two-level uncertainty bounds that are obtained from the knowledge of uncertainty bound and the estimated

  13. Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

    Science.gov (United States)

    Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

    2012-11-01

    Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

  14. Bayesian uncertainty analysis with applications to turbulence modeling

    International Nuclear Information System (INIS)

    Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E.; Prudhomme, Serge; Moser, Robert D.

    2011-01-01

    In this paper, we apply Bayesian uncertainty quantification techniques to the processes of calibrating complex mathematical models and predicting quantities of interest (QoI's) with such models. These techniques also enable the systematic comparison of competing model classes. The processes of calibration and comparison constitute the building blocks of a larger validation process, the goal of which is to accept or reject a given mathematical model for the prediction of a particular QoI for a particular scenario. In this work, we take the first step in this process by applying the methodology to the analysis of the Spalart-Allmaras turbulence model in the context of incompressible, boundary layer flows. Three competing model classes based on the Spalart-Allmaras model are formulated, calibrated against experimental data, and used to issue a prediction with quantified uncertainty. The model classes are compared in terms of their posterior probabilities and their prediction of QoI's. The model posterior probability represents the relative plausibility of a model class given the data. Thus, it incorporates the model's ability to fit experimental observations. Alternatively, comparing models using the predicted QoI connects the process to the needs of decision makers that use the results of the model. We show that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges.

  15. A simplified model of choice behavior under uncertainty

    Directory of Open Access Journals (Sweden)

    Ching-Hung Lin

    2016-08-01

    Full Text Available The Iowa Gambling Task (IGT has been standardized as a clinical assessment tool (Bechara, 2007. Nonetheless, numerous research groups have attempted to modify IGT models to optimize parameters for predicting the choice behavior of normal controls and patients. A decade ago, most researchers considered the expected utility (EU model (Busemeyer and Stout, 2002 to be the optimal model for predicting choice behavior under uncertainty. However, in recent years, studies have demonstrated the prospect utility (PU models (Ahn et al., 2008 to be more effective than the EU models in the IGT. Nevertheless, after some preliminary tests, we propose that Ahn et al. (2008 PU model is not optimal due to some incompatible results between our behavioral and modeling data. This study aims to modify Ahn et al. (2008 PU model to a simplified model and collected 145 subjects’ IGT performance as the benchmark data for comparison. In our simplified PU model, the best goodness-of-fit was found mostly while α approaching zero. More specifically, we retested the key parameters α, λ , and A in the PU model. Notably, the power of influence of the parameters α, λ, and A has a hierarchical order in terms of manipulating the goodness-of-fit in the PU model. Additionally, we found that the parameters λ and A may be ineffective when the parameter α is close to zero in the PU model. The present simplified model demonstrated that decision makers mostly adopted the strategy of gain-stay-loss-shift rather than foreseeing the long-term outcome. However, there still have other behavioral variables that are not well revealed under these dynamic uncertainty situations. Therefore, the optimal behavioral models may not have been found. In short, the best model for predicting choice behavior under dynamic-uncertainty situations should be further evaluated.

  16. Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

    Science.gov (United States)

    Tyler Jon Smith; Lucy Amanda Marshall

    2010-01-01

    Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are...

  17. Numerical solution of dynamic equilibrium models under Poisson uncertainty

    DEFF Research Database (Denmark)

    Posch, Olaf; Trimborn, Timo

    2013-01-01

    We propose a simple and powerful numerical algorithm to compute the transition process in continuous-time dynamic equilibrium models with rare events. In this paper we transform the dynamic system of stochastic differential equations into a system of functional differential equations of the retar...... solution to Lucas' endogenous growth model under Poisson uncertainty are used to compute the exact numerical error. We show how (potential) catastrophic events such as rare natural disasters substantially affect the economic decisions of households....

  18. Formal modeling of a system of chemical reactions under uncertainty.

    Science.gov (United States)

    Ghosh, Krishnendu; Schlipf, John

    2014-10-01

    We describe a novel formalism representing a system of chemical reactions, with imprecise rates of reactions and concentrations of chemicals, and describe a model reduction method, pruning, based on the chemical properties. We present two algorithms, midpoint approximation and interval approximation, for construction of efficient model abstractions with uncertainty in data. We evaluate computational feasibility by posing queries in computation tree logic (CTL) on a prototype of extracellular-signal-regulated kinase (ERK) pathway.

  19. Approximating prediction uncertainty for random forest regression models

    Science.gov (United States)

    John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

    2016-01-01

    Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

  20. Verification and Uncertainty Reduction of Amchitka Underground Nuclear Testing Models

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed Hassan; Jenny Chapman

    2006-02-01

    The modeling of Amchitka underground nuclear tests conducted in 2002 is verified and uncertainty in model input parameters, as well as predictions, has been reduced using newly collected data obtained by the summer 2004 field expedition of CRESP. Newly collected data that pertain to the groundwater model include magnetotelluric (MT) surveys conducted on the island to determine the subsurface salinity and porosity structure of the subsurface, and bathymetric surveys to determine the bathymetric maps of the areas offshore from the Long Shot and Cannikin Sites. Analysis and interpretation of the MT data yielded information on the location of the transition zone, and porosity profiles showing porosity values decaying with depth. These new data sets are used to verify the original model in terms of model parameters, model structure, and model output verification. In addition, by using the new data along with the existing data (chemistry and head data), the uncertainty in model input and output is decreased by conditioning on all the available data. A Markov Chain Monte Carlo (MCMC) approach is adapted for developing new input parameter distributions conditioned on prior knowledge and new data. The MCMC approach is a form of Bayesian conditioning that is constructed in such a way that it produces samples of the model parameters that eventually converge to a stationary posterior distribution. The Bayesian MCMC approach enhances probabilistic assessment. Instead of simply propagating uncertainty forward from input parameters into model predictions (i.e., traditional Monte Carlo approach), MCMC propagates uncertainty backward from data onto parameters, and then forward from parameters into predictions. Comparisons between new data and the original model, and conditioning on all available data using MCMC method, yield the following results and conclusions: (1) Model structure is verified at Long Shot and Cannikin where the high-resolution bathymetric data collected by CRESP

  1. Evaluation of Uncertainties in hydrogeological modeling and groundwater flow analyses. Model calibration

    International Nuclear Information System (INIS)

    Ijiri, Yuji; Ono, Makoto; Sugihara, Yutaka; Shimo, Michito; Yamamoto, Hajime; Fumimura, Kenichi

    2003-03-01

    This study involves evaluation of uncertainty in hydrogeological modeling and groundwater flow analysis. Three-dimensional groundwater flow in Shobasama site in Tono was analyzed using two continuum models and one discontinuous model. The domain of this study covered area of four kilometers in east-west direction and six kilometers in north-south direction. Moreover, for the purpose of evaluating how uncertainties included in modeling of hydrogeological structure and results of groundwater simulation decreased with progress of investigation research, updating and calibration of the models about several modeling techniques of hydrogeological structure and groundwater flow analysis techniques were carried out, based on the information and knowledge which were newly acquired. The acquired knowledge is as follows. As a result of setting parameters and structures in renewal of the models following to the circumstances by last year, there is no big difference to handling between modeling methods. The model calibration is performed by the method of matching numerical simulation with observation, about the pressure response caused by opening and closing of a packer in MIU-2 borehole. Each analysis technique attains reducing of residual sum of squares of observations and results of numerical simulation by adjusting hydrogeological parameters. However, each model adjusts different parameters as water conductivity, effective porosity, specific storage, and anisotropy. When calibrating models, sometimes it is impossible to explain the phenomena only by adjusting parameters. In such case, another investigation may be required to clarify details of hydrogeological structure more. As a result of comparing research from beginning to this year, the following conclusions are obtained about investigation. (1) The transient hydraulic data are effective means in reducing the uncertainty of hydrogeological structure. (2) Effective porosity for calculating pore water velocity of

  2. Statistical approach for uncertainty quantification of experimental modal model parameters

    DEFF Research Database (Denmark)

    Luczak, M.; Peeters, B.; Kahsin, M.

    2014-01-01

    Composite materials are widely used in manufacture of aerospace and wind energy structural components. These load carrying structures are subjected to dynamic time-varying loading conditions. Robust structural dynamics identification procedure impose tight constraints on the quality of modal models...... represent different complexity levels ranging from coupon, through sub-component up to fully assembled aerospace and wind energy structural components made of composite materials. The proposed method is demonstrated on two application cases of a small and large wind turbine blade........ This paper aims at a systematic approach for uncertainty quantification of the parameters of the modal models estimated from experimentally obtained data. Statistical analysis of modal parameters is implemented to derive an assessment of the entire modal model uncertainty measure. Investigated structures...

  3. Another two dark energy models motivated from Karolyhazy uncertainty relation

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Cheng-Yi; Yang, Wen-Li; Song, Yu. [Northwest University, Institute of Modern Physics, Xian (China); Yue, Rui-Hong [Ningbo University, Faculty of Science, Ningbo (China)

    2012-03-15

    The Karolyhazy uncertainty relation indicates that there exists a minimal detectable cell {delta}t{sup 3} over the region t{sup 3} in Minkowski space-time. Due to the energy-time uncertainty relation, the energy of the cell {delta}t {sup 3} cannot be less {delta}t{sup -1}. Then we get a new energy density of metric fluctuations of Minkowski spacetime as {delta}t{sup -4}. Motivated by the energy density, we propose two new dark-energy models. One model is characterized by the age of the universe and the other is characterized by the conformal age of the universe. We find that in the two models, the dark energy mimics a cosmological constant in the late time. (orig.)

  4. Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations

    Directory of Open Access Journals (Sweden)

    Jong Seok Lee

    2018-03-01

    Full Text Available The purpose of this paper is to compare the degree of uncertainty of the water scarcity footprint using the Monte Carlo statistical method and block bootstrap method. Using the hydrological data of a water drainage basin in Korea, characterization factors based on the available water remaining (AWARE model were obtained. The uncertainties of the water scarcity footprint considering temporal variations in paddy rice production in Korea were estimated. The block bootstrap method gave five-times smaller percentage uncertainty values of the model output compared to that of the two different Monte Carlo statistical method scenarios. Incorrect estimation of the probability distribution of the AWARE characterization factor model is what causes the higher uncertainty in the water scarcity footprint value calculated by the Monte Carlo statistical method in this study. This is because AWARE characterization factor values partly follows discrete distribution with extreme value on one side. Therefore, this study suggests that the block bootstrap method is a better choice in analyzing uncertainty compared to the Monte Carlo statistical method when using the AWARE model to quantify the water scarcity footprint.

  5. Effects of input uncertainty on cross-scale crop modeling

    Science.gov (United States)

    Waha, Katharina; Huth, Neil; Carberry, Peter

    2014-05-01

    The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input

  6. Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates

    DEFF Research Database (Denmark)

    Murcia Leon, Juan Pablo; Réthoré, Pierre-Elouan; Dimitrov, Nikolay Krasimirov

    2018-01-01

    of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces......Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating......-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertainty models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation...

  7. Sensitivity of modeled ozone concentrations to uncertainties in biogenic emissions

    International Nuclear Information System (INIS)

    Roselle, S.J.

    1992-06-01

    The study examines the sensitivity of regional ozone (O3) modeling to uncertainties in biogenic emissions estimates. The United States Environmental Protection Agency's (EPA) Regional Oxidant Model (ROM) was used to simulate the photochemistry of the northeastern United States for the period July 2-17, 1988. An operational model evaluation showed that ROM had a tendency to underpredict O3 when observed concentrations were above 70-80 ppb and to overpredict O3 when observed values were below this level. On average, the model underpredicted daily maximum O3 by 14 ppb. Spatial patterns of O3, however, were reproduced favorably by the model. Several simulations were performed to analyze the effects of uncertainties in biogenic emissions on predicted O3 and to study the effectiveness of two strategies of controlling anthropogenic emissions for reducing high O3 concentrations. Biogenic hydrocarbon emissions were adjusted by a factor of 3 to account for the existing range of uncertainty in these emissions. The impact of biogenic emission uncertainties on O3 predictions depended upon the availability of NOx. In some extremely NOx-limited areas, increasing the amount of biogenic emissions decreased O3 concentrations. Two control strategies were compared in the simulations: (1) reduced anthropogenic hydrocarbon emissions, and (2) reduced anthropogenic hydrocarbon and NOx emissions. The simulations showed that hydrocarbon emission controls were more beneficial to the New York City area, but that combined NOx and hydrocarbon controls were more beneficial to other areas of the Northeast. Hydrocarbon controls were more effective as biogenic hydrocarbon emissions were reduced, whereas combined NOx and hydrocarbon controls were more effective as biogenic hydrocarbon emissions were increased

  8. Model Uncertainties for Valencia RPA Effect for MINERvA

    Energy Technology Data Exchange (ETDEWEB)

    Gran, Richard [Univ. of Minnesota, Duluth, MN (United States)

    2017-05-08

    This technical note describes the application of the Valencia RPA multi-nucleon effect and its uncertainty to QE reactions from the GENIE neutrino event generator. The analysis of MINERvA neutrino data in Rodrigues et al. PRL 116 071802 (2016) paper makes clear the need for an RPA suppression, especially at very low momentum and energy transfer. That published analysis does not constrain the magnitude of the effect; it only tests models with and without the effect against the data. Other MINERvA analyses need an expression of the model uncertainty in the RPA effect. A well-described uncertainty can be used for systematics for unfolding, for model errors in the analysis of non-QE samples, and as input for fitting exercises for model testing or constraining backgrounds. This prescription takes uncertainties on the parameters in the Valencia RPA model and adds a (not-as-tight) constraint from muon capture data. For MINERvA we apply it as a 2D ($q_0$,$q_3$) weight to GENIE events, in lieu of generating a full beyond-Fermi-gas quasielastic events. Because it is a weight, it can be applied to the generated and fully Geant4 simulated events used in analysis without a special GENIE sample. For some limited uses, it could be cast as a 1D $Q^2$ weight without much trouble. This procedure is a suitable starting point for NOvA and DUNE where the energy dependence is modest, but probably not adequate for T2K or MicroBooNE.

  9. Uncertainty propagation in a multiscale model of nanocrystalline plasticity

    International Nuclear Information System (INIS)

    Koslowski, M.; Strachan, Alejandro

    2011-01-01

    We characterize how uncertainties propagate across spatial and temporal scales in a physics-based model of nanocrystalline plasticity of fcc metals. Our model combines molecular dynamics (MD) simulations to characterize atomic-level processes that govern dislocation-based-plastic deformation with a phase field approach to dislocation dynamics (PFDD) that describes how an ensemble of dislocations evolve and interact to determine the mechanical response of the material. We apply this approach to a nanocrystalline Ni specimen of interest in micro-electromechanical (MEMS) switches. Our approach enables us to quantify how internal stresses that result from the fabrication process affect the properties of dislocations (using MD) and how these properties, in turn, affect the yield stress of the metallic membrane (using the PFMM model). Our predictions show that, for a nanocrystalline sample with small grain size (4 nm), a variation in residual stress of 20 MPa (typical in today's microfabrication techniques) would result in a variation on the critical resolved shear yield stress of approximately 15 MPa, a very small fraction of the nominal value of approximately 9 GPa. - Highlights: → Quantify how fabrication uncertainties affect yield stress in a microswitch component. → Propagate uncertainties in a multiscale model of single crystal plasticity. → Molecular dynamics quantifies how fabrication variations affect dislocations. → Dislocation dynamics relate variations in dislocation properties to yield stress.

  10. Uncertainty Model for Total Solar Irradiance Estimation on Australian Rooftops

    Science.gov (United States)

    Al-Saadi, Hassan; Zivanovic, Rastko; Al-Sarawi, Said

    2017-11-01

    The installations of solar panels on Australian rooftops have been in rise for the last few years, especially in the urban areas. This motivates academic researchers, distribution network operators and engineers to accurately address the level of uncertainty resulting from grid-connected solar panels. The main source of uncertainty is the intermittent nature of radiation, therefore, this paper presents a new model to estimate the total radiation incident on a tilted solar panel. Where a probability distribution factorizes clearness index, the model is driven upon clearness index with special attention being paid for Australia with the utilization of best-fit-correlation for diffuse fraction. The assessment of the model validity is achieved with the adoption of four goodness-of-fit techniques. In addition, the Quasi Monte Carlo and sparse grid methods are used as sampling and uncertainty computation tools, respectively. High resolution data resolution of solar irradiations for Adelaide city were used for this assessment, with an outcome indicating a satisfactory agreement between actual data variation and model.

  11. 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

  12. Uncertainty modeling of CCS investment strategy in China's power sector

    International Nuclear Information System (INIS)

    Zhou, Wenji; Zhu, Bing; Fuss, Sabine; Szolgayova, Jana; Obersteiner, Michael; Fei, Weiyang

    2010-01-01

    The increasing pressure resulting from the need for CO 2 mitigation is in conflict with the predominance of coal in China's energy structure. A possible solution to this tension between climate change and fossil fuel consumption fact could be the introduction of the carbon capture and storage (CCS) technology. However, high cost and other problems give rise to great uncertainty in R and D and popularization of carbon capture technology. This paper presents a real options model incorporating policy uncertainty described by carbon price scenarios (including stochasticity), allowing for possible technological change. This model is further used to determine the best strategy for investing in CCS technology in an uncertain environment in China and the effect of climate policy on the decision-making process of investment into carbon-saving technologies.

  13. Antineutrinos from Earth: A reference model and its uncertainties

    International Nuclear Information System (INIS)

    Mantovani, Fabio; Carmignani, Luigi; Fiorentini, Gianni; Lissia, Marcello

    2004-01-01

    We predict geoneutrino fluxes in a reference model based on a detailed description of Earth's crust and mantle and using the best available information on the abundances of uranium, thorium, and potassium inside Earth's layers. We estimate the uncertainties of fluxes corresponding to the uncertainties of the element abundances. In addition to distance integrated fluxes, we also provide the differential fluxes as a function of distance from several sites of experimental interest. Event yields at several locations are estimated and their dependence on the neutrino oscillation parameters is discussed. At Kamioka we predict N(U+Th)=35±6 events for 10 32 proton yr and 100% efficiency assuming sin 2 (2θ)=0.863 and δm 2 =7.3x10 -5 eV 2 . The maximal prediction is 55 events, obtained in a model with fully radiogenic production of the terrestrial heat flow

  14. Hydrological model uncertainty due to spatial evapotranspiration estimation methods

    Science.gov (United States)

    Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

    2016-05-01

    Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

  15. Discriminative Random Field Models for Subsurface Contamination Uncertainty Quantification

    Science.gov (United States)

    Arshadi, M.; Abriola, L. M.; Miller, E. L.; De Paolis Kaluza, C.

    2017-12-01

    Application of flow and transport simulators for prediction of the release, entrapment, and persistence of dense non-aqueous phase liquids (DNAPLs) and associated contaminant plumes is a computationally intensive process that requires specification of a large number of material properties and hydrologic/chemical parameters. Given its computational burden, this direct simulation approach is particularly ill-suited for quantifying both the expected performance and uncertainty associated with candidate remediation strategies under real field conditions. Prediction uncertainties primarily arise from limited information about contaminant mass distributions, as well as the spatial distribution of subsurface hydrologic properties. Application of direct simulation to quantify uncertainty would, thus, typically require simulating multiphase flow and transport for a large number of permeability and release scenarios to collect statistics associated with remedial effectiveness, a computationally prohibitive process. The primary objective of this work is to develop and demonstrate a methodology that employs measured field data to produce equi-probable stochastic representations of a subsurface source zone that capture the spatial distribution and uncertainty associated with key features that control remediation performance (i.e., permeability and contamination mass). Here we employ probabilistic models known as discriminative random fields (DRFs) to synthesize stochastic realizations of initial mass distributions consistent with known, and typically limited, site characterization data. Using a limited number of full scale simulations as training data, a statistical model is developed for predicting the distribution of contaminant mass (e.g., DNAPL saturation and aqueous concentration) across a heterogeneous domain. Monte-Carlo sampling methods are then employed, in conjunction with the trained statistical model, to generate realizations conditioned on measured borehole data

  16. Robust Optimization Model for Production Planning Problem under Uncertainty

    Directory of Open Access Journals (Sweden)

    Pembe GÜÇLÜ

    2017-01-01

    Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

  17. Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand

    Directory of Open Access Journals (Sweden)

    Rogier Westerhoff

    2018-01-01

    Full Text Available A nationwide model of groundwater recharge for New Zealand (NGRM, as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014 provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m 3 /s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future

  18. The effects of geometric uncertainties on computational modelling of knee biomechanics

    Science.gov (United States)

    Meng, Qingen; Fisher, John; Wilcox, Ruth

    2017-08-01

    The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models.

  19. Spatial variability and parametric uncertainty in performance assessment models

    International Nuclear Information System (INIS)

    Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

    2011-01-01

    The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

  20. Uncertainties in modelling the climate impact of irrigation

    Science.gov (United States)

    de Vrese, Philipp; Hagemann, Stefan

    2017-11-01

    Irrigation-based agriculture constitutes an essential factor for food security as well as fresh water resources and has a distinct impact on regional and global climate. Many issues related to irrigation's climate impact are addressed in studies that apply a wide range of models. These involve substantial uncertainties related to differences in the model's structure and its parametrizations on the one hand and the need for simplifying assumptions for the representation of irrigation on the other hand. To address these uncertainties, we used the Max Planck Institute for Meteorology's Earth System model into which a simple irrigation scheme was implemented. In order to estimate possible uncertainties with regard to the model's more general structure, we compared the climate impact of irrigation between three simulations that use different schemes for the land-surface-atmosphere coupling. Here, it can be shown that the choice of coupling scheme does not only affect the magnitude of possible impacts but even their direction. For example, when using a scheme that does not explicitly resolve spatial subgrid scale heterogeneity at the surface, irrigation reduces the atmospheric water content, even in heavily irrigated regions. Contrarily, in simulations that use a coupling scheme that resolves heterogeneity at the surface or even within the lowest layers of the atmosphere, irrigation increases the average atmospheric specific humidity. A second experiment targeted possible uncertainties related to the representation of irrigation characteristics. Here, in four simulations the irrigation effectiveness (controlled by the target soil moisture and the non-vegetated fraction of the grid box that receives irrigation) and the timing of delivery were varied. The second experiment shows that uncertainties related to the modelled irrigation characteristics, especially the irrigation effectiveness, are also substantial. In general the impact of irrigation on the state of the land

  1. A python framework for environmental model uncertainty analysis

    Science.gov (United States)

    White, Jeremy; Fienen, Michael N.; Doherty, John E.

    2016-01-01

    We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.

  2. 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 ...

  3. Model parameter uncertainty analysis for an annual field-scale phosphorus 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 ...

  4. Type-2 fuzzy elliptic membership functions for modeling uncertainty

    DEFF Research Database (Denmark)

    Kayacan, Erdal; Sarabakha, Andriy; Coupland, Simon

    2018-01-01

    Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs...... in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance...... advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem...

  5. Structural reliability in context of statistical uncertainties and modelling discrepancies

    International Nuclear Information System (INIS)

    Pendola, Maurice

    2000-01-01

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

  6. All wind farm uncertainty is not the same: The economics of common versus independent causes

    International Nuclear Information System (INIS)

    Veers, P.S.

    1994-01-01

    There is uncertainty in the performance of wind energy installations due to unknowns in the local wind environment, machine response to the environment, and the durability of materials. Some of the unknowns are inherently independent from machine to machine while other uncertainties are common to the entire fleet equally. The FAROW computer software for fatigue and reliability of wind turbines is used to calculate the probability of component failure due to a combination of all sources of uncertainty. Although the total probability of component failure due to all effects is sometimes interpreted as the percentage of components likely to fail, this perception is often far from correct. Different amounts of common versus independent uncertainty are reflected in economic risk due to either high probabilities that a small percentage of the fleet will experience problems or low probabilities that the entire fleet will have problems. The average, or expected cost is the same as would be calculated by combining all sources of uncertainty, but the risk to the fleet may be quite different in nature. Present values of replacement costs are compared for two examples reflecting different stages in the design and development process. Results emphasize that an engineering effort to test and evaluate the design assumptions is necessary to advance a design from the high uncertainty of the conceptual stages to the lower uncertainty of a well engineered and tested machine

  7. Uncertainty and stress: Why it causes diseases and how it is mastered by the brain.

    Science.gov (United States)

    Peters, Achim; McEwen, Bruce S; Friston, Karl

    2017-09-01

    The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or 'expected surprise'. The 'free energy principle' rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected - and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the 'selfish brain'. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by 'allostatic load' that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. A sliding mode observer for hemodynamic characterization under modeling uncertainties

    KAUST Repository

    Zayane, Chadia

    2014-06-01

    This paper addresses the case of physiological states reconstruction in a small region of the brain under modeling uncertainties. The misunderstood coupling between the cerebral blood volume and the oxygen extraction fraction has lead to a partial knowledge of the so-called balloon model describing the hemodynamic behavior of the brain. To overcome this difficulty, a High Order Sliding Mode observer is applied to the balloon system, where the unknown coupling is considered as an internal perturbation. The effectiveness of the proposed method is illustrated through a set of synthetic data that mimic fMRI experiments.

  9. A Bayesian statistical method for quantifying model form uncertainty and two model combination methods

    International Nuclear Information System (INIS)

    Park, Inseok; Grandhi, Ramana V.

    2014-01-01

    Apart from parametric uncertainty, model form uncertainty as well as prediction error may be involved in the analysis of engineering system. Model form uncertainty, inherently existing in selecting the best approximation from a model set cannot be ignored, especially when the predictions by competing models show significant differences. In this research, a methodology based on maximum likelihood estimation is presented to quantify model form uncertainty using the measured differences of experimental and model outcomes, and is compared with a fully Bayesian estimation to demonstrate its effectiveness. While a method called the adjustment factor approach is utilized to propagate model form uncertainty alone into the prediction of a system response, a method called model averaging is utilized to incorporate both model form uncertainty and prediction error into it. A numerical problem of concrete creep is used to demonstrate the processes for quantifying model form uncertainty and implementing the adjustment factor approach and model averaging. Finally, the presented methodology is applied to characterize the engineering benefits of a laser peening process

  10. 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

  11. Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

    Science.gov (United States)

    Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

    2018-02-01

    The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

  12. Dynamics Under Location Uncertainty: Model Derivation, Modified Transport and Uncertainty Quantification

    Science.gov (United States)

    Resseguier, V.; Memin, E.; Chapron, B.; Fox-Kemper, B.

    2017-12-01

    In order to better observe and predict geophysical flows, ensemble-based data assimilation methods are of high importance. In such methods, an ensemble of random realizations represents the variety of the simulated flow's likely behaviors. For this purpose, randomness needs to be introduced in a suitable way and physically-based stochastic subgrid parametrizations are promising paths. This talk will propose a new kind of such a parametrization referred to as modeling under location uncertainty. The fluid velocity is decomposed into a resolved large-scale component and an aliased small-scale one. The first component is possibly random but time-correlated whereas the second is white-in-time but spatially-correlated and possibly inhomogeneous and anisotropic. With such a velocity, the material derivative of any - possibly active - tracer is modified. Three new terms appear: a correction of the large-scale advection, a multiplicative noise and a possibly heterogeneous and anisotropic diffusion. This parameterization naturally ensures attractive properties such as energy conservation for each realization. Additionally, this stochastic material derivative and the associated Reynolds' transport theorem offer a systematic method to derive stochastic models. In particular, we will discuss the consequences of the Quasi-Geostrophic assumptions in our framework. Depending on the turbulence amount, different models with different physical behaviors are obtained. Under strong turbulence assumptions, a simplified diagnosis of frontolysis and frontogenesis at the surface of the ocean is possible in this framework. A Surface Quasi-Geostrophic (SQG) model with a weaker noise influence has also been simulated. A single realization better represents small scales than a deterministic SQG model at the same resolution. Moreover, an ensemble accurately predicts extreme events, bifurcations as well as the amplitudes and the positions of the simulation errors. Figure 1 highlights this last

  13. A Bayesian Framework of Uncertainties Integration in 3D Geological Model

    Science.gov (United States)

    Liang, D.; Liu, X.

    2017-12-01

    3D geological model can describe complicated geological phenomena in an intuitive way while its application may be limited by uncertain factors. Great progress has been made over the years, lots of studies decompose the uncertainties of geological model to analyze separately, while ignored the comprehensive impacts of multi-source uncertainties. Great progress has been made over the years, while lots of studies ignored the comprehensive impacts of multi-source uncertainties when analyzed them item by item from each source. To evaluate the synthetical uncertainty, we choose probability distribution to quantify uncertainty, and propose a bayesian framework of uncertainties integration. With this framework, we integrated data errors, spatial randomness, and cognitive information into posterior distribution to evaluate synthetical uncertainty of geological model. Uncertainties propagate and cumulate in modeling process, the gradual integration of multi-source uncertainty is a kind of simulation of the uncertainty propagation. Bayesian inference accomplishes uncertainty updating in modeling process. Maximum entropy principle makes a good effect on estimating prior probability distribution, which ensures the prior probability distribution subjecting to constraints supplied by the given information with minimum prejudice. In the end, we obtained a posterior distribution to evaluate synthetical uncertainty of geological model. This posterior distribution represents the synthetical impact of all the uncertain factors on the spatial structure of geological model. The framework provides a solution to evaluate synthetical impact on geological model of multi-source uncertainties and a thought to study uncertainty propagation mechanism in geological modeling.

  14. Multiscale Modeling and Uncertainty Quantification for Nuclear Fuel Performance

    Energy Technology Data Exchange (ETDEWEB)

    Estep, Donald [Colorado State Univ., Fort Collins, CO (United States); El-Azab, Anter [Florida State Univ., Tallahassee, FL (United States); Pernice, Michael [Idaho National Lab. (INL), Idaho Falls, ID (United States); Peterson, John W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Polyakov, Peter [Univ. of Wyoming, Laramie, WY (United States); Tavener, Simon [Colorado State Univ., Fort Collins, CO (United States); Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States); Univ. of Utah, Salt Lake City, UT (United States)

    2017-03-23

    In this project, we will address the challenges associated with constructing high fidelity multiscale models of nuclear fuel performance. We (*) propose a novel approach for coupling mesoscale and macroscale models, (*) devise efficient numerical methods for simulating the coupled system, and (*) devise and analyze effective numerical approaches for error and uncertainty quantification for the coupled multiscale system. As an integral part of the project, we will carry out analysis of the effects of upscaling and downscaling, investigate efficient methods for stochastic sensitivity analysis of the individual macroscale and mesoscale models, and carry out a posteriori error analysis for computed results. We will pursue development and implementation of solutions in software used at Idaho National Laboratories on models of interest to the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program.

  15. Uncertainty identification for robust control using a nuclear power plant model

    International Nuclear Information System (INIS)

    Power, M.; Edwards, R.M.

    1995-01-01

    An on-line technique which identifies the uncertainty between a lower order and a higher order nuclear power plant model is presented. The uncertainty identifier produces a hard upper bound in H ∞ on the additive uncertainty. This additive uncertainty description can be used for the design of H infinity or μ-synthesis controllers

  16. Making Invasion models useful for decision makers; incorporating uncertainty, knowledge gaps, and decision-making preferences

    Science.gov (United States)

    Denys Yemshanov; Frank H Koch; Mark Ducey

    2015-01-01

    Uncertainty is inherent in model-based forecasts of ecological invasions. In this chapter, we explore how the perceptions of that uncertainty can be incorporated into the pest risk assessment process. Uncertainty changes a decision maker’s perceptions of risk; therefore, the direct incorporation of uncertainty may provide a more appropriate depiction of risk. Our...

  17. Quantifying uncertainty, variability and likelihood for ordinary differential equation models

    LENUS (Irish Health Repository)

    Weisse, Andrea Y

    2010-10-28

    Abstract Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.

  18. Quantifying uncertainty in Bayesian calibrated animal-to-human PBPK models with informative prior distributions

    Science.gov (United States)

    Understanding and quantifying the uncertainty of model parameters and predictions has gained more interest in recent years with the increased use of computational models in chemical risk assessment. Fully characterizing the uncertainty in risk metrics derived from linked quantita...

  19. Reliability of Coulomb stress changes inferred from correlated uncertainties of finite-fault source models

    KAUST Repository

    Woessner, J.

    2012-07-14

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

  20. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  1. Selection of Representative Models for Decision Analysis Under Uncertainty

    Science.gov (United States)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  2. Workshop on Model Uncertainty and its Statistical Implications

    CERN Document Server

    1988-01-01

    In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

  3. Model structures amplify uncertainty in predicted soil carbon responses to climate change.

    Science.gov (United States)

    Shi, Zheng; Crowell, Sean; Luo, Yiqi; Moore, Berrien

    2018-06-04

    Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

  4. Uncertainty quantification and sensitivity analysis of an arterial wall mechanics model for evaluation of vascular drug therapies.

    Science.gov (United States)

    Heusinkveld, Maarten H G; Quicken, Sjeng; Holtackers, Robert J; Huberts, Wouter; Reesink, Koen D; Delhaas, Tammo; Spronck, Bart

    2018-02-01

    Quantification of the uncertainty in constitutive model predictions describing arterial wall mechanics is vital towards non-invasive assessment of vascular drug therapies. Therefore, we perform uncertainty quantification to determine uncertainty in mechanical characteristics describing the vessel wall response upon loading. Furthermore, a global variance-based sensitivity analysis is performed to pinpoint measurements that are most rewarding to be measured more precisely. We used previously published carotid diameter-pressure and intima-media thickness (IMT) data (measured in triplicate), and Holzapfel-Gasser-Ogden models. A virtual data set containing 5000 diastolic and systolic diameter-pressure points, and IMT values was generated by adding measurement error to the average of the measured data. The model was fitted to single-exponential curves calculated from the data, obtaining distributions of constitutive parameters and constituent load bearing parameters. Additionally, we (1) simulated vascular drug treatment to assess the relevance of model uncertainty and (2) evaluated how increasing the number of measurement repetitions influences model uncertainty. We found substantial uncertainty in constitutive parameters. Simulating vascular drug treatment predicted a 6% point reduction in collagen load bearing ([Formula: see text]), approximately 50% of its uncertainty. Sensitivity analysis indicated that the uncertainty in [Formula: see text] was primarily caused by noise in distension and IMT measurements. Spread in [Formula: see text] could be decreased by 50% when increasing the number of measurement repetitions from 3 to 10. Model uncertainty, notably that in [Formula: see text], could conceal effects of vascular drug therapy. However, this uncertainty could be reduced by increasing the number of measurement repetitions of distension and wall thickness measurements used for model parameterisation.

  5. Tolerance of uncertainty: Conceptual analysis, integrative model, and implications for healthcare.

    Science.gov (United States)

    Hillen, Marij A; Gutheil, Caitlin M; Strout, Tania D; Smets, Ellen M A; Han, Paul K J

    2017-05-01

    Uncertainty tolerance (UT) is an important, well-studied phenomenon in health care and many other important domains of life, yet its conceptualization and measurement by researchers in various disciplines have varied substantially and its essential nature remains unclear. The objectives of this study were to: 1) analyze the meaning and logical coherence of UT as conceptualized by developers of UT measures, and 2) develop an integrative conceptual model to guide future empirical research regarding the nature, causes, and effects of UT. A narrative review and conceptual analysis of 18 existing measures of Uncertainty and Ambiguity Tolerance was conducted, focusing on how measure developers in various fields have defined both the "uncertainty" and "tolerance" components of UT-both explicitly through their writings and implicitly through the items constituting their measures. Both explicit and implicit conceptual definitions of uncertainty and tolerance vary substantially and are often poorly and inconsistently specified. A logically coherent, unified understanding or theoretical model of UT is lacking. To address these gaps, we propose a new integrative definition and multidimensional conceptual model that construes UT as the set of negative and positive psychological responses-cognitive, emotional, and behavioral-provoked by the conscious awareness of ignorance about particular aspects of the world. This model synthesizes insights from various disciplines and provides an organizing framework for future research. We discuss how this model can facilitate further empirical and theoretical research to better measure and understand the nature, determinants, and outcomes of UT in health care and other domains of life. Uncertainty tolerance is an important and complex phenomenon requiring more precise and consistent definition. An integrative definition and conceptual model, intended as a tentative and flexible point of departure for future research, adds needed breadth

  6. A conceptual precipitation-runoff modeling suite: Model selection, calibration and predictive uncertainty assessment

    Science.gov (United States)

    Tyler Jon Smith

    2008-01-01

    In Montana and much of the Rocky Mountain West, the single most important parameter in forecasting the controls on regional water resources is snowpack. Despite the heightened importance of snowpack, few studies have considered the representation of uncertainty in coupled snowmelt/hydrologic conceptual models. Uncertainty estimation provides a direct interpretation of...

  7. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  8. GARUSO - Version 1.0. Uncertainty model for multipath ultrasonic transit time gas flow meters

    Energy Technology Data Exchange (ETDEWEB)

    Lunde, Per; Froeysa, Kjell-Eivind; Vestrheim, Magne

    1997-09-01

    This report describes an uncertainty model for ultrasonic transit time gas flow meters configured with parallel chords, and a PC program, GARUSO Version 1.0, implemented for calculation of the meter`s relative expanded uncertainty. The program, which is based on the theoretical uncertainty model, is used to carry out a simplified and limited uncertainty analysis for a 12`` 4-path meter, where examples of input and output uncertainties are given. The model predicts a relative expanded uncertainty for the meter at a level which further justifies today`s increasing tendency to use this type of instruments for fiscal metering of natural gas. 52 refs., 15 figs., 11 tabs.

  9. Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples

    NARCIS (Netherlands)

    Ettema, D.F.; Timmermans, H.J.P.

    2006-01-01

    A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves

  10. System convergence in transport models: algorithms efficiency and output uncertainty

    DEFF Research Database (Denmark)

    Rich, Jeppe; Nielsen, Otto Anker

    2015-01-01

    of this paper is to analyse convergence performance for the external loop and to illustrate how an improper linkage between the converging parts can lead to substantial uncertainty in the final output. Although this loop is crucial for the performance of large-scale transport models it has not been analysed...... much in the literature. The paper first investigates several variants of the Method of Successive Averages (MSA) by simulation experiments on a toy-network. It is found that the simulation experiments produce support for a weighted MSA approach. The weighted MSA approach is then analysed on large......-scale in the Danish National Transport Model (DNTM). It is revealed that system convergence requires that either demand or supply is without random noise but not both. In that case, if MSA is applied to the model output with random noise, it will converge effectively as the random effects are gradually dampened...

  11. Incentive salience attribution under reward uncertainty: A Pavlovian model.

    Science.gov (United States)

    Anselme, Patrick

    2015-02-01

    There is a vast literature on the behavioural effects of partial reinforcement in Pavlovian conditioning. Compared with animals receiving continuous reinforcement, partially rewarded animals typically show (a) a slower development of the conditioned response (CR) early in training and (b) a higher asymptotic level of the CR later in training. This phenomenon is known as the partial reinforcement acquisition effect (PRAE). Learning models of Pavlovian conditioning fail to account for it. In accordance with the incentive salience hypothesis, it is here argued that incentive motivation (or 'wanting') plays a more direct role in controlling behaviour than does learning, and reward uncertainty is shown to have an excitatory effect on incentive motivation. The psychological origin of that effect is discussed and a computational model integrating this new interpretation is developed. Many features of CRs under partial reinforcement emerge from this model. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Plasticity models of material variability based on uncertainty quantification techniques

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

    2017-11-01

    The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

  13. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

    Science.gov (United States)

    He, L; Huang, G H; Lu, H W

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

  14. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part I. Model development

    Energy Technology Data Exchange (ETDEWEB)

    He, L., E-mail: li.he@ryerson.ca [Department of Civil Engineering, Faculty of Engineering, Architecture and Science, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 (Canada); Huang, G.H. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban Environmental Sciences, Peking University, Beijing 100871 (China); Lu, H.W. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the 'true' ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes.

  15. Uncertainties of Large-Scale Forcing Caused by Surface Turbulence Flux Measurements and the Impacts on Cloud Simulations at the ARM SGP Site

    Science.gov (United States)

    Tang, S.; Xie, S.; Tang, Q.; Zhang, Y.

    2017-12-01

    Two types of instruments, the eddy correlation flux measurement system (ECOR) and the energy balance Bowen ratio system (EBBR), are used at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site to measure surface latent and sensible fluxes. ECOR and EBBR typically sample different land surface types, and the domain-mean surface fluxes derived from ECOR and EBBR are not always consistent. The uncertainties of the surface fluxes will have impacts on the derived large-scale forcing data and further affect the simulations of single-column models (SCM), cloud-resolving models (CRM) and large-eddy simulation models (LES), especially for the shallow-cumulus clouds which are mainly driven by surface forcing. This study aims to quantify the uncertainties of the large-scale forcing caused by surface turbulence flux measurements and investigate the impacts on cloud simulations using long-term observations from the ARM SGP site.

  16. Remediation of the Faultless Underground Nuclear Test: Moving Forward in the Face of Model Uncertainty

    International Nuclear Information System (INIS)

    Chapman, J. B.; Pohlmann, K.; Pohll, G.; Hassan, A.; Sanders, P.; Sanchez, M.; Jaunarajs, S.

    2002-01-01

    The Faultless underground nuclear test, conducted in central Nevada, is the site of an ongoing environmental remediation effort that has successfully progressed through numerous technical challenges due to close cooperation between the U.S. Department of Energy, (DOE) National Nuclear Security Administration and the State of Nevada Division of Environmental Protection (NDEP). The challenges faced at this site are similar to those of many other sites of groundwater contamination: substantial uncertainties due to the relative lack of data from a highly heterogeneous subsurface environment. Knowing when, where, and how to devote the often enormous resources needed to collect new data is a common problem, and one that can cause remediators and regulators to disagree and stall progress toward closing sites. For Faultless, a variety of numerical modeling techniques and statistical tools are used to provide the information needed for DOE and NDEP to confidently move forward along the remediation path to site closure. A general framework for remediation was established in an agreement and consent order between DOE and the State of Nevada that recognized that no cost-effective technology currently exists to remove the source of contaminants in nuclear cavities. Rather, the emphasis of the corrective action is on identifying the impacted groundwater resource and ensuring protection of human health and the environment from the contamination through monitoring. As a result, groundwater flow and transport modeling is the linchpin in the remediation effort. An early issue was whether or not new site data should be collected via drilling and testing prior to modeling. After several iterations of the Corrective Action Investigation Plan, all parties agreed that sufficient data existed to support a flow and transport model for the site. Though several aspects of uncertainty were included in the subsequent modeling work, concerns remained regarding uncertainty in individual

  17. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil

    Directory of Open Access Journals (Sweden)

    Paula Mendes Luz

    2003-10-01

    Full Text Available Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii how vector density spatial heterogeneity influences control efforts; (iii with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4 in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R0 that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.

  18. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil

    Directory of Open Access Journals (Sweden)

    Luz Paula Mendes

    2003-01-01

    Full Text Available Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii how vector density spatial heterogeneity influences control efforts; (iii with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4 in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R0 that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.

  19. A model for optimization of process integration investments under uncertainty

    International Nuclear Information System (INIS)

    Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael

    2011-01-01

    The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.

  20. Multi-Fidelity Uncertainty Propagation for Cardiovascular Modeling

    Science.gov (United States)

    Fleeter, Casey; Geraci, Gianluca; Schiavazzi, Daniele; Kahn, Andrew; Marsden, Alison

    2017-11-01

    Hemodynamic models are successfully employed in the diagnosis and treatment of cardiovascular disease with increasing frequency. However, their widespread adoption is hindered by our inability to account for uncertainty stemming from multiple sources, including boundary conditions, vessel material properties, and model geometry. In this study, we propose a stochastic framework which leverages three cardiovascular model fidelities: 3D, 1D and 0D models. 3D models are generated from patient-specific medical imaging (CT and MRI) of aortic and coronary anatomies using the SimVascular open-source platform, with fluid structure interaction simulations and Windkessel boundary conditions. 1D models consist of a simplified geometry automatically extracted from the 3D model, while 0D models are obtained from equivalent circuit representations of blood flow in deformable vessels. Multi-level and multi-fidelity estimators from Sandia's open-source DAKOTA toolkit are leveraged to reduce the variance in our estimated output quantities of interest while maintaining a reasonable computational cost. The performance of these estimators in terms of computational cost reductions is investigated for a variety of output quantities of interest, including global and local hemodynamic indicators. Sandia National Labs is a multimission laboratory managed and operated by NTESS, LLC, for the U.S. DOE under contract DE-NA0003525. Funding for this project provided by NIH-NIBIB R01 EB018302.

  1. An Uncertainty Structure Matrix for Models and Simulations

    Science.gov (United States)

    Green, Lawrence L.; Blattnig, Steve R.; Hemsch, Michael J.; Luckring, James M.; Tripathi, Ram K.

    2008-01-01

    Software that is used for aerospace flight control and to display information to pilots and crew is expected to be correct and credible at all times. This type of software is typically developed under strict management processes, which are intended to reduce defects in the software product. However, modeling and simulation (M&S) software may exhibit varying degrees of correctness and credibility, depending on a large and complex set of factors. These factors include its intended use, the known physics and numerical approximations within the M&S, and the referent data set against which the M&S correctness is compared. The correctness and credibility of an M&S effort is closely correlated to the uncertainty management (UM) practices that are applied to the M&S effort. This paper describes an uncertainty structure matrix for M&S, which provides a set of objective descriptions for the possible states of UM practices within a given M&S effort. The columns in the uncertainty structure matrix contain UM elements or practices that are common across most M&S efforts, and the rows describe the potential levels of achievement in each of the elements. A practitioner can quickly look at the matrix to determine where an M&S effort falls based on a common set of UM practices that are described in absolute terms that can be applied to virtually any M&S effort. The matrix can also be used to plan those steps and resources that would be needed to improve the UM practices for a given M&S effort.

  2. A risk-averse optimization model for trading wind energy in a market environment under uncertainty

    International Nuclear Information System (INIS)

    Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.

    2011-01-01

    In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. -- Highlights: → We model uncertainties on energy market prices and wind power production. → A hybrid intelligent approach generates price-wind power scenarios. → Risk aversion is also incorporated in the proposed stochastic programming approach. → A realistic case study, based on a wind farm in Portugal, is provided. → Our approach allows selecting the best solution according to the desired risk exposure level.

  3. Parameter estimation techniques and uncertainty in ground water flow model predictions

    International Nuclear Information System (INIS)

    Zimmerman, D.A.; Davis, P.A.

    1990-01-01

    Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs

  4. An analysis of sensitivity and uncertainty associated with the use of the HSPF model for EIA applications

    Energy Technology Data Exchange (ETDEWEB)

    Biftu, G.F.; Beersing, A.; Wu, S.; Ade, F. [Golder Associates, Calgary, AB (Canada)

    2005-07-01

    An outline of a new approach to assessing the sensitivity and uncertainty associated with surface water modelling results using Hydrological Simulation Program-Fortran (HSPF) was presented, as well as the results of a sensitivity and uncertainty analysis. The HSPF model is often used to characterize the hydrological processes in watersheds within the oil sands region. Typical applications of HSPF included calibration of the model parameters using data from gauged watersheds, as well as validation of calibrated models with data sets. Additionally, simulations are often conducted to make flow predictions to support the environmental impact assessment (EIA) process. However, a key aspect of the modelling components of the EIA process is the sensitivity and uncertainty of the modelling results as compared to model parameters. Many of the variations in the HSPF model's outputs are caused by a small number of model parameters and outputs. A sensitivity analysis was performed to identify and focus on key parameters and assumptions that have the most influence on the model's outputs. Analysis entailed varying each parameter in turn, within a range, and examining the resulting relative changes in the model outputs. This analysis consisted of the selection of probability distributions to characterize the uncertainty in the model's key sensitive parameters, as well as the use of Monte Carlo and HSPF simulation to determine the uncertainty in model outputs. tabs, figs.

  5. Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study.

    Science.gov (United States)

    Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

    2007-08-23

    The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful

  6. Parameter and model uncertainty in a life-table model for fine particles (PM2.5: a statistical modeling study

    Directory of Open Access Journals (Sweden)

    Jantunen Matti J

    2007-08-01

    Full Text Available Abstract Background The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5 are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Methods Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i plausibility of mortality outcomes and (ii lag, and parameter uncertainties (iii exposure-response coefficients for different mortality outcomes, and (iv exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. Results The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. Conclusion When estimating life-expectancy, the estimates used for cardiopulmonary exposure

  7. High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME

    Science.gov (United States)

    Otis, Richard A.; Liu, Zi-Kui

    2017-05-01

    One foundational component of the integrated computational materials engineering (ICME) and Materials Genome Initiative is the computational thermodynamics based on the calculation of phase diagrams (CALPHAD) method. The CALPHAD method pioneered by Kaufman has enabled the development of thermodynamic, atomic mobility, and molar volume databases of individual phases in the full space of temperature, composition, and sometimes pressure for technologically important multicomponent engineering materials, along with sophisticated computational tools for using the databases. In this article, our recent efforts will be presented in terms of developing new computational tools for high-throughput modeling and uncertainty quantification based on high-throughput, first-principles calculations and the CALPHAD method along with their potential propagations to downstream ICME modeling and simulations.

  8. Estimation of spatial uncertainties of tomographic velocity models

    Energy Technology Data Exchange (ETDEWEB)

    Jordan, M.; Du, Z.; Querendez, E. [SINTEF Petroleum Research, Trondheim (Norway)

    2012-12-15

    This research project aims to evaluate the possibility of assessing the spatial uncertainties in tomographic velocity model building in a quantitative way. The project is intended to serve as a test of whether accurate and specific uncertainty estimates (e.g., in meters) can be obtained. The project is based on Monte Carlo-type perturbations of the velocity model as obtained from the tomographic inversion guided by diagonal and off-diagonal elements of the resolution and the covariance matrices. The implementation and testing of this method was based on the SINTEF in-house stereotomography code, using small synthetic 2D data sets. To test the method the calculation and output of the covariance and resolution matrices was implemented, and software to perform the error estimation was created. The work included the creation of 2D synthetic data sets, the implementation and testing of the software to conduct the tests (output of the covariance and resolution matrices which are not implicitly provided by stereotomography), application to synthetic data sets, analysis of the test results, and creating the final report. The results show that this method can be used to estimate the spatial errors in tomographic images quantitatively. The results agree with' the known errors for our synthetic models. However, the method can only be applied to structures in the model, where the change of seismic velocity is larger than the predicted error of the velocity parameter amplitudes. In addition, the analysis is dependent on the tomographic method, e.g., regularization and parameterization. The conducted tests were very successful and we believe that this method could be developed further to be applied to third party tomographic images.

  9. 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

  10. 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...

  11. 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.

  12. Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

    Science.gov (United States)

    Soleimani, Hossein; Hensman, James; Saria, Suchi

    2017-08-21

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

  13. Quantile uncertainty and value-at-risk model risk.

    Science.gov (United States)

    Alexander, Carol; Sarabia, José María

    2012-08-01

    This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value-at-Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of "model risk" in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value-at-Risk model risk and compute the required regulatory capital add-on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value-at-Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks. © 2012 Society for Risk Analysis.

  14. Quantifying uncertainty in LCA-modelling of waste management systems

    DEFF Research Database (Denmark)

    Clavreul, Julie; Guyonnet, D.; Christensen, Thomas Højlund

    2012-01-01

    Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present...... the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining...

  15. Addressing model uncertainty in dose-response: The case of chloroform

    International Nuclear Information System (INIS)

    Evans, J.S.

    1994-01-01

    This paper discusses the issues involved in addressing model uncertainty in the analysis of dose-response relationships. A method for addressing model uncertainty is described and applied to characterize the uncertainty in estimates of the carcinogenic potency of chloroform. The approach, which is rooted in Bayesian concepts of subjective probability, uses probability trees and formally-elicited expert judgments to address model uncertainty. It is argued that a similar approach could be used to improve the characterization of model uncertainty in the dose-response relationships for health effects from ionizing radiation

  16. Model uncertainty in financial markets : Long run risk and parameter uncertainty

    NARCIS (Netherlands)

    de Roode, F.A.

    2014-01-01

    Uncertainty surrounding key parameters of financial markets, such as the in- flation and equity risk premium, constitute a major risk for institutional investors with long investment horizons. Hedging the investors’ inflation exposure can be challenging due to the lack of domestic inflation-linked

  17. Uncertainty and sensitivity assessments of an agricultural-hydrological model (RZWQM2) using the GLUE method

    Science.gov (United States)

    Sun, Mei; Zhang, Xiaolin; Huo, Zailin; Feng, Shaoyuan; Huang, Guanhua; Mao, Xiaomin

    2016-03-01

    Quantitatively ascertaining and analyzing the effects of model uncertainty on model reliability is a focal point for agricultural-hydrological models due to more uncertainties of inputs and processes. In this study, the generalized likelihood uncertainty estimation (GLUE) method with Latin hypercube sampling (LHS) was used to evaluate the uncertainty of the RZWQM-DSSAT (RZWQM2) model outputs responses and the sensitivity of 25 parameters related to soil properties, nutrient transport and crop genetics. To avoid the one-sided risk of model prediction caused by using a single calibration criterion, the combined likelihood (CL) function integrated information concerning water, nitrogen, and crop production was introduced in GLUE analysis for the predictions of the following four model output responses: the total amount of water content (T-SWC) and the nitrate nitrogen (T-NIT) within the 1-m soil profile, the seed yields of waxy maize (Y-Maize) and winter wheat (Y-Wheat). In the process of evaluating RZWQM2, measurements and meteorological data were obtained from a field experiment that involved a winter wheat and waxy maize crop rotation system conducted from 2003 to 2004 in southern Beijing. The calibration and validation results indicated that RZWQM2 model can be used to simulate the crop growth and water-nitrogen migration and transformation in wheat-maize crop rotation planting system. The results of uncertainty analysis using of GLUE method showed T-NIT was sensitive to parameters relative to nitrification coefficient, maize growth characteristics on seedling period, wheat vernalization period, and wheat photoperiod. Parameters on soil saturated hydraulic conductivity, nitrogen nitrification and denitrification, and urea hydrolysis played an important role in crop yield component. The prediction errors for RZWQM2 outputs with CL function were relatively lower and uniform compared with other likelihood functions composed of individual calibration criterion. This

  18. Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

    Science.gov (United States)

    Slaba, T. C.; O'Neill, P. M.; Golge, S.; Norbury, J. W.

    2015-01-01

    For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

  19. Calibration under uncertainty for finite element models of masonry monuments

    Energy Technology Data Exchange (ETDEWEB)

    Atamturktur, Sezer,; Hemez, Francois,; Unal, Cetin

    2010-02-01

    Historical unreinforced masonry buildings often include features such as load bearing unreinforced masonry vaults and their supporting framework of piers, fill, buttresses, and walls. The masonry vaults of such buildings are among the most vulnerable structural components and certainly among the most challenging to analyze. The versatility of finite element (FE) analyses in incorporating various constitutive laws, as well as practically all geometric configurations, has resulted in the widespread use of the FE method for the analysis of complex unreinforced masonry structures over the last three decades. However, an FE model is only as accurate as its input parameters, and there are two fundamental challenges while defining FE model input parameters: (1) material properties and (2) support conditions. The difficulties in defining these two aspects of the FE model arise from the lack of knowledge in the common engineering understanding of masonry behavior. As a result, engineers are unable to define these FE model input parameters with certainty, and, inevitably, uncertainties are introduced to the FE model.

  20. Uncertainty in dual permeability model parameters for structured soils

    Science.gov (United States)

    Arora, B.; Mohanty, B. P.; McGuire, J. T.

    2012-01-01

    Successful application of dual permeability models (DPM) to predict contaminant transport is contingent upon measured or inversely estimated soil hydraulic and solute transport parameters. The difficulty in unique identification of parameters for the additional macropore- and matrix-macropore interface regions, and knowledge about requisite experimental data for DPM has not been resolved to date. Therefore, this study quantifies uncertainty in dual permeability model parameters of experimental soil columns with different macropore distributions (single macropore, and low- and high-density multiple macropores). Uncertainty evaluation is conducted using adaptive Markov chain Monte Carlo (AMCMC) and conventional Metropolis-Hastings (MH) algorithms while assuming 10 out of 17 parameters to be uncertain or random. Results indicate that AMCMC resolves parameter correlations and exhibits fast convergence for all DPM parameters while MH displays large posterior correlations for various parameters. This study demonstrates that the choice of parameter sampling algorithms is paramount in obtaining unique DPM parameters when information on covariance structure is lacking, or else additional information on parameter correlations must be supplied to resolve the problem of equifinality of DPM parameters. This study also highlights the placement and significance of matrix-macropore interface in flow experiments of soil columns with different macropore densities. Histograms for certain soil hydraulic parameters display tri-modal characteristics implying that macropores are drained first followed by the interface region and then by pores of the matrix domain in drainage experiments. Results indicate that hydraulic properties and behavior of the matrix-macropore interface is not only a function of saturated hydraulic conductivity of the macroporematrix interface (Ksa) and macropore tortuosity (lf) but also of other parameters of the matrix and macropore domains.

  1. Evidence-based quantification of uncertainties induced via simulation-based modeling

    International Nuclear Information System (INIS)

    Riley, Matthew E.

    2015-01-01

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

  2. Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

    Science.gov (United States)

    Wang, W.; Rinke, A.; Moore, J. C.; Cui, X.; Ji, D.; Li, Q.; Zhang, N.; Wang, C.; Zhang, S.; Lawrence, D. M.; McGuire, A. D.; Zhang, W.; Delire, C.; Koven, C.; Saito, K.; MacDougall, A.; Burke, E.; Decharme, B.

    2016-02-01

    We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135 × 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101 × 104 km2). However the uncertainty (1 to 128 × 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for

  3. Uncertainties in (E)UV model atmosphere fluxes

    Science.gov (United States)

    Rauch, T.

    2008-04-01

    Context: During the comparison of synthetic spectra calculated with two NLTE model atmosphere codes, namely TMAP and TLUSTY, we encounter systematic differences in the EUV fluxes due to the treatment of level dissolution by pressure ionization. Aims: In the case of Sirius B, we demonstrate an uncertainty in modeling the EUV flux reliably in order to challenge theoreticians to improve the theory of level dissolution. Methods: We calculated synthetic spectra for hot, compact stars using state-of-the-art NLTE model-atmosphere techniques. Results: Systematic differences may occur due to a code-specific cutoff frequency of the H I Lyman bound-free opacity. This is the case for TMAP and TLUSTY. Both codes predict the same flux level at wavelengths lower than about 1500 Å for stars with effective temperatures (T_eff) below about 30 000 K only, if the same cutoff frequency is chosen. Conclusions: The theory of level dissolution in high-density plasmas, which is available for hydrogen only should be generalized to all species. Especially, the cutoff frequencies for the bound-free opacities should be defined in order to make predictions of UV fluxes more reliable.

  4. Assessing uncertainty in SRTM elevations for global flood modelling

    Science.gov (United States)

    Hawker, L. P.; Rougier, J.; Neal, J. C.; Bates, P. D.

    2017-12-01

    The SRTM DEM is widely used as the topography input to flood models in data-sparse locations. Understanding spatial error in the SRTM product is crucial in constraining uncertainty about elevations and assessing the impact of these upon flood prediction. Assessment of SRTM error was carried out by Rodriguez et al (2006), but this did not explicitly quantify the spatial structure of vertical errors in the DEM, and nor did it distinguish between errors over different types of landscape. As a result, there is a lack of information about spatial structure of vertical errors of the SRTM in the landscape that matters most to flood models - the floodplain. Therefore, this study attempts this task by comparing SRTM, an error corrected SRTM product (The MERIT DEM of Yamazaki et al., 2017) and near truth LIDAR elevations for 3 deltaic floodplains (Mississippi, Po, Wax Lake) and a large lowland region (the Fens, UK). Using the error covariance function, calculated by comparing SRTM elevations to the near truth LIDAR, perturbations of the 90m SRTM DEM were generated, producing a catalogue of plausible DEMs. This allows modellers to simulate a suite of plausible DEMs at any aggregated block size above native SRTM resolution. Finally, the generated DEM's were input into a hydrodynamic model of the Mekong Delta, built using the LISFLOOD-FP hydrodynamic model, to assess how DEM error affects the hydrodynamics and inundation extent across the domain. The end product of this is an inundation map with the probability of each pixel being flooded based on the catalogue of DEMs. In a world of increasing computer power, but a lack of detailed datasets, this powerful approach can be used throughout natural hazard modelling to understand how errors in the SRTM DEM can impact the hazard assessment.

  5. Quantum-memory-assisted entropic uncertainty in spin models with Dzyaloshinskii-Moriya interaction

    Science.gov (United States)

    Huang, Zhiming

    2018-02-01

    In this article, we investigate the dynamics and correlations of quantum-memory-assisted entropic uncertainty, the tightness of the uncertainty, entanglement, quantum correlation and mixedness for various spin chain models with Dzyaloshinskii-Moriya (DM) interaction, including the XXZ model with DM interaction, the XY model with DM interaction and the Ising model with DM interaction. We find that the uncertainty grows to a stable value with growing temperature but reduces as the coupling coefficient, anisotropy parameter and DM values increase. It is found that the entropic uncertainty is closely correlated with the mixedness of the system. The increasing quantum correlation can result in a decrease in the uncertainty, and the robustness of quantum correlation is better than entanglement since entanglement means sudden birth and death. The tightness of the uncertainty drops to zero, apart from slight volatility as various parameters increase. Furthermore, we propose an effective approach to steering the uncertainty by weak measurement reversal.

  6. Testing methodologies for quantifying physical models uncertainties. A comparative exercise using CIRCE and IPREM (FFTBM)

    Energy Technology Data Exchange (ETDEWEB)

    Freixa, Jordi, E-mail: jordi.freixa-terradas@upc.edu; Alfonso, Elsa de, E-mail: elsa.de.alfonso@upc.edu; Reventós, Francesc, E-mail: francesc.reventos@upc.edu

    2016-08-15

    Highlights: • Uncertainty of physical models are a key issue in Best estimate plus uncertainty analysis. • Estimation of uncertainties of physical models of thermal hydraulics system codes. • Comparison of CIRCÉ and FFTBM methodologies. • Simulation of reflood experiments in order to evaluate uncertainty of physical models related to the reflood scenario. - Abstract: The increasing importance of Best-Estimate Plus Uncertainty (BEPU) analyses in nuclear safety and licensing processes have lead to several international activities. The latest findings highlighted the uncertainties of physical models as one of the most controversial aspects of BEPU. This type of uncertainties is an important contributor to the total uncertainty of NPP BE calculations. Due to the complexity of estimating this uncertainty, it is often assessed solely by engineering judgment. The present study comprises a comparison of two different state-of-the-art methodologies CIRCÉ and IPREM (FFTBM) capable of quantifying the uncertainty of physical models. Similarities and differences of their results are discussed through the observation of probability distribution functions and envelope calculations. In particular, the analyzed scenario is core reflood. Experimental data from the FEBA and PERICLES test facilities is employed while the thermal hydraulic simulations are carried out with RELAP5/mod3.3. This work is undertaken under the framework of PREMIUM (Post-BEMUSE Reflood Model Input Uncertainty Methods) benchmark.

  7. Implementation ambiguity: The fifth element long lost in uncertainty budgets for land biogeochemical modeling

    Science.gov (United States)

    Tang, J.; Riley, W. J.

    2015-12-01

    Previous studies have identified four major sources of predictive uncertainty in modeling land biogeochemical (BGC) processes: (1) imperfect initial conditions (e.g., assumption of preindustrial equilibrium); (2) imperfect boundary conditions (e.g., climate forcing data); (3) parameterization (type I equifinality); and (4) model structure (type II equifinality). As if that were not enough to cause substantial sleep loss in modelers, we propose here a fifth element of uncertainty that results from implementation ambiguity that occurs when the model's mathematical description is translated into computational code. We demonstrate the implementation ambiguity using the example of nitrogen down regulation, a necessary process in modeling carbon-climate feedbacks. We show that, depending on common land BGC model interpretations of the governing equations for mineral nitrogen, there are three different implementations of nitrogen down regulation. We coded these three implementations in the ACME land model (ALM), and explored how they lead to different preindustrial and contemporary land biogeochemical states and fluxes. We also show how this implementation ambiguity can lead to different carbon-climate feedback estimates across the RCP scenarios. We conclude by suggesting how to avoid such implementation ambiguity in ESM BGC models.

  8. Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model

    Directory of Open Access Journals (Sweden)

    Kairong Lin

    2014-01-01

    Full Text Available Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM. The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE method with the shuffled complex evolution Metropolis (SCEM-UA sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high Nash-Sutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.

  9. Data assimilation techniques and modelling uncertainty in geosciences

    Directory of Open Access Journals (Sweden)

    M. Darvishi

    2014-10-01

    Full Text Available "You cannot step into the same river twice". Perhaps this ancient quote is the best phrase to describe the dynamic nature of the earth system. If we regard the earth as a several mixed systems, we want to know the state of the system at any time. The state could be time-evolving, complex (such as atmosphere or simple and finding the current state requires complete knowledge of all aspects of the system. On one hand, the Measurements (in situ and satellite data are often with errors and incomplete. On the other hand, the modelling cannot be exact; therefore, the optimal combination of the measurements with the model information is the best choice to estimate the true state of the system. Data assimilation (DA methods are powerful tools to combine observations and a numerical model. Actually, DA is an interaction between uncertainty analysis, physical modelling and mathematical algorithms. DA improves knowledge of the past, present or future system states. DA provides a forecast the state of complex systems and better scientific understanding of calibration, validation, data errors and their probability distributions. Nowadays, the high performance and capabilities of DA have led to extensive use of it in different sciences such as meteorology, oceanography, hydrology and nuclear cores. In this paper, after a brief overview of the DA history and a comparison with conventional statistical methods, investigated the accuracy and computational efficiency of two main classical algorithms of DA involving stochastic DA (BLUE and Kalman filter and variational DA (3D and 4D-Var, then evaluated quantification and modelling of the errors. Finally, some of DA applications in geosciences and the challenges facing the DA are discussed.

  10. Uncertainty of Modal Parameters Estimated by ARMA Models

    DEFF Research Database (Denmark)

    Jensen, Jakob Laigaard; Brincker, Rune; Rytter, Anders

    In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the param...

  11. Uncertainty modeling dedicated to professor Boris Kovalerchuk on his anniversary

    CERN Document Server

    2017-01-01

    This book commemorates the 65th birthday of Dr. Boris Kovalerchuk, and reflects many of the research areas covered by his work. It focuses on data processing under uncertainty, especially fuzzy data processing, when uncertainty comes from the imprecision of expert opinions. The book includes 17 authoritative contributions by leading experts.

  12. Sensitivity and uncertainty analysis for a field-scale P loss model

    Science.gov (United States)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that there are inherent uncertainties with model predictions, limited studies have addressed model prediction uncertainty. In this study we assess the effect of model input error on predict...

  13. The role of uncertainty in supply chains under dynamic modeling

    Directory of Open Access Journals (Sweden)

    M. Fera

    2017-01-01

    Full Text Available The uncertainty in the supply chains (SCs for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP. It aims at defining the best level of information of the client’s order going back through the several supply chain (SC phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i customer-oriented strategies are preferable under high volatility of demand, (ii production-focused strategies are suggested when the probability of stock-outs is high, (iii no specific location is preferable if a centralized control architecture is implemented, (iv centralization requires cooperation among partners to achieve the SC optimum point, (v the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.

  14. Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

    Directory of Open Access Journals (Sweden)

    Chicaiza E.G.

    2017-06-01

    Full Text Available Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA, trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

  15. Mesh refinement for uncertainty quantification through model reduction

    International Nuclear Information System (INIS)

    Li, Jing; Stinis, Panos

    2015-01-01

    We present a novel way of deciding when and where to refine a mesh in probability space in order to facilitate uncertainty quantification in the presence of discontinuities in random space. A discontinuity in random space makes the application of generalized polynomial chaos expansion techniques prohibitively expensive. The reason is that for discontinuous problems, the expansion converges very slowly. An alternative to using higher terms in the expansion is to divide the random space in smaller elements where a lower degree polynomial is adequate to describe the randomness. In general, the partition of the random space is a dynamic process since some areas of the random space, particularly around the discontinuity, need more refinement than others as time evolves. In the current work we propose a way to decide when and where to refine the random space mesh based on the use of a reduced model. The idea is that a good reduced model can monitor accurately, within a random space element, the cascade of activity to higher degree terms in the chaos expansion. In turn, this facilitates the efficient allocation of computational sources to the areas of random space where they are more needed. For the Kraichnan–Orszag system, the prototypical system to study discontinuities in random space, we present theoretical results which show why the proposed method is sound and numerical results which corroborate the theory

  16. Modeling Uncertainty of Directed Movement via Markov Chains

    Directory of Open Access Journals (Sweden)

    YIN Zhangcai

    2015-10-01

    Full Text Available Probabilistic time geography (PTG is suggested as an extension of (classical time geography, in order to present the uncertainty of an agent located at the accessible position by probability. This may provide a quantitative basis for most likely finding an agent at a location. In recent years, PTG based on normal distribution or Brown bridge has been proposed, its variance, however, is irrelevant with the agent's speed or divergent with the increase of the speed; so they are difficult to take into account application pertinence and stability. In this paper, a new method is proposed to model PTG based on Markov chain. Firstly, a bidirectional conditions Markov chain is modeled, the limit of which, when the moving speed is large enough, can be regarded as the Brown bridge, thus has the characteristics of digital stability. Then, the directed movement is mapped to Markov chains. The essential part is to build step length, the state space and transfer matrix of Markov chain according to the space and time position of directional movement, movement speed information, to make sure the Markov chain related to the movement speed. Finally, calculating continuously the probability distribution of the directed movement at any time by the Markov chains, it can be get the possibility of an agent located at the accessible position. Experimental results show that, the variance based on Markov chains not only is related to speed, but also is tending towards stability with increasing the agent's maximum speed.

  17. A global water supply reservoir yield model with uncertainty analysis

    International Nuclear Information System (INIS)

    Kuria, Faith W; Vogel, Richard M

    2014-01-01

    Understanding the reliability and uncertainty associated with water supply yields derived from surface water reservoirs is central for planning purposes. Using a global dataset of monthly river discharge, we introduce a generalized model for estimating the mean and variance of water supply yield, Y, expected from a reservoir for a prespecified reliability, R, and storage capacity, S assuming a flow record of length n. The generalized storage–reliability–yield (SRY) relationships reported here have numerous water resource applications ranging from preliminary water supply investigations, to economic and climate change impact assessments. An example indicates how our generalized SRY relationship can be combined with a hydroclimatic model to determine the impact of climate change on surface reservoir water supply yields. We also document that the variability of estimates of water supply yield are invariant to characteristics of the reservoir system, including its storage capacity and reliability. Standardized metrics of the variability of water supply yields are shown to depend only on the sample size of the inflows and the statistical characteristics of the inflow series. (paper)

  18. Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

    Science.gov (United States)

    Chicaiza, E. G.; Leiva, C. A.; Arranz, J. J.; Buenańo, X. E.

    2017-06-01

    Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

  19. Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

    Science.gov (United States)

    Li, L.; Xu, C.-Y.; Engeland, K.

    2012-04-01

    With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

  20. Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

    Science.gov (United States)

    Han, Feng; Zheng, Yi

    2018-06-01

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

  1. Decision-making model of generation technology under uncertainty based on real option theory

    International Nuclear Information System (INIS)

    Ming, Zeng; Ping, Zhang; Shunkun, Yu; Ge, Zhang

    2016-01-01

    Highlights: • A decision-making model of generation technology investment is proposed. • The irreversible investment concept and real option theory is introduced. • Practical data was used to prove the validity of the model. • Impact of electricity and fuel price fluctuation on investment was analyzed. - Abstract: The introduction of market competition and the increased uncertainty factors makes the generators have to decide not only on whether to invest generation capacity or not but also on what kind of generation technology to choose. In this paper, a decision-making model of generation technology investment is proposed. The irreversible investment concept and real option theory is introduced as the fundamental of the model. In order to explain the decision-making process of generator’s investment, the decision-making optimization model was built considering two generation technologies, i.e., the heat-only system and the combined heat and power generation. Also, we discussed the theory deducing process, which explained how to eliminate the overrated economic potential caused by risk hazard, based on economic evaluation of both generation technologies. Finally, practical data from electricity market of Inner Mongolia was used to prove the validity of the model and the impact of uncertainties of electricity and fuel price fluctuation on investment was analyzed according to the simulated results.

  2. Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

    Science.gov (United States)

    Wang, A.; Moore, J.C.; Cui, Xingquan; Ji, D.; Li, Q.; Zhang, N.; Wang, C.; Zhang, S.; Lawrence, D.M.; McGuire, A.D.; Zhang, W.; Delire, C.; Koven, C.; Saito, K.; MacDougall, A.; Burke, E.; Decharme, B.

    2016-01-01

     We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future

  3. Demand Uncertainty

    DEFF Research Database (Denmark)

    Nguyen, Daniel Xuyen

    This paper presents a model of trade that explains why firms wait to export and why many exporters fail. Firms face uncertain demands that are only realized after the firm enters the destination. The model retools the timing of uncertainty resolution found in productivity heterogeneity models....... This retooling addresses several shortcomings. First, the imperfect correlation of demands reconciles the sales variation observed in and across destinations. Second, since demands for the firm's output are correlated across destinations, a firm can use previously realized demands to forecast unknown demands...... in untested destinations. The option to forecast demands causes firms to delay exporting in order to gather more information about foreign demand. Third, since uncertainty is resolved after entry, many firms enter a destination and then exit after learning that they cannot profit. This prediction reconciles...

  4. Uncertainty characterization and quantification in air pollution models. Application to the ADMS-Urban model.

    Science.gov (United States)

    Debry, E.; Malherbe, L.; Schillinger, C.; Bessagnet, B.; Rouil, L.

    2009-04-01

    Evaluation of human exposure to atmospheric pollution usually requires the knowledge of pollutants concentrations in ambient air. In the framework of PAISA project, which studies the influence of socio-economical status on relationships between air pollution and short term health effects, the concentrations of gas and particle pollutants are computed over Strasbourg with the ADMS-Urban model. As for any modeling result, simulated concentrations come with uncertainties which have to be characterized and quantified. There are several sources of uncertainties related to input data and parameters, i.e. fields used to execute the model like meteorological fields, boundary conditions and emissions, related to the model formulation because of incomplete or inaccurate treatment of dynamical and chemical processes, and inherent to the stochastic behavior of atmosphere and human activities [1]. Our aim is here to assess the uncertainties of the simulated concentrations with respect to input data and model parameters. In this scope the first step consisted in bringing out the input data and model parameters that contribute most effectively to space and time variability of predicted concentrations. Concentrations of several pollutants were simulated for two months in winter 2004 and two months in summer 2004 over five areas of Strasbourg. The sensitivity analysis shows the dominating influence of boundary conditions and emissions. Among model parameters, the roughness and Monin-Obukhov lengths appear to have non neglectable local effects. Dry deposition is also an important dynamic process. The second step of the characterization and quantification of uncertainties consists in attributing a probability distribution to each input data and model parameter and in propagating the joint distribution of all data and parameters into the model so as to associate a probability distribution to the modeled concentrations. Several analytical and numerical methods exist to perform an

  5. Modeling Input Errors to Improve Uncertainty Estimates for Sediment Transport Model Predictions

    Science.gov (United States)

    Jung, J. Y.; Niemann, J. D.; Greimann, B. P.

    2016-12-01

    Bayesian methods using Markov chain Monte Carlo algorithms have recently been applied to sediment transport models to assess the uncertainty in the model predictions due to the parameter values. Unfortunately, the existing approaches can only attribute overall uncertainty to the parameters. This limitation is critical because no model can produce accurate forecasts if forced with inaccurate input data, even if the model is well founded in physical theory. In this research, an existing Bayesian method is modified to consider the potential errors in input data during the uncertainty evaluation process. The input error is modeled using Gaussian distributions, and the means and standard deviations are treated as uncertain parameters. The proposed approach is tested by coupling it to the Sedimentation and River Hydraulics - One Dimension (SRH-1D) model and simulating a 23-km reach of the Tachia River in Taiwan. The Wu equation in SRH-1D is used for computing the transport capacity for a bed material load of non-cohesive material. Three types of input data are considered uncertain: (1) the input flowrate at the upstream boundary, (2) the water surface elevation at the downstream boundary, and (3) the water surface elevation at a hydraulic structure in the middle of the reach. The benefits of modeling the input errors in the uncertainty analysis are evaluated by comparing the accuracy of the most likely forecast and the coverage of the observed data by the credible intervals to those of the existing method. The results indicate that the internal boundary condition has the largest uncertainty among those considered. Overall, the uncertainty estimates from the new method are notably different from those of the existing method for both the calibration and forecast periods.

  6. Modelling sensitivity and uncertainty in a LCA model for waste management systems - EASETECH

    DEFF Research Database (Denmark)

    Damgaard, Anders; Clavreul, Julie; Baumeister, Hubert

    2013-01-01

    In the new model, EASETECH, developed for LCA modelling of waste management systems, a general approach for sensitivity and uncertainty assessment for waste management studies has been implemented. First general contribution analysis is done through a regular interpretation of inventory and impact...

  7. Quantification of the impact of precipitation spatial distribution uncertainty on predictive uncertainty of a snowmelt runoff model

    Science.gov (United States)

    Jacquin, A. P.

    2012-04-01

    This study is intended to quantify the impact of uncertainty about precipitation spatial distribution on predictive uncertainty of a snowmelt runoff model. This problem is especially relevant in mountain catchments with a sparse precipitation observation network and relative short precipitation records. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment's glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation at a station and a precipitation factor FPi. If other precipitation data are not available, these precipitation factors must be adjusted during the calibration process and are thus seen as parameters of the model. In the case of the fifth zone, glaciers are seen as an inexhaustible source of water that melts when the snow cover is depleted.The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. The model's predictive uncertainty is measured in terms of the output variance of the mean squared error of the Box-Cox transformed discharge, the relative volumetric error, and the weighted average of snow water equivalent in the elevation zones at the end of the simulation period. Sobol's variance decomposition (SVD) method is used for assessing the impact of precipitation spatial distribution, represented by the precipitation factors FPi, on the models' predictive uncertainty. In the SVD method, the first order effect of a parameter (or group of parameters) indicates the fraction of predictive uncertainty that could be reduced if the true value of this parameter (or group) was known. Similarly, the total effect of a parameter (or group) measures the fraction of predictive uncertainty that would remain if the true value of this parameter (or group) was unknown, but all the remaining model parameters could be fixed

  8. Fast uncertainty reduction strategies relying on Gaussian process models

    International Nuclear Information System (INIS)

    Chevalier, Clement

    2013-01-01

    This work deals with sequential and batch-sequential evaluation strategies of real-valued functions under limited evaluation budget, using Gaussian process models. Optimal Stepwise Uncertainty Reduction (SUR) strategies are investigated for two different problems, motivated by real test cases in nuclear safety. First we consider the problem of identifying the excursion set above a given threshold T of a real-valued function f. Then we study the question of finding the set of 'safe controlled configurations', i.e. the set of controlled inputs where the function remains below T, whatever the value of some others non-controlled inputs. New SUR strategies are presented, together with efficient procedures and formulas to compute and use them in real world applications. The use of fast formulas to recalculate quickly the posterior mean or covariance function of a Gaussian process (referred to as the 'kriging update formulas') does not only provide substantial computational savings. It is also one of the key tools to derive closed form formulas enabling a practical use of computationally-intensive sampling strategies. A contribution in batch-sequential optimization (with the multi-points Expected Improvement) is also presented. (author)

  9. Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Zhen [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Safta, Cosmin [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sargsyan, Khachik [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Najm, Habib N. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); van Bloemen Waanders, Bart Gustaaf [Sandia National Lab. (SNL-CA), Livermore, CA (United States); LaFranchi, Brian W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ivey, Mark D. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Schrader, Paul E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Michelsen, Hope A. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Bambha, Ray P. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

    2014-09-01

    In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of green- house gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO2 . This will allow for the examination of regional-scale transport and distribution of CO2 along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO2 inversions. We have tested the approach using data and model outputs from the TransCom3 global CO2 inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses de- terministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two ap- proaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and un- certainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model - FLEXPART-WRF. These two models share the same WRF

  10. Parameter uncertainty analysis for the annual phosphorus loss estimator (APLE) model

    Science.gov (United States)

    Technical abstract: Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analys...

  11. Estimating the magnitude of prediction uncertainties for field-scale P loss models

    Science.gov (United States)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, an uncertainty analysis for the Annual P Loss Estima...

  12. Exploring uncertainty in glacier mass balance modelling with Monte Carlo simulation

    NARCIS (Netherlands)

    Machguth, H.; Purves, R.S.; Oerlemans, J.; Hoelzle, M.; Paul, F.

    2008-01-01

    By means of Monte Carlo simulations we calculated uncertainty in modelled cumulative mass balance over 400 days at one particular point on the tongue of Morteratsch Glacier, Switzerland, using a glacier energy balance model of intermediate complexity. Before uncertainty assessment, the model was

  13. An Efficient Deterministic Approach to Model-based Prediction Uncertainty

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the...

  14. Quantification of Uncertainties in Integrated Spacecraft System Models, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed effort is to investigate a novel uncertainty quantification (UQ) approach based on non-intrusive polynomial chaos (NIPC) for computationally efficient...

  15. Modeling, design, and simulation of systems with uncertainties

    CERN Document Server

    Rauh, Andreas

    2011-01-01

    This three-fold contribution to the field covers both theory and current research in algorithmic approaches to uncertainty handling, real-life applications such as robotics and biomedical engineering, and fresh approaches to reliably implementing software.

  16. Global sensitivity analysis for identifying important parameters of nitrogen nitrification and denitrification under model uncertainty and scenario uncertainty

    Science.gov (United States)

    Chen, Zhuowei; Shi, Liangsheng; Ye, Ming; Zhu, Yan; Yang, Jinzhong

    2018-06-01

    Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. By using a new variance-based global sensitivity analysis method, this paper identifies important parameters for nitrogen reactive transport with simultaneous consideration of these three uncertainties. A combination of three scenarios of soil temperature and two scenarios of soil moisture creates a total of six scenarios. Four alternative models describing the effect of soil temperature and moisture content are used to evaluate the reduction functions used for calculating actual reaction rates. The results show that for nitrogen reactive transport problem, parameter importance varies substantially among different models and scenarios. Denitrification and nitrification process is sensitive to soil moisture content status rather than to the moisture function parameter. Nitrification process becomes more important at low moisture content and low temperature. However, the changing importance of nitrification activity with respect to temperature change highly relies on the selected model. Model-averaging is suggested to assess the nitrification (or denitrification) contribution by reducing the possible model error. Despite the introduction of biochemical heterogeneity or not, fairly consistent parameter importance rank is obtained in this study: optimal denitrification rate (Kden) is the most important parameter; reference temperature (Tr) is more important than temperature coefficient (Q10); empirical constant in moisture response function (m) is the least important one. Vertical distribution of soil moisture but not temperature plays predominant role controlling nitrogen reaction. This study provides insight into the nitrogen reactive transport modeling and demonstrates an effective strategy of selecting the important parameters when future temperature and soil moisture carry uncertainties or when modelers face with multiple ways of establishing nitrogen

  17. Can Bayesian Belief Networks help tackling conceptual model uncertainties in contaminated site risk assessment?

    DEFF Research Database (Denmark)

    Troldborg, Mads; Thomsen, Nanna Isbak; McKnight, Ursula S.

    different conceptual models may describe the same contaminated site equally well. In many cases, conceptual model uncertainty has been shown to be one of the dominant sources for uncertainty and is therefore essential to account for when quantifying uncertainties in risk assessments. We present here......A key component in risk assessment of contaminated sites is the formulation of a conceptual site model. The conceptual model is a simplified representation of reality and forms the basis for the mathematical modelling of contaminant fate and transport at the site. A conceptual model should...... a Bayesian Belief Network (BBN) approach for evaluating the uncertainty in risk assessment of groundwater contamination from contaminated sites. The approach accounts for conceptual model uncertainty by considering multiple conceptual models, each of which represents an alternative interpretation of the site...

  18. Generic uncertainty model for DETRA for environmental consequence analyses. Application and sample outputs

    International Nuclear Information System (INIS)

    Suolanen, V.; Ilvonen, M.

    1998-10-01

    Computer model DETRA applies a dynamic compartment modelling approach. The compartment structure of each considered application can be tailored individually. This flexible modelling method makes it possible that the transfer of radionuclides can be considered in various cases: aquatic environment and related food chains, terrestrial environment, food chains in general and food stuffs, body burden analyses of humans, etc. In the former study on this subject, modernization of the user interface of DETRA code was carried out. This new interface works in Windows environment and the usability of the code has been improved. The objective of this study has been to further develop and diversify the user interface so that also probabilistic uncertainty analyses can be performed by DETRA. The most common probability distributions are available: uniform, truncated Gaussian and triangular. The corresponding logarithmic distributions are also available. All input data related to a considered case can be varied, although this option is seldomly needed. The calculated output values can be selected as monitored values at certain simulation time points defined by the user. The results of a sensitivity run are immediately available after simulation as graphical presentations. These outcomes are distributions generated for varied parameters, density functions of monitored parameters and complementary cumulative density functions (CCDF). An application considered in connection with this work was the estimation of contamination of milk caused by radioactive deposition of Cs (10 kBq(Cs-137)/m 2 ). The multi-sequence calculation model applied consisted of a pasture modelling part and a dormant season modelling part. These two sequences were linked periodically simulating the realistic practice of care taking of domestic animals in Finland. The most important parameters were varied in this exercise. The performed diversifying of the user interface of DETRA code seems to provide an easily

  19. Uncertainty modelling and code calibration for composite materials

    DEFF Research Database (Denmark)

    Toft, Henrik Stensgaard; Branner, Kim; Mishnaevsky, Leon, Jr

    2013-01-01

    and measurement uncertainties which are introduced on the different scales. Typically, these uncertainties are taken into account in the design process using characteristic values and partial safety factors specified in a design standard. The value of the partial safety factors should reflect a reasonable balance...... to wind turbine blades are calibrated for two typical lay-ups using a large number of load cases and ratios between the aerodynamic forces and the inertia forces....

  20. Mass discharge estimation from contaminated sites: Multi-model solutions for assessment of conceptual uncertainty

    DEFF Research Database (Denmark)

    Thomsen, Nanna Isbak; Troldborg, Mads; McKnight, Ursula S.

    2012-01-01

    site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level...... the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We...... propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same...

  1. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

    NARCIS (Netherlands)

    Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.

    2015-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We

  2. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty (discussion paper)

    NARCIS (Netherlands)

    Pande, S.; Arkesteijn, L.; Savenije, H.H.G.; Bastidas, L.A.

    2015-01-01

    This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is

  3. A review of different perspectives on uncertainty and risk and an alternative modeling paradigm

    International Nuclear Information System (INIS)

    Samson, Sundeep; Reneke, James A.; Wiecek, Margaret M.

    2009-01-01

    The literature in economics, finance, operations research, engineering and in general mathematics is first reviewed on the subject of defining uncertainty and risk. The review goes back to 1901. Different perspectives on uncertainty and risk are examined and a new paradigm to model uncertainty and risk is proposed using relevant ideas from this study. This new paradigm is used to represent, aggregate and propagate uncertainty and interpret the resulting variability in a challenge problem developed by Oberkampf et al. [2004, Challenge problems: uncertainty in system response given uncertain parameters. Reliab Eng Syst Safety 2004; 85(1): 11-9]. The challenge problem is further extended into a decision problem that is treated within a multicriteria decision making framework to illustrate how the new paradigm yields optimal decisions under uncertainty. The accompanying risk is defined as the probability of an unsatisfactory system response quantified by a random function of the uncertainty

  4. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  5. Impact of Damping Uncertainty on SEA Model Response Variance

    Science.gov (United States)

    Schiller, Noah; Cabell, Randolph; Grosveld, Ferdinand

    2010-01-01

    Statistical Energy Analysis (SEA) is commonly used to predict high-frequency vibroacoustic levels. This statistical approach provides the mean response over an ensemble of random subsystems that share the same gross system properties such as density, size, and damping. Recently, techniques have been developed to predict the ensemble variance as well as the mean response. However these techniques do not account for uncertainties in the system properties. In the present paper uncertainty in the damping loss factor is propagated through SEA to obtain more realistic prediction bounds that account for both ensemble and damping variance. The analysis is performed on a floor-equipped cylindrical test article that resembles an aircraft fuselage. Realistic bounds on the damping loss factor are determined from measurements acquired on the sidewall of the test article. The analysis demonstrates that uncertainties in damping have the potential to significantly impact the mean and variance of the predicted response.

  6. Uncertainty in the learning rates of energy technologies. An experiment in a global multi-regional energy system model

    International Nuclear Information System (INIS)

    Rout, Ullash K.; Blesl, Markus; Fahl, Ulrich; Remme, Uwe; Voss, Alfred

    2009-01-01

    The diffusion of promising energy technologies in the market depends on their future energy production-cost development. When analyzing these technologies in an integrated assessment model using endogenous technological learning, the uncertainty in the assumed learning rates (LRs) plays a crucial role in the production-cost development and model outcomes. This study examines the uncertainty in LRs of some energy technologies under endogenous global learning implementation and presents a floor-cost modeling procedure to systematically regulate the uncertainty in LRs of energy technologies. The article narrates the difficulties of data assimilation, as compatible with mixed integer programming segmentations, and comprehensively presents the causes of uncertainty in LRs. This work is executed using a multi-regional and long-horizon energy system model based on 'TIMES' framework. All regions receive an economic advantage to learn in a common domain, and resource-ample regions obtain a marginal advantage for better exploitation of the learning technologies, due to a lower supply-side fuel-cost development. The lowest learning investment associated with the maximum LR mobilizes more deployment of the learning technologies. The uncertainty in LRs has an impact on the diffusion of energy technologies tested, and therefore this study scrutinizes the role of policy support for some of the technologies investigated. (author)

  7. Uncertainty modelling and analysis of environmental systems: a river sediment yield example

    NARCIS (Netherlands)

    Keesman, K.J.; Koskela, J.; Guillaume, J.H.; Norton, J.P.; Croke, B.; Jakeman, A.

    2011-01-01

    Abstract: Throughout the last decades uncertainty analysis has become an essential part of environmental model building (e.g. Beck 1987; Refsgaard et al., 2007). The objective of the paper is to introduce stochastic and setmembership uncertainty modelling concepts, which basically differ in the

  8. 'spup' - An R package for uncertainty propagation in spatial environmental modelling

    NARCIS (Netherlands)

    Sawicka, K.; Heuvelink, G.B.M.

    2016-01-01

    Computer models are crucial tools in engineering and environmental sciences for simulating the behaviour of complex systems. While many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Advances in uncertainty analysis

  9. Uncertainty in the environmental modelling process – A framework and guidance

    NARCIS (Netherlands)

    Refsgaard, J.C.; van der Sluijs, J.P.|info:eu-repo/dai/nl/073427489; Hojberg, A.L.; Vanrolleghem, P.

    2007-01-01

    A terminology and typology of uncertainty is presented together with a framework for the modelling process, its interaction with the broader water management process and the role of uncertainty at different stages in the modelling processes. Brief reviews have been made of 14 different (partly

  10. Leaf area index uncertainty estimates for model-data fusion applications

    Science.gov (United States)

    Andrew D. Richardson; D. Bryan Dail; D.Y. Hollinger

    2011-01-01

    Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux...

  11. Uncertainty in eddy covariance measurements and its application to physiological models

    Science.gov (United States)

    D.Y. Hollinger; A.D. Richardson; A.D. Richardson

    2005-01-01

    Flux data are noisy, and this uncertainty is largely due to random measurement error. Knowledge of uncertainty is essential for the statistical evaluation of modeled andmeasured fluxes, for comparison of parameters derived by fitting models to measured fluxes and in formal data-assimilation efforts. We used the difference between simultaneous measurements from two...

  12. Quantifying uncertainty of geological 3D layer models, constructed with a-priori geological expertise

    NARCIS (Netherlands)

    Gunnink, J.J.; Maljers, D.; Hummelman, J.

    2010-01-01

    Uncertainty quantification of geological models that are constructed with additional geological expert-knowledge is not straightforward. To construct sound geological 3D layer models we use a lot of additional knowledge, with an uncertainty that is hard to quantify. Examples of geological expert

  13. Uncertainties in modelling the spatial and temporal variations in aerosol concentrations

    NARCIS (Netherlands)

    Meij, de A.

    2009-01-01

    Aerosols play a key role in air quality (health aspects) and climate. In this thesis atmospheric chemistry transport models are used to study the uncertainties in aerosol modelling and to evaluate the effects of emission reduction scenarios on air quality. Uncertainties in: the emissions of gas and

  14. A global model for residential energy use: Uncertainty in calibration to regional data

    International Nuclear Information System (INIS)

    van Ruijven, Bas; van Vuuren, Detlef P.; de Vries, Bert; van der Sluijs, Jeroen P.

    2010-01-01

    Uncertainties in energy demand modelling allow for the development of different models, but also leave room for different calibrations of a single model. We apply an automated model calibration procedure to analyse calibration uncertainty of residential sector energy use modelling in the TIMER 2.0 global energy model. This model simulates energy use on the basis of changes in useful energy intensity, technology development (AEEI) and price responses (PIEEI). We find that different implementations of these factors yield behavioural model results. Model calibration uncertainty is identified as influential source for variation in future projections: amounting 30% to 100% around the best estimate. Energy modellers should systematically account for this and communicate calibration uncertainty ranges. (author)

  15. 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 0.18uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.

  16. Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data (Invited)

    DEFF Research Database (Denmark)

    Minsley, Burke; Christensen, Nikolaj Kruse; Christensen, Steen

    of airborne electromagnetic (AEM) data to estimate large-scale model structural geometry, i.e. the spatial distribution of different lithological units based on assumed or estimated resistivity-lithology relationships, and the uncertainty in those structures given imperfect measurements. Geophysically derived...... estimates of model structural uncertainty are then combined with hydrologic observations to assess the impact of model structural error on hydrologic calibration and prediction errors. Using a synthetic numerical model, we describe a sequential hydrogeophysical approach that: (1) uses Bayesian Markov chain...... Monte Carlo (McMC) methods to produce a robust estimate of uncertainty in electrical resistivity parameter values, (2) combines geophysical parameter uncertainty estimates with borehole observations of lithology to produce probabilistic estimates of model structural uncertainty over the entire AEM...

  17. 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.

  18. Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows

    Science.gov (United States)

    Schaefer, John; West, Thomas; Hosder, Serhat; Rumsey, Christopher; Carlson, Jan-Renee; Kleb, William

    2015-01-01

    The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.

  19. 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.

  20. [Uncertainty analysis of ecological risk assessment caused by heavy-metals deposition from MSWI emission].

    Science.gov (United States)

    Liao, Zhi-Heng; Sun, Jia-Ren; Wu, Dui; Fan, Shao-Jia; Ren, Ming-Zhong; Lü, Jia-Yang

    2014-06-01

    The CALPUFF model was applied to simulate the ground-level atmospheric concentrations of Pb and Cd from municipal solid waste incineration (MSWI) plants, and the soil concentration model was used to estimate soil concentration increments after atmospheric deposition based on Monte Carlo simulation, then ecological risk assessment was conducted by the potential ecological risk index method. The results showed that the largest atmospheric concentrations of Pb and Cd were 5.59 x 109-3) microg x m(-3) and 5.57 x 10(-4) microg x m(-3), respectively, while the maxima of soil concentration incremental medium of Pb and Cd were 2.26 mg x kg(-1) and 0.21 mg x kg(-1), respectively; High risk areas were located next to the incinerators, Cd contributed the most to the ecological risk, and Pb was basically free of pollution risk; Higher ecological hazard level was predicted at the most polluted point in urban areas with a 55.30% probability, while in rural areas, the most polluted point was assessed to moderate ecological hazard level with a 72.92% probability. In addition, sensitivity analysis of calculation parameters in the soil concentration model was conducted, which showed the simulated results of urban and rural area were most sensitive to soil mix depth and dry deposition rate, respectively.

  1. Assessment of structural model and parameter uncertainty with a multi-model system for soil water balance models

    Science.gov (United States)

    Michalik, Thomas; Multsch, Sebastian; Frede, Hans-Georg; Breuer, Lutz

    2016-04-01

    Water for agriculture is strongly limited in arid and semi-arid regions and often of low quality in terms of salinity. The application of saline waters for irrigation increases the salt load in the rooting zone and has to be managed by leaching to maintain a healthy soil, i.e. to wash out salts by additional irrigation. Dynamic simulation models are helpful tools to calculate the root zone water fluxes and soil salinity content in order to investigate best management practices. However, there is little information on structural and parameter uncertainty for simulations regarding the water and salt balance of saline irrigation. Hence, we established a multi-model system with four different models (AquaCrop, RZWQM, SWAP, Hydrus1D/UNSATCHEM) to analyze the structural and parameter uncertainty by using the Global Likelihood and Uncertainty Estimation (GLUE) method. Hydrus1D/UNSATCHEM and SWAP were set up with multiple sets of different implemented functions (e.g. matric and osmotic stress for root water uptake) which results in a broad range of different model structures. The simulations were evaluated against soil water and salinity content observations. The posterior distribution of the GLUE analysis gives behavioral parameters sets and reveals uncertainty intervals for parameter uncertainty. Throughout all of the model sets, most parameters accounting for the soil water balance show a low uncertainty, only one or two out of five to six parameters in each model set displays a high uncertainty (e.g. pore-size distribution index in SWAP and Hydrus1D/UNSATCHEM). The differences between the models and model setups reveal the structural uncertainty. The highest structural uncertainty is observed for deep percolation fluxes between the model sets of Hydrus1D/UNSATCHEM (~200 mm) and RZWQM (~500 mm) that are more than twice as high for the latter. The model sets show a high variation in uncertainty intervals for deep percolation as well, with an interquartile range (IQR) of

  2. Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation

    Energy Technology Data Exchange (ETDEWEB)

    Alam, Azimil Gani; Rahman, Haolia; Kim, Jung-Kyung; Han, Hwataik [Kookmin University, Seoul (Korea, Republic of)

    2017-05-15

    Demand control ventilation is employed to save energy by adjusting airflow rate according to the ventilation load of a building. This paper investigates a method for occupancy estimation by using a dynamic neural network model based on carbon dioxide concentration in an occupied zone. The method can be applied to most commercial and residential buildings where human effluents to be ventilated. An indoor simulation program CONTAMW is used to generate indoor CO{sub 2} data corresponding to various occupancy schedules and airflow patterns to train neural network models. Coefficients of variation are obtained depending on the complexities of the physical parameters as well as the system parameters of neural networks, such as the numbers of hidden neurons and tapped delay lines. We intend to identify the uncertainties caused by the model parameters themselves, by excluding uncertainties in input data inherent in measurement. Our results show estimation accuracy is highly influenced by the frequency of occupancy variation but not significantly influenced by fluctuation in the airflow rate. Furthermore, we discuss the applicability and validity of the present method based on passive environmental conditions for estimating occupancy in a room from the viewpoint of demand control ventilation applications.

  3. Uncertainty modelling of critical column buckling for reinforced ...

    Indian Academy of Sciences (India)

    Buckling is a critical issue for structural stability in structural design. In most of the buckling analyses, applied loads, structural and material properties are considered certain. However, in reality, these parameters are uncertain. Therefore, a prognostic solution is necessary and uncertainties have to be considered. Fuzzy logic ...

  4. A Multi-objective Model for Transmission Planning Under Uncertainties

    DEFF Research Database (Denmark)

    Zhang, Chunyu; Wang, Qi; Ding, Yi

    2014-01-01

    The significant growth of distributed energy resources (DERs) associated with smart grid technologies has prompted excessive uncertainties in the transmission system. The most representative is the novel notation of commercial aggregator who has lighted a bright way for DERs to participate power...

  5. Estimation of Model Uncertainties in Closed-loop Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    This paper describe a method for estimation of parameters or uncertainties in closed-loop systems. The method is based on an application of the dual YJBK (after Youla, Jabr, Bongiorno and Kucera) parameterization of all systems stabilized by a given controller. The dual YJBK transfer function...

  6. Hydrological model uncertainty due to spatial evapotranspiration estimation methods

    Czech Academy of Sciences Publication Activity Database

    Yu, X.; Lamačová, Anna; Duffy, Ch.; Krám, P.; Hruška, Jakub

    2016-01-01

    Roč. 90, part B (2016), s. 90-101 ISSN 0098-3004 R&D Projects: GA MŠk(CZ) LO1415 Institutional support: RVO:67179843 Keywords : Uncertainty * Evapotranspiration * Forest management * PIHM * Biome-BGC Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 2.533, year: 2016

  7. Uncertainty Assessment in Long Term Urban Drainage Modelling

    DEFF Research Database (Denmark)

    Thorndahl, Søren

    the probability of system failures (defined as either flooding or surcharge of manholes or combined sewer overflow); (2) an application of the Generalized Likelihood Uncertainty Estimation methodology in which an event based stochastic calibration is performed; and (3) long term Monte Carlo simulations...

  8. Uncertainty Quantification given Discontinuous Climate Model Response and a Limited Number of Model Runs

    Science.gov (United States)

    Sargsyan, K.; Safta, C.; Debusschere, B.; Najm, H.

    2010-12-01

    Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of

  9. Quantification of Wave Model Uncertainties Used for Probabilistic Reliability Assessments of Wave Energy Converters

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  10. Determination of Wave Model Uncertainties used for Probabilistic Reliability Assessments of Wave Energy Devices

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  11. On Evaluation of Recharge Model Uncertainty: a Priori and a Posteriori

    International Nuclear Information System (INIS)

    Ming Ye; Karl Pohlmann; Jenny Chapman; David Shafer

    2006-01-01

    Hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions. Hydrologic analyses typically rely on a single conceptual-mathematical model, which ignores conceptual model uncertainty and may result in bias in predictions and under-estimation of predictive uncertainty. This study is to assess conceptual model uncertainty residing in five recharge models developed to date by different researchers based on different theories for Nevada and Death Valley area, CA. A recently developed statistical method, Maximum Likelihood Bayesian Model Averaging (MLBMA), is utilized for this analysis. In a Bayesian framework, the recharge model uncertainty is assessed, a priori, using expert judgments collected through an expert elicitation in the form of prior probabilities of the models. The uncertainty is then evaluated, a posteriori, by updating the prior probabilities to estimate posterior model probability. The updating is conducted through maximum likelihood inverse modeling by calibrating the Death Valley Regional Flow System (DVRFS) model corresponding to each recharge model against observations of head and flow. Calibration results of DVRFS for the five recharge models are used to estimate three information criteria (AIC, BIC, and KIC) used to rank and discriminate these models. Posterior probabilities of the five recharge models, evaluated using KIC, are used as weights to average head predictions, which gives posterior mean and variance. The posterior quantities incorporate both parametric and conceptual model uncertainties

  12. Uncertainty in parameterisation and model structure affect simulation results in coupled ecohydrological models

    Directory of Open Access Journals (Sweden)

    S. Arnold

    2009-10-01

    Full Text Available In this paper we develop and apply a conceptual ecohydrological model to investigate the effects of model structure and parameter uncertainty on the simulation of vegetation structure and hydrological dynamics. The model is applied for a typical water limited riparian ecosystem along an ephemeral river: the middle section of the Kuiseb River in Namibia. We modelled this system by coupling an ecological model with a conceptual hydrological model. The hydrological model is storage based with stochastical forcing from the flood. The ecosystem is modelled with a population model, and represents three dominating riparian plant populations. In appreciation of uncertainty about population dynamics, we applied three model versions with increasing complexity. Population parameters were found by Latin hypercube sampling of the parameter space and with the constraint that three species should coexist as observed. Two of the three models were able to reproduce the observed coexistence. However, both models relied on different coexistence mechanisms, and reacted differently to change of long term memory in the flood forcing. The coexistence requirement strongly constrained the parameter space for both successful models. Only very few parameter sets (0.5% of 150 000 samples allowed for coexistence in a representative number of repeated simulations (at least 10 out of 100 and the success of the coexistence mechanism was controlled by the combination of population parameters. The ensemble statistics of average values of hydrologic variables like transpiration and depth to ground water were similar for both models, suggesting that they were mainly controlled by the applied hydrological model. The ensemble statistics of the fluctuations of depth to groundwater and transpiration, however, differed significantly, suggesting that they were controlled by the applied ecological model and coexistence mechanisms. Our study emphasizes that uncertainty about ecosystem

  13. A model of entry-exit decisions and capacity choice under demand uncertainty

    OpenAIRE

    Isik, Murat; Coble, Keith H.; Hudson, Darren; House, Lisa O.

    2003-01-01

    Many investment decisions of agribusiness firms, such as when to invest in an emerging market or whether to expand the capacity of the firm, involve irreversible investment and uncertainty about demand, cost or competition. This paper uses an option-value model to examine the factors affecting an agribusiness firm's decision whether and how much to invest in an emerging market under demand uncertainty. Demand uncertainty and irreversibility of investment make investment less desirable than th...

  14. Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

    Science.gov (United States)

    Aguilo, Miguel A.; Warner, James E.

    2017-01-01

    This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

  15. Decreasing Kd uncertainties through the application of thermodynamic sorption models

    International Nuclear Information System (INIS)

    Domènech, Cristina; García, David; Pękala, Marek

    2015-01-01

    Radionuclide retardation processes during transport are expected to play an important role in the safety assessment of subsurface disposal facilities for radioactive waste. The linear distribution coefficient (K d ) is often used to represent radionuclide retention, because analytical solutions to the classic advection–diffusion-retardation equation under simple boundary conditions are readily obtainable, and because numerical implementation of this approach is relatively straightforward. For these reasons, the K d approach lends itself to probabilistic calculations required by Performance Assessment (PA) calculations. However, it is widely recognised that K d values derived from laboratory experiments generally have a narrow field of validity, and that the uncertainty of the K d outside this field increases significantly. Mechanistic multicomponent geochemical simulators can be used to calculate K d values under a wide range of conditions. This approach is powerful and flexible, but requires expert knowledge on the part of the user. The work presented in this paper aims to develop a simplified approach of estimating K d values whose level of accuracy would be comparable with those obtained by fully-fledged geochemical simulators. The proposed approach consists of deriving simplified algebraic expressions by combining relevant mass action equations. This approach was applied to three distinct geochemical systems involving surface complexation and ion-exchange processes. Within bounds imposed by model simplifications, the presented approach allows radionuclide K d values to be estimated as a function of key system-controlling parameters, such as the pH and mineralogy. This approach could be used by PA professionals to assess the impact of key geochemical parameters on the variability of radionuclide K d values. Moreover, the presented approach could be relatively easily implemented in existing codes to represent the influence of temporal and spatial changes in

  16. Context, Experience, Expectation, and Action—Towards an Empirically Grounded, General Model for Analyzing Biographical Uncertainty

    Directory of Open Access Journals (Sweden)

    Herwig Reiter

    2010-01-01

    Full Text Available The article proposes a general, empirically grounded model for analyzing biographical uncertainty. The model is based on findings from a qualitative-explorative study of transforming meanings of unemployment among young people in post-Soviet Lithuania. In a first step, the particular features of the uncertainty puzzle in post-communist youth transitions are briefly discussed. A historical event like the collapse of state socialism in Europe, similar to the recent financial and economic crisis, is a generator of uncertainty par excellence: it undermines the foundations of societies and the taken-for-grantedness of related expectations. Against this background, the case of a young woman and how she responds to the novel threat of unemployment in the transition to the world of work is introduced. Her uncertainty management in the specific time perspective of certainty production is then conceptually rephrased by distinguishing three types or levels of biographical uncertainty: knowledge, outcome, and recognition uncertainty. Biographical uncertainty, it is argued, is empirically observable through the analysis of acting and projecting at the biographical level. The final part synthesizes the empirical findings and the conceptual discussion into a stratification model of biographical uncertainty as a general tool for the biographical analysis of uncertainty phenomena. URN: urn:nbn:de:0114-fqs100120

  17. Review of strategies for handling geological uncertainty in groundwater flow and transport modeling

    DEFF Research Database (Denmark)

    Refsgaard, Jens Christian; Christensen, Steen; Sonnenborg, Torben O.

    2012-01-01

    parameters; and (c) model parameters including local scale heterogeneity. The most common methodologies for uncertainty assessments within each of these categories, such as multiple modeling, Monte Carlo analysis, regression analysis and moment equation approach, are briefly described with emphasis...

  18. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

    Science.gov (United States)

    Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multisp...

  19. Statistical methodology for discrete fracture model - including fracture size, orientation uncertainty together with intensity uncertainty and variability

    International Nuclear Information System (INIS)

    Darcel, C.; Davy, P.; Le Goc, R.; Dreuzy, J.R. de; Bour, O.

    2009-11-01

    the other, addresses the issue of the nature of the transition. We develop a new 'mechanistic' model that could help in modeling why and where this transition can occur. The transition between both regimes would occur for a fracture length of 1-10 m and even at a smaller scale for the few outcrops that follow the self-similar density model. A consequence for the disposal issue is that the model that is likely to apply in the 'blind' scale window between 10-100 m is the self-similar model as it is defined for large-scale lineaments. The self-similar model, as it is measured for some outcrops and most lineament maps, is definitely worth being investigated as a reference for scales above 1-10 m. In the rest of the report, we develop a methodology for incorporating uncertainty and variability into the DFN modeling. Fracturing properties arise from complex processes which produce an intrinsic variability; characterizing this variability as an admissible variation of model parameter or as the division of the site into subdomains with distinct DFN models is a critical point of the modeling effort. Moreover, the DFN model encompasses a part of uncertainty, due to data inherent uncertainties and sampling limits. Both effects must be quantified and incorporated into the DFN site model definition process. In that context, all available borehole data including recording of fracture intercept positions, pole orientation and relative uncertainties are used as the basis for the methodological development and further site model assessment. An elementary dataset contains a set of discrete fracture intercepts from which a parent orientation/density distribution can be computed. The elementary bricks of the site, from which these initial parent density distributions are computed, rely on the former Single Hole Interpretation division of the boreholes into sections whose local boundaries are expected to reflect - locally - geology and fracturing properties main characteristics. From that

  20. Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide.

    Science.gov (United States)

    Bilcke, Joke; Beutels, Philippe; Brisson, Marc; Jit, Mark

    2011-01-01

    Accounting for uncertainty is now a standard part of decision-analytic modeling and is recommended by many health technology agencies and published guidelines. However, the scope of such analyses is often limited, even though techniques have been developed for presenting the effects of methodological, structural, and parameter uncertainty on model results. To help bring these techniques into mainstream use, the authors present a step-by-step guide that offers an integrated approach to account for different kinds of uncertainty in the same model, along with a checklist for assessing the way in which uncertainty has been incorporated. The guide also addresses special situations such as when a source of uncertainty is difficult to parameterize, resources are limited for an ideal exploration of uncertainty, or evidence to inform the model is not available or not reliable. for identifying the sources of uncertainty that influence results most are also described. Besides guiding analysts, the guide and checklist may be useful to decision makers who need to assess how well uncertainty has been accounted for in a decision-analytic model before using the results to make a decision.

  1. Uncertainty characterization and quantification in air pollution models. Application to the CHIMERE model

    Science.gov (United States)

    Debry, Edouard; Mallet, Vivien; Garaud, Damien; Malherbe, Laure; Bessagnet, Bertrand; Rouïl, Laurence

    2010-05-01

    Prev'Air is the French operational system for air pollution forecasting. It is developed and maintained by INERIS with financial support from the French Ministry for Environment. On a daily basis it delivers forecasts up to three days ahead for ozone, nitrogene dioxide and particles over France and Europe. Maps of concentration peaks and daily averages are freely available to the general public. More accurate data can be provided to customers and modelers. Prev'Air forecasts are based on the Chemical Transport Model CHIMERE. French authorities rely more and more on this platform to alert the general public in case of high pollution events and to assess the efficiency of regulation measures when such events occur. For example the road speed limit may be reduced in given areas when the ozone level exceeds one regulatory threshold. These operational applications require INERIS to assess the quality of its forecasts and to sensitize end users about the confidence level. Indeed concentrations always remain an approximation of the true concentrations because of the high uncertainty on input data, such as meteorological fields and emissions, because of incomplete or inaccurate representation of physical processes, and because of efficiencies in numerical integration [1]. We would like to present in this communication the uncertainty analysis of the CHIMERE model led in the framework of an INERIS research project aiming, on the one hand, to assess the uncertainty of several deterministic models and, on the other hand, to propose relevant indicators describing air quality forecast and their uncertainty. There exist several methods to assess the uncertainty of one model. Under given assumptions the model may be differentiated into an adjoint model which directly provides the concentrations sensitivity to given parameters. But so far Monte Carlo methods seem to be the most widely and oftenly used [2,3] as they are relatively easy to implement. In this framework one

  2. Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model

    DEFF Research Database (Denmark)

    Zhang, Donghua; Madsen, Henrik; Ridler, Marc E.

    2015-01-01

    The ensemble Kalman filter (EnKF) is a popular data assimilation (DA) technique that has been extensively used in environmental sciences for combining complementary information from model predictions and observations. One of the major challenges in EnKF applications is the description of model un...... with respect to performance and sensitivity. Results show that inappropriate definition of model uncertainty can greatly degrade the assimilation performance, and an appropriate combination of different model uncertainty sources is advised....

  3. Managing structural uncertainty in health economic decision models: a discrepancy approach

    OpenAIRE

    Strong, M.; Oakley, J.; Chilcott, J.

    2012-01-01

    Healthcare resource allocation decisions are commonly informed by computer model predictions of population mean costs and health effects. It is common to quantify the uncertainty in the prediction due to uncertain model inputs, but methods for quantifying uncertainty due to inadequacies in model structure are less well developed. We introduce an example of a model that aims to predict the costs and health effects of a physical activity promoting intervention. Our goal is to develop a framewor...

  4. Reliability of a new biokinetic model of zirconium in internal dosimetry: part I, parameter uncertainty analysis.

    Science.gov (United States)

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

    2011-12-01

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

  5. How uncertainty in socio-economic variables affects large-scale transport model forecasts

    DEFF Research Database (Denmark)

    Manzo, Stefano; Nielsen, Otto Anker; Prato, Carlo Giacomo

    2015-01-01

    A strategic task assigned to large-scale transport models is to forecast the demand for transport over long periods of time to assess transport projects. However, by modelling complex systems transport models have an inherent uncertainty which increases over time. As a consequence, the longer...... the period forecasted the less reliable is the forecasted model output. Describing uncertainty propagation patterns over time is therefore important in order to provide complete information to the decision makers. Among the existing literature only few studies analyze uncertainty propagation patterns over...

  6. Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

    Energy Technology Data Exchange (ETDEWEB)

    Cole, Charles R.; Bergeron, Marcel P.; Murray, Christopher J.; Thorne, Paul D.; Wurstner, Signe K.; Rogers, Phillip M.

    2001-11-09

    Pacific Northwest National Laboratory (PNNL) embarked on a new initiative to strengthen the technical defensibility of the predictions being made with a site-wide groundwater flow and transport model at the U.S. Department of Energy Hanford Site in southeastern Washington State. In FY 2000, the focus of the initiative was on the characterization of major uncertainties in the current conceptual model that would affect model predictions. The long-term goals of the initiative are the development and implementation of an uncertainty estimation methodology in future assessments and analyses using the site-wide model. This report focuses on the development and implementation of an uncertainty analysis framework.

  7. On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

    Directory of Open Access Journals (Sweden)

    M. Migliavacca

    2012-06-01

    Full Text Available Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere.

    Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity.

    Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements.

    We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO2 emissions vs. low CO2 emissions scenario. Parameter uncertainty was the smallest (average 95% Confidence Interval – CI: 2.4 days century−1 for scenario B1 and 4.5 days century−1 for A1fi, whereas driver uncertainty was the largest (up to 8.4 days century−1 in the simulated trends. The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century−1 for A1fi, ±3.6 days century−1 for B1. The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per

  8. Statistical methodology for discrete fracture model - including fracture size, orientation uncertainty together with intensity uncertainty and variability

    Energy Technology Data Exchange (ETDEWEB)

    Darcel, C. (Itasca Consultants SAS (France)); Davy, P.; Le Goc, R.; Dreuzy, J.R. de; Bour, O. (Geosciences Rennes, UMR 6118 CNRS, Univ. def Rennes, Rennes (France))

    2009-11-15

    the lineament scale (k{sub t} = 2) on the other, addresses the issue of the nature of the transition. We develop a new 'mechanistic' model that could help in modeling why and where this transition can occur. The transition between both regimes would occur for a fracture length of 1-10 m and even at a smaller scale for the few outcrops that follow the self-similar density model. A consequence for the disposal issue is that the model that is likely to apply in the 'blind' scale window between 10-100 m is the self-similar model as it is defined for large-scale lineaments. The self-similar model, as it is measured for some outcrops and most lineament maps, is definitely worth being investigated as a reference for scales above 1-10 m. In the rest of the report, we develop a methodology for incorporating uncertainty and variability into the DFN modeling. Fracturing properties arise from complex processes which produce an intrinsic variability; characterizing this variability as an admissible variation of model parameter or as the division of the site into subdomains with distinct DFN models is a critical point of the modeling effort. Moreover, the DFN model encompasses a part of uncertainty, due to data inherent uncertainties and sampling limits. Both effects must be quantified and incorporated into the DFN site model definition process. In that context, all available borehole data including recording of fracture intercept positions, pole orientation and relative uncertainties are used as the basis for the methodological development and further site model assessment. An elementary dataset contains a set of discrete fracture intercepts from which a parent orientation/density distribution can be computed. The elementary bricks of the site, from which these initial parent density distributions are computed, rely on the former Single Hole Interpretation division of the boreholes into sections whose local boundaries are expected to reflect - locally - geology

  9. Error and Uncertainty Analysis for Ecological Modeling and Simulation

    Science.gov (United States)

    2001-12-01

    nitrate flux to the Gulf of Mexico. Nature (Brief Communication) 414: 166-167. (Uncertainty analysis done with SERDP software) Gertner, G., G...D. Goolsby 2001. Relating N inputs to the Mississippi River Basin and nitrate flux in the Lower Mississippi River: A comparison of approaches...Journal of Remote Sensing, 25(4):367-380. Wu, J., D.E. Jelinski, M. Luck, and P.T. Tueller, 2000. Multiscale analysis of landscape heterogeneity: scale

  10. Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

    Science.gov (United States)

    Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

    2013-04-01

    SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

  11. Exploring entropic uncertainty relation in the Heisenberg XX model with inhomogeneous magnetic field

    Science.gov (United States)

    Huang, Ai-Jun; Wang, Dong; Wang, Jia-Ming; Shi, Jia-Dong; Sun, Wen-Yang; Ye, Liu

    2017-08-01

    In this work, we investigate the quantum-memory-assisted entropic uncertainty relation in a two-qubit Heisenberg XX model with inhomogeneous magnetic field. It has been found that larger coupling strength J between the two spin-chain qubits can effectively reduce the entropic uncertainty. Besides, we observe the mechanics of how the inhomogeneous field influences the uncertainty, and find out that when the inhomogeneous field parameter b1. Intriguingly, the entropic uncertainty can shrink to zero when the coupling coefficients are relatively large, while the entropic uncertainty only reduces to 1 with the increase of the homogeneous magnetic field. Additionally, we observe the purity of the state and Bell non-locality and obtain that the entropic uncertainty is anticorrelated with both the purity and Bell non-locality of the evolution state.

  12. Predicting Cumulative Incidence Probability: Marginal and Cause-Specific Modelling

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2005-01-01

    cumulative incidence probability; cause-specific hazards; subdistribution hazard; binomial modelling......cumulative incidence probability; cause-specific hazards; subdistribution hazard; binomial modelling...

  13. Model-based adaptive sliding mode control of the subcritical boiler-turbine system with uncertainties.

    Science.gov (United States)

    Tian, Zhen; Yuan, Jingqi; Xu, Liang; Zhang, Xiang; Wang, Jingcheng

    2018-05-25

    As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction. Copyright © 2018. Published by Elsevier Ltd.

  14. Uncertainty propagation analysis of an N2O emission model at the plot and landscape scale

    NARCIS (Netherlands)

    Nol, L.; Heuvelink, G.B.M.; Veldkamp, A.; Vries, de W.; Kros, J.

    2010-01-01

    Nitrous oxide (N2O) emission from agricultural land is an important component of the total annual greenhouse gas (GHG) budget. In addition, uncertainties associated with agricultural N2O emissions are large. The goals of this work were (i) to quantify the uncertainties of modelled N2O emissions

  15. Application of the emission inventory model TEAM: Uncertainties in dioxin emission estimates for central Europe

    NARCIS (Netherlands)

    Pulles, M.P.J.; Kok, H.; Quass, U.

    2006-01-01

    This study uses an improved emission inventory model to assess the uncertainties in emissions of dioxins and furans associated with both knowledge on the exact technologies and processes used, and with the uncertainties of both activity data and emission factors. The annual total emissions for the

  16. Sources of uncertainties in modelling black carbon at the global scale

    NARCIS (Netherlands)

    Vignati, E.; Karl, M.; Krol, M.C.; Wilson, J.; Stier, P.; Cavalli, F.

    2010-01-01

    Our understanding of the global black carbon (BC) cycle is essentially qualitative due to uncertainties in our knowledge of its properties. This work investigates two source of uncertainties in modelling black carbon: those due to the use of different schemes for BC ageing and its removal rate in

  17. Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1)

    DEFF Research Database (Denmark)

    Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan

    2009-01-01

    The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predict...

  18. Uncertainty Evaluation with Multi-Dimensional Model of LBLOCA in OPR1000 Plant

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jieun; Oh, Deog Yeon; Seul, Kwang-Won; Lee, Jin Ho [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

    2016-10-15

    KINS has used KINS-REM (KINS-Realistic Evaluation Methodology) which developed for Best- Estimate (BE) calculation and uncertainty quantification for regulatory audit. This methodology has been improved continuously by numerous studies, such as uncertainty parameters and uncertainty ranges. In this study, to evaluate the applicability of improved KINS-REM for OPR1000 plant, uncertainty evaluation with multi-dimensional model for confirming multi-dimensional phenomena was conducted with MARS-KS code. In this study, the uncertainty evaluation with multi- dimensional model of OPR1000 plant was conducted for confirming the applicability of improved KINS- REM The reactor vessel modeled using MULTID component of MARS-KS code, and total 29 uncertainty parameters were considered by 124 sampled calculations. Through 124 calculations using Mosaique program with MARS-KS code, peak cladding temperature was calculated and final PCT was determined by the 3rd order Wilks' formula. The uncertainty parameters which has strong influence were investigated by Pearson coefficient analysis. They were mostly related with plant operation and fuel material properties. Evaluation results through the 124 calculations and sensitivity analysis show that improved KINS-REM could be reasonably applicable for uncertainty evaluation with multi-dimensional model calculations of OPR1000 plants.

  19. Application of Uncertainty and Sensitivity Analysis to a Kinetic Model for Enzymatic Biodiesel Production

    DEFF Research Database (Denmark)

    Price, Jason Anthony; Nordblad, Mathias; Woodley, John

    2014-01-01

    This paper demonstrates the added benefits of using uncertainty and sensitivity analysis in the kinetics of enzymatic biodiesel production. For this study, a kinetic model by Fedosov and co-workers is used. For the uncertainty analysis the Monte Carlo procedure was used to statistically quantify...

  20. Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

    NARCIS (Netherlands)

    Dewi, Ratna Sari; Bijker, Wietske; Stein, Alfred

    2017-01-01

    This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades

  1. Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez, Ana [Department of Statistics and Operational Research, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain); Carlos, Sofia [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain); Martorell, Sebastian [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain)], E-mail: smartore@iqn.upv.es; Villanueva, Jose F. [Department of Chemical and Nuclear Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46071 Valencia (Spain)

    2009-01-15

    Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.

  2. Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

    International Nuclear Information System (INIS)

    Sanchez, Ana; Carlos, Sofia; Martorell, Sebastian; Villanueva, Jose F.

    2009-01-01

    Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral

  3. Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

    International Nuclear Information System (INIS)

    Koch, J.; Peterson, S-R.

    1995-10-01

    Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs

  4. Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

    Energy Technology Data Exchange (ETDEWEB)

    Koch, J. [Israel Atomic Energy Commission, Yavne (Israel). Soreq Nuclear Research Center; Peterson, S-R.

    1995-10-01

    Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs.

  5. Considering the Epistemic Uncertainties of the Variogram Model in Locating Additional Exploratory Drillholes

    Directory of Open Access Journals (Sweden)

    Saeed Soltani

    2015-06-01

    Full Text Available To enhance the certainty of the grade block model, it is necessary to increase the number of exploratory drillholes and collect more data from the deposit. The inputs of the process of locating these additional drillholes include the variogram model parameters, locations of the samples taken from the initial drillholes, and the geological block model. The uncertainties of these inputs will lead to uncertainties in the optimal locations of additional drillholes. Meanwhile, the locations of the initial data are crisp, but the variogram model parameters and the geological model have uncertainties due to the limitation of the number of initial data. In this paper, effort has been made to consider the effects of variogram uncertainties on the optimal location of additional drillholes using the fuzzy kriging and solve the locating problem with the genetic algorithm (GA optimization method.A bauxite deposit case study has shown the efficiency of the proposed model.

  6. Model structural uncertainty quantification and hydrologic parameter and prediction error analysis using airborne electromagnetic data

    DEFF Research Database (Denmark)

    Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen

    Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...

  7. Large-Scale Transport Model Uncertainty and Sensitivity Analysis: Distributed Sources in Complex Hydrogeologic Systems

    International Nuclear Information System (INIS)

    Sig Drellack, Lance Prothro

    2007-01-01

    The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result of the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The

  8. Assessment and Reduction of Model Parametric Uncertainties: A Case Study with A Distributed Hydrological Model

    Science.gov (United States)

    Gan, Y.; Liang, X. Z.; Duan, Q.; Xu, J.; Zhao, P.; Hong, Y.

    2017-12-01

    The uncertainties associated with the parameters of a hydrological model need to be quantified and reduced for it to be useful for operational hydrological forecasting and decision support. An uncertainty quantification framework is presented to facilitate practical assessment and reduction of model parametric uncertainties. A case study, using the distributed hydrological model CREST for daily streamflow simulation during the period 2008-2010 over ten watershed, was used to demonstrate the performance of this new framework. Model behaviors across watersheds were analyzed by a two-stage stepwise sensitivity analysis procedure, using LH-OAT method for screening out insensitive parameters, followed by MARS-based Sobol' sensitivity indices for quantifying each parameter's contribution to the response variance due to its first-order and higher-order effects. Pareto optimal sets of the influential parameters were then found by the adaptive surrogate-based multi-objective optimization procedure, using MARS model for approximating the parameter-response relationship and SCE-UA algorithm for searching the optimal parameter sets of the adaptively updated surrogate model. The final optimal parameter sets were validated against the daily streamflow simulation of the same watersheds during the period 2011-2012. The stepwise sensitivity analysis procedure efficiently reduced the number of parameters that need to be calibrated from twelve to seven, which helps to limit the dimensionality of calibration problem and serves to enhance the efficiency of parameter calibration. The adaptive MARS-based multi-objective calibration exercise provided satisfactory solutions to the reproduction of the observed streamflow for all watersheds. The final optimal solutions showed significant improvement when compared to the default solutions, with about 65-90% reduction in 1-NSE and 60-95% reduction in |RB|. The validation exercise indicated a large improvement in model performance with about 40

  9. Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

    Science.gov (United States)

    Maiorano, Andrea; Martre, Pierre; Asseng, Senthold; Ewert, Frank; Mueller, Christoph; Roetter, Reimund P.; Ruane, Alex C.; Semenov, Mikhail A.; Wallach, Daniel; Wang, Enli

    2016-01-01

    To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

  10. Evaluation of uncertainty in geological framework models at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Bagtzoglou, A.C.; Stirewalt, G.L.; Henderson, D.B.; Seida, S.B.

    1995-01-01

    The first step towards determining compliance with the performance objectives for both the repository system and the geologic setting at Yucca Mountain requires the development of detailed geostratigraphic models. This paper proposes an approach for the evaluation of the degree of uncertainty inherent in geologic maps and associated three-dimensional geological models. Following this approach, an assessment of accuracy and completeness of the data and evaluation of conceptual uncertainties in the geological framework models can be performed

  11. A statistical methodology for quantification of uncertainty in best estimate code physical models

    International Nuclear Information System (INIS)

    Vinai, Paolo; Macian-Juan, Rafael; Chawla, Rakesh

    2007-01-01

    A novel uncertainty assessment methodology, based on a statistical non-parametric approach, is presented in this paper. It achieves quantification of code physical model uncertainty by making use of model performance information obtained from studies of appropriate separate-effect tests. Uncertainties are quantified in the form of estimated probability density functions (pdf's), calculated with a newly developed non-parametric estimator. The new estimator objectively predicts the probability distribution of the model's 'error' (its uncertainty) from databases reflecting the model's accuracy on the basis of available experiments. The methodology is completed by applying a novel multi-dimensional clustering technique based on the comparison of model error samples with the Kruskall-Wallis test. This takes into account the fact that a model's uncertainty depends on system conditions, since a best estimate code can give predictions for which the accuracy is affected by the regions of the physical space in which the experiments occur. The final result is an objective, rigorous and accurate manner of assigning uncertainty to coded models, i.e. the input information needed by code uncertainty propagation methodologies used for assessing the accuracy of best estimate codes in nuclear systems analysis. The new methodology has been applied to the quantification of the uncertainty in the RETRAN-3D void model and then used in the analysis of an independent separate-effect experiment. This has clearly demonstrated the basic feasibility of the approach, as well as its advantages in yielding narrower uncertainty bands in quantifying the code's accuracy for void fraction predictions

  12. Assessment of errors and uncertainty patterns in GIA modeling

    DEFF Research Database (Denmark)

    Barletta, Valentina Roberta; Spada, G.

    2012-01-01

    During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one......, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland...

  13. 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

  14. Demand and generation cost uncertainty modelling in power system optimization studies

    Energy Technology Data Exchange (ETDEWEB)

    Gomes, Bruno Andre; Saraiva, Joao Tome [INESC Porto and Departamento de Engenharia Electrotecnica e Computadores, Faculdade de Engenharia da Universidade do Porto, FEUP, Campus da FEUP Rua Roberto Frias 378, 4200 465 Porto (Portugal)

    2009-06-15

    This paper describes the formulations and the solution algorithms developed to include uncertainties in the generation cost function and in the demand on DC OPF studies. The uncertainties are modelled by trapezoidal fuzzy numbers and the solution algorithms are based on multiparametric linear programming techniques. These models are a development of an initial formulation detailed in several publications co-authored by the second author of this paper. Now, we developed a more complete model and a more accurate solution algorithm in the sense that it is now possible to capture the widest possible range of values of the output variables reflecting both demand and generation cost uncertainties. On the other hand, when modelling simultaneously demand and generation cost uncertainties, we are representing in a more realistic way the volatility that is currently inherent to power systems. Finally, the paper includes a case study to illustrate the application of these models based on the IEEE 24 bus test system. (author)

  15. Parameter sensitivity and uncertainty of the forest carbon flux model FORUG : a Monte Carlo analysis

    Energy Technology Data Exchange (ETDEWEB)

    Verbeeck, H.; Samson, R.; Lemeur, R. [Ghent Univ., Ghent (Belgium). Laboratory of Plant Ecology; Verdonck, F. [Ghent Univ., Ghent (Belgium). Dept. of Applied Mathematics, Biometrics and Process Control

    2006-06-15

    The FORUG model is a multi-layer process-based model that simulates carbon dioxide (CO{sub 2}) and water exchange between forest stands and the atmosphere. The main model outputs are net ecosystem exchange (NEE), total ecosystem respiration (TER), gross primary production (GPP) and evapotranspiration. This study used a sensitivity analysis to identify the parameters contributing to NEE uncertainty in the FORUG model. The aim was to determine if it is necessary to estimate the uncertainty of all parameters of a model to determine overall output uncertainty. Data used in the study were the meteorological and flux data of beech trees in Hesse. The Monte Carlo method was used to rank sensitivity and uncertainty parameters in combination with a multiple linear regression. Simulations were run in which parameters were assigned probability distributions and the effect of variance in the parameters on the output distribution was assessed. The uncertainty of the output for NEE was estimated. Based on the arbitrary uncertainty of 10 key parameters, a standard deviation of 0.88 Mg C per year per NEE was found, which was equal to 24 per cent of the mean value of NEE. The sensitivity analysis showed that the overall output uncertainty of the FORUG model could be determined by accounting for only a few key parameters, which were identified as corresponding to critical parameters in the literature. It was concluded that the 10 most important parameters determined more than 90 per cent of the output uncertainty. High ranking parameters included soil respiration; photosynthesis; and crown architecture. It was concluded that the Monte Carlo technique is a useful tool for ranking the uncertainty of parameters of process-based forest flux models. 48 refs., 2 tabs., 2 figs.

  16. Entropic uncertainty relations in the Heisenberg XXZ model and its controlling via filtering operations

    Science.gov (United States)

    Ming, Fei; Wang, Dong; Shi, Wei-Nan; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu

    2018-04-01

    The uncertainty principle is recognized as an elementary ingredient of quantum theory and sets up a significant bound to predict outcome of measurement for a couple of incompatible observables. In this work, we develop dynamical features of quantum memory-assisted entropic uncertainty relations (QMA-EUR) in a two-qubit Heisenberg XXZ spin chain with an inhomogeneous magnetic field. We specifically derive the dynamical evolutions of the entropic uncertainty with respect to the measurement in the Heisenberg XXZ model when spin A is initially correlated with quantum memory B. It has been found that the larger coupling strength J of the ferromagnetism ( J 0 ) chains can effectively degrade the measuring uncertainty. Besides, it turns out that the higher temperature can induce the inflation of the uncertainty because the thermal entanglement becomes relatively weak in this scenario, and there exists a distinct dynamical behavior of the uncertainty when an inhomogeneous magnetic field emerges. With the growing magnetic field | B | , the variation of the entropic uncertainty will be non-monotonic. Meanwhile, we compare several different optimized bounds existing with the initial bound proposed by Berta et al. and consequently conclude Adabi et al.'s result is optimal. Moreover, we also investigate the mixedness of the system of interest, dramatically associated with the uncertainty. Remarkably, we put forward a possible physical interpretation to explain the evolutionary phenomenon of the uncertainty. Finally, we take advantage of a local filtering operation to steer the magnitude of the uncertainty. Therefore, our explorations may shed light on the entropic uncertainty under the Heisenberg XXZ model and hence be of importance to quantum precision measurement over solid state-based quantum information processing.

  17. Mass discharge estimation from contaminated sites: Multi-model solutions for assessment of conceptual uncertainty

    Science.gov (United States)

    Thomsen, N. I.; Troldborg, M.; McKnight, U. S.; Binning, P. J.; Bjerg, P. L.

    2012-04-01

    Mass discharge estimates are increasingly being used in the management of contaminated sites. Such estimates have proven useful for supporting decisions related to the prioritization of contaminated sites in a groundwater catchment. Potential management options can be categorised as follows: (1) leave as is, (2) clean up, or (3) further investigation needed. However, mass discharge estimates are often very uncertain, which may hamper the management decisions. If option 1 is incorrectly chosen soil and water quality will decrease, threatening or destroying drinking water resources. The risk of choosing option 2 is to spend money on remediating a site that does not pose a problem. Choosing option 3 will often be safest, but may not be the optimal economic solution. Quantification of the uncertainty in mass discharge estimates can therefore greatly improve the foundation for selecting the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level of information. Parameter uncertainty is quantified using Monte Carlo simulations. For each conceptual model we calculate a transient mass discharge estimate with uncertainty bounds resulting from

  18. Future bloom and blossom frost risk for Malus domestica considering climate model and impact model uncertainties.

    Science.gov (United States)

    Hoffmann, Holger; Rath, Thomas

    2013-01-01

    The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K(-1), showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.

  19. A tool for efficient, model-independent management optimization under uncertainty

    Science.gov (United States)

    White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

    2018-01-01

    To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

  20. Upgrade of Common Cause Failure Modelling of NPP Krsko PSA

    International Nuclear Information System (INIS)

    Vukovic, I.; Mikulicic, V.; Vrbanic, I.

    2006-01-01

    Over the last thirty years the probabilistic safety assessments (PSA) have been increasingly applied in technical engineering practice. Various failure modes of system of concern are mathematically and explicitly modelled by means of fault tree structure. Statistical independence of basic events from which the fault tree is built is not acceptable for an event category referred to as common cause failures (CCF). Based on overview of current international status of modelling of common cause failures in PSA several steps were made related to primary technical basis for methodology and data used for CCF model upgrade project in NPP Krsko (NEK) PSA. As a primary technical basis for methodological aspects of CCF modelling in Krsko PSA the following documents were considered: NUREG/CR-5485, NUREG/CR-4780, and Westinghouse Owners Group documents (WOG) WCAP-15674 and WCAP-15167. Use of these documents is supported by the most relevant guidelines and standards in the field, such as ASME PRA Standard and NRC Regulatory Guide 1.200. WCAP documents are in compliance with NUREG/CR-5485 and NUREG/CR-4780. Additionally, they provide WOG perspective on CCF modelling, which is important to consider since NEK follows WOG practice in resolving many generic and regulatory issues. It is, therefore, desirable that NEK CCF methodology and modelling is in general accordance with recommended WOG approaches. As a primary basis for CCF data needed to estimate CCF model parameters and their uncertainty, the main used documents were: NUREG/CR-5497, NUREG/CR-6268, WCAP-15167, and WCAP-16187. Use of NUREG/CR-5497 and NUREG/CR-6268 as a source of data for CCF parameter estimating is supported by the most relevant industry and regulatory PSA guides and standards currently existing in the field, including WOG. However, the WCAP document WCAP-16187 has provided a basis for CCF parameter values specific to Westinghouse PWR plants. Many of events from NRC / INEEL database were re-classified in WCAP

  1. Effect of Uncertainty Parameters in Blowdown and Reflood Models for OPR1000 LBLOCA Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Huh, Byung Gil; Jin, Chang Yong; Seul, Kwangwon; Hwang, Taesuk [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

    2014-05-15

    KINS(Korea Institute of Nuclear Safety) has also performed the audit calculation with the KINS Realistic Evaluation Methodology(KINS-REM) to confirm the validity of licensee's calculation. In the BEPU method, it is very important to quantify the code and model uncertainty. It is referred in the following requirement: BE calculations in Regulatory Guide 1.157 - 'the code and models used are acceptable and applicable to the specific facility over the intended operating range and must quantify the uncertainty in the specific application'. In general, the uncertainty of model/code should be obtained through the data comparison with relevant integral- and separate-effect tests at different scales. However, it is not easy to determine these kinds of uncertainty because of the difficulty for evaluating accurately various experiments. Therefore, the expert judgment has been used in many cases even with the limitation that the uncertainty range of important parameters can be wide and inaccurate. In the KINS-REM, six heat transfer parameters in the blowdown phase have been used to consider the uncertainty of models. Recently, MARS-KS code was modified to consider the uncertainty of the five heat transfer parameters in the reflood phase. Accordingly, it is required that the uncertainty range for parameters of reflood models is determined and the effect of these ranges is evaluated. In this study, the large break LOCA (LBLOCA) analysis for OPR1000 was performed to identify the effect of uncertainty parameters in blowdown and reflood models.

  2. Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

    Science.gov (United States)

    Feyen, Luc; Caers, Jef

    2006-06-01

    In this work, we address the problem of characterizing the heterogeneity and uncertainty of hydraulic properties for complex geological settings. Hereby, we distinguish between two scales of heterogeneity, namely the hydrofacies structure and the intrafacies variability of the hydraulic properties. We employ multiple-point geostatistics to characterize the hydrofacies architecture. The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization. The intrafacies variability of the hydraulic properties is represented using conventional two-point correlation methods, more precisely, spatial covariance models under a multi-Gaussian spatial law. We address the different levels and sources of uncertainty in characterizing the subsurface heterogeneity, and explore their effect on groundwater flow and transport predictions. Typically, uncertainty is assessed by way of many images, termed realizations, of a fixed statistical model. However, in many cases, sampling from a fixed stochastic model does not adequately represent the space of uncertainty. It neglects the uncertainty related to the selection of the stochastic model and the estimation of its input parameters. We acknowledge the uncertainty inherent in the definition of the prior conceptual model of aquifer architecture and in the estimation of global statistics, anisotropy, and correlation scales. Spatial bootstrap is used to assess the uncertainty of the unknown statistical parameters. As an illustrative example, we employ a synthetic field that represents a fluvial setting consisting of an interconnected network of channel sands embedded within finer-grained floodplain material. For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty. Results indicate the importance of accurately describing the facies geometry, especially for transport

  3. Review of uncertainty estimates associated with models for assessing the impact of breeder reactor radioactivity releases

    International Nuclear Information System (INIS)

    Miller, C.; Little, C.A.

    1982-08-01

    The purpose is to summarize estimates based on currently available data of the uncertainty associated with radiological assessment models. The models being examined herein are those recommended previously for use in breeder reactor assessments. Uncertainty estimates are presented for models of atmospheric and hydrologic transport, terrestrial and aquatic food-chain bioaccumulation, and internal and external dosimetry. Both long-term and short-term release conditions are discussed. The uncertainty estimates presented in this report indicate that, for many sites, generic models and representative parameter values may be used to calculate doses from annual average radionuclide releases when these calculated doses are on the order of one-tenth or less of a relevant dose limit. For short-term, accidental releases, especially those from breeder reactors located in sites dominated by complex terrain and/or coastal meteorology, the uncertainty in the dose calculations may be much larger than an order of magnitude. As a result, it may be necessary to incorporate site-specific information into the dose calculation under these circumstances to reduce this uncertainty. However, even using site-specific information, natural variability and the uncertainties in the dose conversion factor will likely result in an overall uncertainty of greater than an order of magnitude for predictions of dose or concentration in environmental media following shortterm releases

  4. Essays in energy policy and planning modeling under uncertainty: Value of information, optimistic biases, and simulation of capacity markets

    Science.gov (United States)

    Hu, Ming-Che

    . Theoretical analyses prove the general existence of this bias for both competitive and oligopolistic models when production costs and demand curves are uncertain. Also demonstrated is an optimistic bias for the net benefits of introducing a new technology into a market when the cost of the new technology is uncertainty. The optimistic biases are quantified for a model of the northwest European electricity market (including Belgium, France, Germany and the Netherlands). Demand uncertainty results in an optimistic bias of 150,000-220,000 [Euro]/hr of total surplus and natural gas price uncertainty yields a smaller bias of 8,000-10,000 [Euro]/hr for total surplus. Further, adding a new uncertain technology (biomass) to the set of possible generation methods almost doubles the optimistic bias (14,000-18,000 [Euro]/hr). The third question concerns ex ante evaluation of the Reliability Pricing Model (RPM)---the new PJM capacity market---launched in June 2007. A Monte Carlo simulation model is developed to simulate PJM capacity market and predict market performance, producer revenue, and consumer payments. An important input to RPM is a demand curve for capacity; several alternative demand curves are compared, and sensitivity analyses conducted of those conclusions. One conclusion is that the sloped demand curves are more robust because those demand curves gives higher reliability with lower consumer payments. In addition, the performance of the curves is evaluated for a more sophisticated market design in which the demand curve can be adjusted in response to previous market outcomes and where the capital costs may change unexpectedly. The simulation shows that curve adjustment increases system reliability with lower consumer payments. Also the effect of learning-by-doing, leading to lower plant capital costs, leads to higher average reserve margin and lower consumer payments. In contrast, a the sudden rise in capital costs causes a decrease in reliability and an increase in

  5. A stochastic multi-agent optimization model for energy infrastructure planning under uncertainty and competition.

    Science.gov (United States)

    2017-07-04

    This paper presents a stochastic multi-agent optimization model that supports energy infrastruc- : ture planning under uncertainty. The interdependence between dierent decision entities in the : system is captured in an energy supply chain network, w...

  6. Event based uncertainty assessment in urban drainage modelling, applying the GLUE methodology

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Beven, K.J.; Jensen, Jacob Birk

    2008-01-01

    of combined sewer overflow. The GLUE methodology is used to test different conceptual setups in order to determine if one model setup gives a better goodness of fit conditional on the observations than the other. Moreover, different methodological investigations of GLUE are conducted in order to test......In the present paper an uncertainty analysis on an application of the commercial urban drainage model MOUSE is conducted. Applying the Generalized Likelihood Uncertainty Estimation (GLUE) methodology the model is conditioned on observation time series from two flow gauges as well as the occurrence...... if the uncertainty analysis is unambiguous. It is shown that the GLUE methodology is very applicable in uncertainty analysis of this application of an urban drainage model, although it was shown to be quite difficult of get good fits of the whole time series....

  7. Uncertainty and Sensitivity Analysis of Filtration Models for Non-Fickian transport and Hyperexponential deposition

    DEFF Research Database (Denmark)

    Yuan, Hao; Sin, Gürkan

    2011-01-01

    Uncertainty and sensitivity analyses are carried out to investigate the predictive accuracy of the filtration models for describing non-Fickian transport and hyperexponential deposition. Five different modeling approaches, involving the elliptic equation with different types of distributed...... filtration coefficients and the CTRW equation expressed in Laplace space, are selected to simulate eight experiments. These experiments involve both porous media and colloid-medium interactions of different heterogeneity degrees. The uncertainty of elliptic equation predictions with distributed filtration...... coefficients is larger than that with a single filtration coefficient. The uncertainties of model predictions from the elliptic equation and CTRW equation in Laplace space are minimal for solute transport. Higher uncertainties of parameter estimation and model outputs are observed in the cases with the porous...

  8. Treatment of uncertainties in atmospheric chemical systems: A combined modeling and experimental approach

    Science.gov (United States)

    Pun, Betty Kong-Ling

    1998-12-01

    Uncertainty is endemic in modeling. This thesis is a two- phase program to understand the uncertainties in urban air pollution model predictions and in field data used to validate them. Part I demonstrates how to improve atmospheric models by analyzing the uncertainties in these models and using the results to guide new experimentation endeavors. Part II presents an experiment designed to characterize atmospheric fluctuations, which have significant implications towards the model validation process. A systematic study was undertaken to investigate the effects of uncertainties in the SAPRC mechanism for gas- phase chemistry in polluted atmospheres. The uncertainties of more than 500 parameters were compiled, including reaction rate constants, product coefficients, organic composition, and initial conditions. Uncertainty propagation using the Deterministic Equivalent Modeling Method (DEMM) revealed that the uncertainties in ozone predictions can be up to 45% based on these parametric uncertainties. The key parameters found to dominate the uncertainties of the predictions include photolysis rates of NO2, O3, and formaldehyde; the rate constant for nitric acid formation; and initial amounts of NOx and VOC. Similar uncertainty analysis procedures applied to two other mechanisms used in regional air quality models led to the conclusion that in the presence of parametric uncertainties, the mechanisms cannot be discriminated. Research efforts should focus on reducing parametric uncertainties in photolysis rates, reaction rate constants, and source terms. A new tunable diode laser (TDL) infrared spectrometer was designed and constructed to measure multiple pollutants simultaneously in the same ambient air parcels. The sensitivities of the one hertz measurements were 2 ppb for ozone, 1 ppb for NO, and 0.5 ppb for NO2. Meteorological data were also collected for wind, temperature, and UV intensity. The field data showed clear correlations between ozone, NO, and NO2 in the one

  9. A Study on Uncertainty Quantification of Reflood Model using CIRCE Methodology

    International Nuclear Information System (INIS)

    Jeon, Seongsu; Hong, Soonjoon; Oh, Deogyeon; Bang, Youngseok

    2013-01-01

    The CIRCE method is intended to quantify the uncertainties of the correlations of a code. It may replace the expert judgment generally used. In this study, an uncertainty quantification of reflood model was performed using CIRCE methodology. In this paper, the application process of CIRCE methodology and main results are briefly described. This research is expected to be useful to improve the present audit calculation methodology, KINS-REM. In this study, an uncertainty quantification of reflood model was performed using CIRCE methodology. The application of CIRCE provided the satisfactory results. This research is expected to be useful to improve the present audit calculation methodology, KINS-REM

  10. A GLUE uncertainty analysis of a drying model of pharmaceutical granules

    DEFF Research Database (Denmark)

    Mortier, Séverine Thérèse F.C.; Van Hoey, Stijn; Cierkens, Katrijn

    2013-01-01

    unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma™, GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level...... on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty...

  11. Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

    Science.gov (United States)

    Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

    2017-11-01

    The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

  12. Examination of the uncertainty in contaminant fate and transport modeling: a case study in the Venice Lagoon.

    Science.gov (United States)

    Sommerfreund, J; Arhonditsis, G B; Diamond, M L; Frignani, M; Capodaglio, G; Gerino, M; Bellucci, L; Giuliani, S; Mugnai, C

    2010-03-01

    A Monte Carlo analysis is used to quantify environmental parametric uncertainty in a multi-segment, multi-chemical model of the Venice Lagoon. Scientific knowledge, expert judgment and observational data are used to formulate prior probability distributions that characterize the uncertainty pertaining to 43 environmental system parameters. The propagation of this uncertainty through the model is then assessed by a comparative analysis of the moments (central tendency, dispersion) of the model output distributions. We also apply principal component analysis in combination with correlation analysis to identify the most influential parameters, thereby gaining mechanistic insights into the ecosystem functioning. We found that modeled concentrations of Cu, Pb, OCDD/F and PCB-180 varied by up to an order of magnitude, exhibiting both contaminant- and site-specific variability. These distributions generally overlapped with the measured concentration ranges. We also found that the uncertainty of the contaminant concentrations in the Venice Lagoon was characterized by two modes of spatial variability, mainly driven by the local hydrodynamic regime, which separate the northern and central parts of the lagoon and the more isolated southern basin. While spatial contaminant gradients in the lagoon were primarily shaped by hydrology, our analysis also shows that the interplay amongst the in-place historical pollution in the central lagoon, the local suspended sediment concentrations and the sediment burial rates exerts significant control on the variability of the contaminant concentrations. We conclude that the probabilistic analysis presented herein is valuable for quantifying uncertainty and probing its cause in over-parameterized models, while some of our results can be used to dictate where additional data collection efforts should focus on and the directions that future model refinement should follow. (c) 2009 Elsevier Inc. All rights reserved.

  13. Modeling of spatial dependence in wind power forecast uncertainty

    DEFF Research Database (Denmark)

    Papaefthymiou, George; Pinson, Pierre

    2008-01-01

    It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus...... neglect the interdependence structure of prediction errors, induced by movement of meteorological fronts, or more generally by inertia of meteorological systems. This issue is addressed here by describing a method that permits to generate interdependent scenarios of wind generation for spatially...... distributed wind power production for specific look-ahead times. The approach is applied to the case of western Denmark split in 5 zones, for a total capacity of more than 2.1 GW. The interest of the methodology for improving the resolution of probabilistic forecasts, for a range of decision-making problems...

  14. Impact on Model Uncertainty of Diabatization in Distillation Columns

    DEFF Research Database (Denmark)

    Bisgaard, Thomas; Huusom, Jakob Kjøbsted; Abildskov, Jens

    2014-01-01

    index. Error propagation of uncertain parameters to second-law efficiency, the operation expenditures, the capital expenditures , and the dominant time constant for separations of benzene-toluene and benzene-flourobenzene are investigated . Conclusions favoring the HIDiC over the CDiC w.r.t. operation...... expenditures in the benzene-toluene separation can not made within 95 % confidence. The sensitivity of overall heat transfer coefficient decreases on all indicators as the separation becomes more difficult......This work provides uncertainty and sensitivity analysis of design of conventional and heat integrated distillation columns using Monte Carlo simulations. Selected uncertain parameters are relative volatility, heat of vaporization, the overall heat transfer coefficient , tray hold-up, and adiabat ic...

  15. Some concepts of model uncertainty for performance assessments of nuclear waste repositories

    International Nuclear Information System (INIS)

    Eisenberg, N.A.; Sagar, B.; Wittmeyer, G.W.

    1994-01-01

    Models of the performance of nuclear waste repositories will be central to making regulatory decisions regarding the safety of such facilities. The conceptual model of repository performance is represented by mathematical relationships, which are usually implemented as one or more computer codes. A geologic system may allow many conceptual models, which are consistent with the observations. These conceptual models may or may not have the same mathematical representation. Experiences in modeling the performance of a waste repository representation. Experiences in modeling the performance of a waste repository (which is, in part, a geologic system), show that this non-uniqueness of conceptual models is a significant source of model uncertainty. At the same time, each conceptual model has its own set of parameters and usually, it is not be possible to completely separate model uncertainty from parameter uncertainty for the repository system. Issues related to the origin of model uncertainty, its relation to parameter uncertainty, and its incorporation in safety assessments are discussed from a broad regulatory perspective. An extended example in which these issues are explored numerically is also provided

  16. Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community

    Directory of Open Access Journals (Sweden)

    K. J. Franz

    2011-11-01

    Full Text Available The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE, and the Shuffle Complex Evolution Metropolis (SCEM. Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.

  17. Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model.

    Science.gov (United States)

    Bolster, Carl H; Vadas, Peter A

    2013-07-01

    Models are often used to predict phosphorus (P) loss from agricultural fields. Although it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study we assessed the effect of model input error on predictions of annual P loss by the Annual P Loss Estimator (APLE) model. Our objectives were (i) to conduct a sensitivity analyses for all APLE input variables to determine which variables the model is most sensitive to, (ii) to determine whether the relatively easy-to-implement first-order approximation (FOA) method provides accurate estimates of model prediction uncertainties by comparing results with the more accurate Monte Carlo simulation (MCS) method, and (iii) to evaluate the performance of the APLE model against measured P loss data when uncertainties in model predictions and measured data are included. Our results showed that for low to moderate uncertainties in APLE input variables, the FOA method yields reasonable estimates of model prediction uncertainties, although for cases where manure solid content is between 14 and 17%, the FOA method may not be as accurate as the MCS method due to a discontinuity in the manure P loss component of APLE at a manure solid content of 15%. The estimated uncertainties in APLE predictions based on assumed errors in the input variables ranged from ±2 to 64% of the predicted value. Results from this study highlight the importance of including reasonable estimates of model uncertainty when using models to predict P loss. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

  18. 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.

  19. A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour

    Directory of Open Access Journals (Sweden)

    Peter E. Land

    2018-05-01

    Full Text Available Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.

  20. Assessing the DICE model: uncertainty associated with the emission and retention of greenhouse gases

    International Nuclear Information System (INIS)

    Kaufmann, R.K.

    1997-01-01

    Analysis of the DICE model indicates that it contains unsupported assumptions, simple extrapolations, and mis-specifications that cause it to understate the rate at which economic activity emits greenhouse gases and the rate at which the atmosphere retains greenhouse gases. The model assumes a world population that is 2 billion people lower than the 'base case' projected by demographers. The model extrapolates a decline in the quantity of greenhouse gases emitted per unit of economic activity that is possible only if there is a structural break in the economic and engineering factors have determined this ratio over the last century. The model uses a single equation to simulate the rate at which greenhouse gases accumulate in the atmosphere. The forecast for the airborne fraction generated by this equation contradicts forecasts generated by models that represent the physical and chemical processes which determine the movement of carbon from the atmosphere to the ocean. When these unsupported assumptions, simple extrapolations, and misspecifications are remedied with simple fixes, the economic impact of global climate change increases several fold. Similarly, these remedies increase the impact of uncertainty on estimates for the economic impact of global climate change. Together, these results indicate that considerable scientific and economic research is needed before the threat of climate change can be dismissed with any degree of certainty. 23 refs., 3 figs

  1. Does model fit decrease the uncertainty of the data in comparison with a general non-model least squares fit?

    International Nuclear Information System (INIS)

    Pronyaev, V.G.

    2003-01-01

    The information entropy is taken as a measure of knowledge about the object and the reduced univariante variance as a common measure of uncertainty. Covariances in the model versus non-model least square fits are discussed

  2. Development of mechanistic sorption model and treatment of uncertainties for Ni sorption on montmorillonite/bentonite

    International Nuclear Information System (INIS)

    Ochs, Michael; Ganter, Charlotte; Tachi, Yukio; Suyama, Tadahiro; Yui, Mikazu

    2011-02-01

    Sorption and diffusion of radionuclides in buffer materials (bentonite) are the key processes in the safe geological disposal of radioactive waste, because migration of radionuclides in this barrier is expected to be diffusion-controlled and retarded by sorption processes. It is therefore necessary to understand the detailed/coupled processes of sorption and diffusion in compacted bentonite and develop mechanistic /predictive models, so that reliable parameters can be set under a variety of geochemical conditions relevant to performance assessment (PA). For this purpose, JAEA has developed the integrated sorption and diffusion (ISD) model/database in montmorillonite/bentonite systems. The main goal of the mechanistic model/database development is to provide a tool for a consistent explanation, prediction, and uncertainty assessment of K d as well as diffusion parameters needed for the quantification of radionuclide transport. The present report focuses on developing the thermodynamic sorption model (TSM) and on the quantification and handling of model uncertainties in applications, based on illustrating by example of Ni sorption on montmorillonite/bentonite. This includes 1) a summary of the present state of the art of thermodynamic sorption modeling, 2) a discussion of the selection of surface species and model design appropriate for the present purpose, 3) possible sources and representations of TSM uncertainties, and 4) details of modeling, testing and uncertainty evaluation for Ni sorption. Two fundamentally different approaches are presented and compared for representing TSM uncertainties: 1) TSM parameter uncertainties calculated by FITEQL optimization routines and some statistical procedure, 2) overall error estimated by direct comparison of modeled and experimental K d values. The overall error in K d is viewed as the best representation of model uncertainty in ISD model/database development. (author)

  3. RANS modeling for particle transport and deposition in turbulent duct flows: Near wall model uncertainties

    International Nuclear Information System (INIS)

    Jayaraju, S.T.; Sathiah, P.; Roelofs, F.; Dehbi, A.

    2015-01-01

    Highlights: • Near-wall modeling uncertainties in the RANS particle transport and deposition are addressed in a turbulent duct flow. • Discrete Random Walk (DRW) model and Continuous Random Walk (CRW) model performances are tested. • Several near-wall anisotropic model accuracy is assessed. • Numerous sensitivity studies are performed to recommend a robust, well-validated near-wall model for accurate particle deposition predictions. - Abstract: Dust accumulation in the primary system of a (V)HTR is identified as one of the foremost concerns during a potential accident. Several numerical efforts have focused on the use of RANS methodology to better understand the complex phenomena of fluid–particle interaction at various flow conditions. In the present work, several uncertainties relating to the near-wall modeling of particle transport and deposition are addressed for the RANS approach. The validation analyses are performed in a fully developed turbulent duct flow setup. A standard k − ε turbulence model with enhanced wall treatment is used for modeling the turbulence. For the Lagrangian phase, the performance of a continuous random walk (CRW) model and a discrete random walk (DRW) model for the particle transport and deposition are assessed. For wall bounded flows, it is generally seen that accounting for near wall anisotropy is important to accurately predict particle deposition. The various near-wall correlations available in the literature are either derived from the DNS data or from the experimental data. A thorough investigation into various near-wall correlations and their applicability for accurate particle deposition predictions are assessed. The main outcome of the present work is a well validated turbulence model with optimal near-wall modeling which provides realistic particle deposition predictions

  4. Uncertainty modelling and structured singular value computation applied to an electro-mechanical system

    NARCIS (Netherlands)

    Steinbuch, M.; Terlouw, J.C.; Bosgra, O.H.; Smit, S.G.

    1992-01-01

    The investigation of closed-loop systems subject to model perturbations is an important issue to assure stability robustness of a control design. A large variety of model perturbations can be described by norm-bounded uncertainty models. A general approach for modelling structured complex and

  5. Sliding mode fault tolerant control dealing with modeling uncertainties and actuator faults.

    Science.gov (United States)

    Wang, Tao; Xie, Wenfang; Zhang, Youmin

    2012-05-01

    In this paper, two sliding mode control algorithms are developed for nonlinear systems with both modeling uncertainties and actuator faults. The first algorithm is developed under an assumption that the uncertainty bounds are known. Different design parameters are utilized to deal with modeling uncertainties and actuator faults, respectively. The second algorithm is an adaptive version of the first one, which is developed to accommodate uncertainties and faults without utilizing exact bounds information. The stability of the overall control systems is proved by using a Lyapunov function. The effectiveness of the developed algorithms have been verified on a nonlinear longitudinal model of Boeing 747-100/200. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool

    Science.gov (United States)

    Pisoni, E.; Albrecht, D.; Mara, T. A.; Rosati, R.; Tarantola, S.; Thunis, P.

    2018-06-01

    Air quality has significantly improved in Europe over the past few decades. Nonetheless we still find high concentrations in measurements mainly in specific regions or cities. This dimensional shift, from EU-wide to hot-spot exceedances, calls for a novel approach to regional air quality management (to complement EU-wide existing policies). The SHERPA (Screening for High Emission Reduction Potentials on Air quality) modelling tool was developed in this context. It provides an additional tool to be used in support to regional/local decision makers responsible for the design of air quality plans. It is therefore important to evaluate the quality of the SHERPA model, and its behavior in the face of various kinds of uncertainty. Uncertainty and sensitivity analysis techniques can be used for this purpose. They both reveal the links between assumptions and forecasts, help in-model simplification and may highlight unexpected relationships between inputs and outputs. Thus, a policy steered SHERPA module - predicting air quality improvement linked to emission reduction scenarios - was evaluated by means of (1) uncertainty analysis (UA) to quantify uncertainty in the model output, and (2) by sensitivity analysis (SA) to identify the most influential input sources of this uncertainty. The results of this study provide relevant information about the key variables driving the SHERPA output uncertainty, and advise policy-makers and modellers where to place their efforts for an improved decision-making process.

  7. Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint

    Directory of Open Access Journals (Sweden)

    R. H. Moore

    2013-04-01

    Full Text Available We use the Global Modelling Initiative (GMI chemical transport model with a cloud droplet parameterisation adjoint to quantify the sensitivity of cloud droplet number concentration to uncertainties in predicting CCN concentrations. Published CCN closure uncertainties for six different sets of simplifying compositional and mixing state assumptions are used as proxies for modelled CCN uncertainty arising from application of those scenarios. It is found that cloud droplet number concentrations (Nd are fairly insensitive to the number concentration (Na of aerosol which act as CCN over the continents (∂lnNd/∂lnNa ~10–30%, but the sensitivities exceed 70% in pristine regions such as the Alaskan Arctic and remote oceans. This means that CCN concentration uncertainties of 4–71% translate into only 1–23% uncertainty in cloud droplet number, on average. Since most of the anthropogenic indirect forcing is concentrated over the continents, this work shows that the application of Köhler theory and attendant simplifying assumptions in models is not a major source of uncertainty in predicting cloud droplet number or anthropogenic aerosol indirect forcing for the liquid, stratiform clouds simulated in these models. However, it does highlight the sensitivity of some remote areas to pollution brought into the region via long-range transport (e.g., biomass burning or from seasonal biogenic sources (e.g., phytoplankton as a source of dimethylsulfide in the southern oceans. Since these transient processes are not captured well by the climatological emissions inventories employed by current large-scale models, the uncertainties in aerosol-cloud interactions during these events could be much larger than those uncovered here. This finding motivates additional measurements in these pristine regions, for which few observations exist, to quantify the impact (and associated uncertainty of transient aerosol processes on cloud properties.

  8. Sensitivity to plant modelling uncertainties in optimal feedback control of sound radiation from a panel

    DEFF Research Database (Denmark)

    Mørkholt, Jakob

    1997-01-01

    Optimal feedback control of broadband sound radiation from a rectangular baffled panel has been investigated through computer simulations. Special emphasis has been put on the sensitivity of the optimal feedback control to uncertainties in the modelling of the system under control.A model...... in terms of a set of radiation filters modelling the radiation dynamics.Linear quadratic feedback control applied to the panel in order to minimise the radiated sound power has then been simulated. The sensitivity of the model based controller to modelling uncertainties when using feedback from actual...

  9. α-Decomposition for estimating parameters in common cause failure modeling based on causal inference

    International Nuclear Information System (INIS)

    Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi

    2013-01-01

    The traditional α-factor model has focused on the occurrence frequencies of common cause failure (CCF) events. Global α-factors in the α-factor model are defined as fractions of failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available failure data. Joint distributions of CCF parameters are actually determined by a set of possible causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α-decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different causes, which are named as decomposed α-factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential causes and failures. Secondly, because all potential causes have different occurrence frequencies and abilities to trigger dependent failures or independent failures, a regression model is provided and proved by conditional probability. Global α-factors are expressed by explanatory variables (causes’ occurrence frequencies) and parameters (decomposed α-factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α-decomposition process. This study shows that the α-decomposition method can integrate failure information from cause, component and system level. It can parameterize the CCF risk significance of possible causes and can update probability distributions of global α-factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding causes’ occurrence frequency of each targeted system

  10. Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield

    Science.gov (United States)

    Baurle, R. A.; Axdahl, E. L.

    2017-01-01

    Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.

  11. Probabilistic model of random uncertainties in structural dynamics for mis-tuned bladed disks; Modele probabiliste des incertitudes en dynamique des structures pour le desaccordage des roues aubagees

    Energy Technology Data Exchange (ETDEWEB)

    Capiez-Lernout, E.; Soize, Ch. [Universite de Marne la Vallee, Lab. de Mecanique, 77 (France)

    2003-10-01

    The mis-tuning of blades is frequently the cause of spatial localizations for the dynamic forced response in turbomachinery industry. The random character of mis-tuning requires the construction of probabilistic models of random uncertainties. A usual parametric probabilistic description considers the mis-tuning through the Young modulus of each blade. This model consists in mis-tuning blade eigenfrequencies, assuming the blade modal shapes unchanged. Recently a new approach known as a non-parametric model of random uncertainties has been introduced for modelling random uncertainties in elasto-dynamics. This paper proposes the construction of a non-parametric model which is coherent with all the uncertainties which characterize mis-tuning. As mis-tuning is a phenomenon which is independent from one blade to another one, the structure is considered as an assemblage of substructures. The mean reduced matrix model required by the non-parametric approach is thus constructed by dynamic sub-structuring. A comparative approach is also needed to study the influence of the non-parametric approach for a usual parametric model adapted to mis-tuning. A numerical example is presented. (authors)

  12. Model uncertainty and multimodel inference in reliability estimation within a longitudinal framework.

    Science.gov (United States)

    Alonso, Ariel; Laenen, Annouschka

    2013-05-01

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

  13. Validation and uncertainty analysis of a pre-treatment 2D dose prediction model

    Science.gov (United States)

    Baeza, Jose A.; Wolfs, Cecile J. A.; Nijsten, Sebastiaan M. J. J. G.; Verhaegen, Frank

    2018-02-01

    Independent verification of complex treatment delivery with megavolt photon beam radiotherapy (RT) has been effectively used to detect and prevent errors. This work presents the validation and uncertainty analysis of a model that predicts 2D portal dose images (PDIs) without a patient or phantom in the beam. The prediction model is based on an exponential point dose model with separable primary and secondary photon fluence components. The model includes a scatter kernel, off-axis ratio map, transmission values and penumbra kernels for beam-delimiting components. These parameters were derived through a model fitting procedure supplied with point dose and dose profile measurements of radiation fields. The model was validated against a treatment planning system (TPS; Eclipse) and radiochromic film measurements for complex clinical scenarios, including volumetric modulated arc therapy (VMAT). Confidence limits on fitted model parameters were calculated based on simulated measurements. A sensitivity analysis was performed to evaluate the effect of the parameter uncertainties on the model output. For the maximum uncertainty, the maximum deviating measurement sets were propagated through the fitting procedure and the model. The overall uncertainty was assessed using all simulated measurements. The validation of the prediction model against the TPS and the film showed a good agreement, with on average 90.8% and 90.5% of pixels passing a (2%,2 mm) global gamma analysis respectively, with a low dose threshold of 10%. The maximum and overall uncertainty of the model is dependent on the type of clinical plan used as input. The results can be used to study the robustness of the model. A model for predicting accurate 2D pre-treatment PDIs in complex RT scenarios can be used clinically and its uncertainties can be taken into account.

  14. 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...

  15. Good Modeling Practice for PAT Applications: Propagation of Input Uncertainty and Sensitivity Analysis

    DEFF Research Database (Denmark)

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

    2009-01-01

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

  16. Fuzzy uncertainty modeling applied to AP1000 nuclear power plant LOCA

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  17. Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model

    KAUST Repository

    Wang, Shitao

    2016-05-27

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

  18. Modeling Multibody Systems with Uncertainties. Part I: Theoretical and Computational Aspects

    International Nuclear Information System (INIS)

    Sandu, Adrian; Sandu, Corina; Ahmadian, Mehdi

    2006-01-01

    This study explores the use of generalized polynomial chaos theory for modeling complex nonlinear multibody dynamic systems in the presence of parametric and external uncertainty. The polynomial chaos framework has been chosen because it offers an efficient computational approach for the large, nonlinear multibody models of engineering systems of interest, where the number of uncertain parameters is relatively small, while the magnitude of uncertainties can be very large (e.g., vehicle-soil interaction). The proposed methodology allows the quantification of uncertainty distributions in both time and frequency domains, and enables the simulations of multibody systems to produce results with 'error bars'. The first part of this study presents the theoretical and computational aspects of the polynomial chaos methodology. Both unconstrained and constrained formulations of multibody dynamics are considered. Direct stochastic collocation is proposed as less expensive alternative to the traditional Galerkin approach. It is established that stochastic collocation is equivalent to a stochastic response surface approach. We show that multi-dimensional basis functions are constructed as tensor products of one-dimensional basis functions and discuss the treatment of polynomial and trigonometric nonlinearities. Parametric uncertainties are modeled by finite-support probability densities. Stochastic forcings are discretized using truncated Karhunen-Loeve expansions. The companion paper 'Modeling Multibody Dynamic Systems With Uncertainties. Part II: Numerical Applications' illustrates the use of the proposed methodology on a selected set of test problems. The overall conclusion is that despite its limitations, polynomial chaos is a powerful approach for the simulation of multibody systems with uncertainties

  19. Model of transition between causes of death.

    Science.gov (United States)

    Damiani, P; Aubenque, M

    1975-06-01

    This paper describes an attempt to estimate the probabilities of transition between various major causes of death during the period 1954-1962. The regression coefficients have been estimated from French département death rates for ten main or typical causes of death, assessed by sex for the age group 45-64 years.

  20. Modeling key processes causing climate change and variability

    Energy Technology Data Exchange (ETDEWEB)

    Henriksson, S.

    2013-09-01

    Greenhouse gas warming, internal climate variability and aerosol climate effects are studied and the importance to understand these key processes and being able to separate their influence on the climate is discussed. Aerosol-climate model ECHAM5-HAM and the COSMOS millennium model consisting of atmospheric, ocean and carbon cycle and land-use models are applied and results compared to measurements. Topics at focus are climate sensitivity, quasiperiodic variability with a period of 50-80 years and variability at other timescales, climate effects due to aerosols over India and climate effects of northern hemisphere mid- and high-latitude volcanic eruptions. The main findings of this work are (1) pointing out the remaining challenges in reducing climate sensitivity uncertainty from observational evidence, (2) estimates for the amplitude of a 50-80 year quasiperiodic oscillation in global mean temperature ranging from 0.03 K to 0.17 K and for its phase progression as well as the synchronising effect of external forcing, (3) identifying a power law shape S(f) {proportional_to} f-{alpha} for the spectrum of global mean temperature with {alpha} {approx} 0.8 between multidecadal and El Nino timescales with a smaller exponent in modelled climate without external forcing, (4) separating aerosol properties and climate effects in India by season and location (5) the more efficient dispersion of secondary sulfate aerosols than primary carbonaceous aerosols in the simulations, (6) an increase in monsoon rainfall in northern India due to aerosol light absorption and a probably larger decrease due to aerosol dimming effects and (7) an estimate of mean maximum cooling of 0.19 K due to larger northern hemisphere mid- and high-latitude volcanic eruptions. The results could be applied or useful in better isolating the human-caused climate change signal, in studying the processes further and in more detail, in decadal climate prediction, in model evaluation and in emission policy

  1. Uncertainty analysis in Titan ionospheric simulated ion mass spectra: unveiling a set of issues for models accuracy improvement

    Science.gov (United States)

    Hébrard, Eric; Carrasco, Nathalie; Dobrijevic, Michel; Pernot, Pascal

    Ion Neutral Mass Spectrometer (INMS) aboard Cassini revealed a rich coupled ion-neutral chemistry in the ionosphere, producing heavy hydrocarbons and nitriles ions. The modeling of such a complex environment is challenging, as it requires a detailed and accurate description of the different relevant processes such as photodissociation cross sections and neutral-neutral reaction rates on one hand, and ionisation cross sections, ion-molecule and recombination reaction rates on the other hand. Underpinning models calculations, each of these processes is parameterized by kinetic constants which, when known, have been studied experimentally and/or theoretically over a range of temperatures and pressures that are most often not representative of Titan's atmosphere. The sizeable experimental and theoretical uncertainties reported in the literature merge therefore with the uncertainties resulting subsequently from the unavoidable estimations or extrapolations to Titan's atmosphere conditions. Such large overall uncertainties have to be accounted for in all resulting inferences most of all to evaluate the quality of the model definition. We have undertaken a systematic study of the uncertainty sources in the simulation of ion mass spectra as recorded by Cassini/INMS in Titan ionosphere during the T5 flyby at 1200 km. Our simulated spectra seem much less affected by the uncertainties on ion-molecule reactions than on neutral-neutral reactions. Photochemical models of Titan's atmosphere are indeed so poorly predictive at high altitudes, in the sense that their computed predictions display such large uncertainties, that we found them to give rise to bimodal and hypersensitive abundance distributions for some major compounds like acetylene C2 H2 and ethylene C2 H4 . We will show to what extent global uncertainty and sensitivity analysis enabled us to identify the causes of this bimodality and to pinpoint the key processes that mostly contribute to limit the accuracy of the

  2. A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

    DEFF Research Database (Denmark)

    Thomsen, Nanna Isbak; Binning, Philip John; McKnight, Ursula S.

    2016-01-01

    the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found...... to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models...... that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert...

  3. Uncertainty estimation and ensemble forecast with a chemistry-transport model - Application to air-quality modeling and simulation

    International Nuclear Information System (INIS)

    Mallet, Vivien

    2005-01-01

    The thesis deals with the evaluation of a chemistry-transport model, not primarily with classical comparisons to observations, but through the estimation of its a priori uncertainties due to input data, model formulation and numerical approximations. These three uncertainty sources are studied respectively on the basis of Monte Carlos simulations, multi-models simulations and numerical schemes inter-comparisons. A high uncertainty is found, in output ozone concentrations. In order to overtake the limitations due to the uncertainty, a solution is ensemble forecast. Through combinations of several models (up to forty-eight models) on the basis of past observations, the forecast can be significantly improved. The achievement of this work has also led to develop the innovative modelling-system Polyphemus. (author) [fr

  4. Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies (Final Report)

    Science.gov (United States)

    EPA announced the availability of the final report, Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies. This report summarizes some of the recent progress in characterizing uncertainty and variability in physi...

  5. Application of probabilistic modelling for the uncertainty evaluation of alignment measurements of large accelerator magnets assemblies

    Science.gov (United States)

    Doytchinov, I.; Tonnellier, X.; Shore, P.; Nicquevert, B.; Modena, M.; Mainaud Durand, H.

    2018-05-01

    Micrometric assembly and alignment requirements for future particle accelerators, and especially large assemblies, create the need for accurate uncertainty budgeting of alignment measurements. Measurements and uncertainties have to be accurately stated and traceable, to international standards, for metre-long sized assemblies, in the range of tens of µm. Indeed, these hundreds of assemblies will be produced and measured by several suppliers around the world, and will have to be integrated into a single machine. As part of the PACMAN project at CERN, we proposed and studied a practical application of probabilistic modelling of task-specific alignment uncertainty by applying a simulation by constraints calibration method. Using this method, we calibrated our measurement model using available data from ISO standardised tests (10360 series) for the metrology equipment. We combined this model with reference measurements and analysis of the measured data to quantify the actual specific uncertainty of each alignment measurement procedure. Our methodology was successfully validated against a calibrated and traceable 3D artefact as part of an international inter-laboratory study. The validated models were used to study the expected alignment uncertainty and important sensitivity factors in measuring the shortest and longest of the compact linear collider study assemblies, 0.54 m and 2.1 m respectively. In both cases, the laboratory alignment uncertainty was within the targeted uncertainty budget of 12 µm (68% confidence level). It was found that the remaining uncertainty budget for any additional alignment error compensations, such as the thermal drift error due to variation in machine operation heat load conditions, must be within 8.9 µm and 9.8 µm (68% confidence level) respectively.

  6. 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

  7. Implications of Uncertainty in Fossil Fuel Emissions for Terrestrial Ecosystem Modeling

    Science.gov (United States)

    King, A. W.; Ricciuto, D. M.; Mao, J.; Andres, R. J.

    2017-12-01

    Given observations of the increase in atmospheric CO2, estimates of anthropogenic emissions and models of oceanic CO2 uptake, one can estimate net global CO2 exchange between the atmosphere and terrestrial ecosystems as the residual of the balanced global carbon budget. Estimates from the Global Carbon Project 2016 show that terrestrial ecosystems are a growing sink for atmospheric CO2 (averaging 2.12 Gt C y-1 for the period 1959-2015 with a growth rate of 0.03 Gt C y-1 per year) but with considerable year-to-year variability (standard deviation of 1.07 Gt C y-1). Within the uncertainty of the observations, emissions estimates and ocean modeling, this residual calculation is a robust estimate of a global terrestrial sink for CO2. A task of terrestrial ecosystem science is to explain the trend and variability in this estimate. However, "within the uncertainty" is an important caveat. The uncertainty (2σ; 95% confidence interval) in fossil fuel emissions is 8.4% (±0.8 Gt C in 2015). Combined with uncertainty in other carbon budget components, the 2σ uncertainty surrounding the global net terrestrial ecosystem CO2 exchange is ±1.6 Gt C y-1. Ignoring the uncertainty, the estimate of a general terrestrial sink includes 2 years (1987 and 1998) in which terrestrial ecosystems are a small source of CO2 to the atmosphere. However, with 2σ uncertainty, terrestrial ecosystems may have been a source in as many as 18 years. We examine how well global terrestrial biosphere models simulate the trend and interannual variability of the global-budget estimate of the terrestrial sink within the context of this uncertainty (e.g., which models fall outside the 2σ uncertainty and in what years). Models are generally capable of reproducing the trend in net terrestrial exchange, but are less able to capture interannual variability and often fall outside the 2σ uncertainty. The trend in the residual carbon budget estimate is primarily associated with the increase in atmospheric CO2

  8. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates

    Science.gov (United States)

    Ronald E. McRoberts; James A. Westfall

    2014-01-01

    Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model...

  9. Spatial regression methods capture prediction uncertainty in species distribution model projections through time

    Science.gov (United States)

    Alan K. Swanson; Solomon Z. Dobrowski; Andrew O. Finley; James H. Thorne; Michael K. Schwartz

    2013-01-01

    The uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. Generalized linear mixed models (GLMM) have the ability to...

  10. 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

  11. The Effects of Uncertainty in Speed-Flow Curve Parameters on a Large-Scale Model

    DEFF Research Database (Denmark)

    Manzo, Stefano; Nielsen, Otto Anker; Prato, Carlo Giacomo

    2014-01-01

    -delay functions express travel time as a function of traffic flows and the theoretical capacity of the modeled facility. The U.S. Bureau of Public Roads (BPR) formula is one of the most extensively applied volume delay functions in practice. This study investigated uncertainty in the BPR parameters. Initially......-stage Danish national transport model. The results clearly highlight the importance to modeling purposes of taking into account BPR formula parameter uncertainty, expressed as a distribution of values rather than assumed point values. Indeed, the model output demonstrates a noticeable sensitivity to parameter...

  12. Numerical solution of continuous-time DSGE models under Poisson uncertainty

    DEFF Research Database (Denmark)

    Posch, Olaf; Trimborn, Timo

    We propose a simple and powerful method for determining the transition process in continuous-time DSGE models under Poisson uncertainty numerically. The idea is to transform the system of stochastic differential equations into a system of functional differential equations of the retarded type. We...... classes of models. We illustrate the algorithm simulating both the stochastic neoclassical growth model and the Lucas model under Poisson uncertainty which is motivated by the Barro-Rietz rare disaster hypothesis. We find that, even for non-linear policy functions, the maximum (absolute) error is very...

  13. A systematic approach to the modelling of measurements for uncertainty evaluation

    International Nuclear Information System (INIS)

    Sommer, K D; Weckenmann, A; Siebert, B R L

    2005-01-01

    The evaluation of measurement uncertainty is based on both, the knowledge about the measuring process and the quantities which influence the measurement result. The knowledge about the measuring process is represented by the model equation which expresses the interrelation between the measurand and the input quantities. Therefore, the modelling of the measurement is a key element of modern uncertainty evaluation. A modelling concept has been developed that is based on the idea of the measuring chain. It gets on with only a few generic model structures. From this concept, a practical stepwise procedure has been derived

  14. Deterministic Method for Obtaining Nominal and Uncertainty Models of CD Drives

    DEFF Research Database (Denmark)

    Vidal, Enrique Sanchez; Stoustrup, Jakob; Andersen, Palle

    2002-01-01

    In this paper a deterministic method for obtaining the nominal and uncertainty models of the focus loop in a CD-player is presented based on parameter identification and measurements in the focus loop of 12 actual CD drives that differ by having worst-case behaviors with respect to various...... properties. The method provides a systematic way to derive a nominal average model as well as a structures multiplicative input uncertainty model, and it is demonstrated how to apply mu-theory to design a controller based on the models obtained that meets certain robust performance criteria....

  15. CAPTURING UNCERTAINTY IN UNSATURATED-ZONE FLOW USING DIFFERENT CONCEPTUAL MODELS OF FRACTURE-MATRIX INTERACTION

    International Nuclear Information System (INIS)

    SUSAN J. ALTMAN, MICHAEL L. WILSON, GUMUNDUR S. BODVARSSON

    1998-01-01

    Preliminary calculations show that the two different conceptual models of fracture-matrix interaction presented here yield different results pertinent to the performance of the potential repository at Yucca Mountain. Namely, each model produces different ranges of flow in the fractures, where radionuclide transport is thought to be most important. This method of using different flow models to capture both conceptual model and parameter uncertainty ensures that flow fields used in TSPA calculations will be reasonably calibrated to the available data while still capturing this uncertainty. This method also allows for the use of three-dimensional flow fields for the TSPA-VA calculations

  16. What Makes Hydrologic Models Differ? Using SUMMA to Systematically Explore Model Uncertainty and Error

    Science.gov (United States)

    Bennett, A.; Nijssen, B.; Chegwidden, O.; Wood, A.; Clark, M. P.

    2017-12-01

    Model intercomparison experiments have been conducted to quantify the variability introduced during the model development process, but have had limited success in identifying the sources of this model variability. The Structure for Unifying Multiple Modeling Alternatives (SUMMA) has been developed as a framework which defines a general set of conservation equations for mass and energy as well as a common core of numerical solvers along with the ability to set options for choosing between different spatial discretizations and flux parameterizations. SUMMA can be thought of as a framework for implementing meta-models which allows for the investigation of the impacts of decisions made during the model development process. Through this flexibility we develop a hierarchy of definitions which allows for models to be compared to one another. This vocabulary allows us to define the notion of weak equivalence between model instantiations. Through this weak equivalence we develop the concept of model mimicry, which can be used to investigate the introduction of uncertainty and error during the modeling process as well as provide a framework for identifying modeling decisions which may complement or negate one another. We instantiate SUMMA instances that mimic the behaviors of the Variable Infiltration Capacity (VIC) model and the Precipitation Runoff Modeling System (PRMS) by choosing modeling decisions which are implemented in each model. We compare runs from these models and their corresponding mimics across the Columbia River Basin located in the Pacific Northwest of the United States and Canada. From these comparisons, we are able to determine the extent to which model implementation has an effect on the results, as well as determine the changes in sensitivity of parameters due to these implementation differences. By examining these changes in results and sensitivities we can attempt to postulate changes in the modeling decisions which may provide better estimation of

  17. Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

    DEFF Research Database (Denmark)

    Dotto, C. B.; Mannina, G.; Kleidorfer, M.

    2012-01-01

    -UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multiobjective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty...... techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside...... the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper...

  18. Use of Paired Simple and Complex Models to Reduce Predictive Bias and Quantify Uncertainty

    DEFF Research Database (Denmark)

    Doherty, John; Christensen, Steen

    2011-01-01

    -constrained uncertainty analysis. Unfortunately, however, many system and process details on which uncertainty may depend are, by design, omitted from simple models. This can lead to underestimation of the uncertainty associated with many predictions of management interest. The present paper proposes a methodology...... of these details born of the necessity for model outputs to replicate observations of historical system behavior. In contrast, the rapid run times and general numerical reliability of simple models often promulgates good calibration and ready implementation of sophisticated methods of calibration...... that attempts to overcome the problems associated with complex models on the one hand and simple models on the other hand, while allowing access to the benefits each of them offers. It provides a theoretical analysis of the simplification process from a subspace point of view, this yielding insights...

  19. Investigation of discrete-fracture network conceptual model uncertainty at Forsmark

    International Nuclear Information System (INIS)

    Geier, Joel

    2011-04-01

    In the present work a discrete fracture model has been further developed and implemented using the latest SKB site investigation data. The model can be used for analysing the fracture network and to model flow through the rock in Forsmark. The aim has been to study uncertainties in the hydrological discrete fracture network (DFN) for the repository model. More specifically the objective has been to study to which extent available data limits uncertainties in the DFN model and how data that can be obtained in future underground work can further limit these uncertainties. Moreover, the effects on deposition hole utilisation and placement have been investigated as well as the effects on the flow to deposition holes

  20. MODELS OF AIR TRAFFIC CONTROLLERS ERRORS PREVENTION IN TERMINAL CONTROL AREAS UNDER UNCERTAINTY CONDITIONS

    Directory of Open Access Journals (Sweden)

    Volodymyr Kharchenko

    2017-03-01

    Full Text Available Purpose: the aim of this study is to research applied models of air traffic controllers’ errors prevention in terminal control areas (TMA under uncertainty conditions. In this work the theoretical framework descripting safety events and errors of air traffic controllers connected with the operations in TMA is proposed. Methods: optimisation of terminal control area formal description based on the Threat and Error management model and the TMA network model of air traffic flows. Results: the human factors variables associated with safety events in work of air traffic controllers under uncertainty conditions were obtained. The Threat and Error management model application principles to air traffic controller operations and the TMA network model of air traffic flows were proposed. Discussion: Information processing context for preventing air traffic controller errors, examples of threats in work of air traffic controllers, which are relevant for TMA operations under uncertainty conditions.

  1. Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model

    KAUST Repository

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

    2016-01-01

    Polynomial Chaos expansions are used to analyze u