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Sample records for statistical uncertainty analysis

  1. Uncertainty analysis with statistically correlated failure data

    International Nuclear Information System (INIS)

    Modarres, M.; Dezfuli, H.; Roush, M.L.

    1987-01-01

    Likelihood of occurrence of the top event of a fault tree or sequences of an event tree is estimated from the failure probability of components that constitute the events of the fault/event tree. Component failure probabilities are subject to statistical uncertainties. In addition, there are cases where the failure data are statistically correlated. At present most fault tree calculations are based on uncorrelated component failure data. This chapter describes a methodology for assessing the probability intervals for the top event failure probability of fault trees or frequency of occurrence of event tree sequences when event failure data are statistically correlated. To estimate mean and variance of the top event, a second-order system moment method is presented through Taylor series expansion, which provides an alternative to the normally used Monte Carlo method. For cases where component failure probabilities are statistically correlated, the Taylor expansion terms are treated properly. Moment matching technique is used to obtain the probability distribution function of the top event through fitting the Johnson Ssub(B) distribution. The computer program, CORRELATE, was developed to perform the calculations necessary for the implementation of the method developed. (author)

  2. Statistical uncertainty analysis of radon transport in nonisothermal, unsaturated soils

    International Nuclear Information System (INIS)

    Holford, D.J.; Owczarski, P.C.; Gee, G.W.; Freeman, H.D.

    1990-10-01

    To accurately predict radon fluxes soils to the atmosphere, we must know more than the radium content of the soil. Radon flux from soil is affected not only by soil properties, but also by meteorological factors such as air pressure and temperature changes at the soil surface, as well as the infiltration of rainwater. Natural variations in meteorological factors and soil properties contribute to uncertainty in subsurface model predictions of radon flux, which, when coupled with a building transport model, will also add uncertainty to predictions of radon concentrations in homes. A statistical uncertainty analysis using our Rn3D finite-element numerical model was conducted to assess the relative importance of these meteorological factors and the soil properties affecting radon transport. 10 refs., 10 figs., 3 tabs

  3. Statistical analysis of the uncertainty related to flood hazard appraisal

    Science.gov (United States)

    Notaro, Vincenza; Freni, Gabriele

    2015-12-01

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

  4. Statistically based uncertainty assessments in nuclear risk analysis

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  5. A Preliminary Study on Sensitivity and Uncertainty Analysis with Statistic Method: Uncertainty Analysis with Cross Section Sampling from Lognormal Distribution

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-10-15

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

  6. A Preliminary Study on Sensitivity and Uncertainty Analysis with Statistic Method: Uncertainty Analysis with Cross Section Sampling from Lognormal Distribution

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  7. Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

    Science.gov (United States)

    He, Yuning; Davies, Misty Dawn

    2014-01-01

    The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

  8. 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.

  9. Statistical uncertainties and unrecognized relationships

    International Nuclear Information System (INIS)

    Rankin, J.P.

    1985-01-01

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

  10. Uncertainty analysis

    International Nuclear Information System (INIS)

    Thomas, R.E.

    1982-03-01

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

  11. To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis.

    Science.gov (United States)

    Theorell, Axel; Leweke, Samuel; Wiechert, Wolfgang; Nöh, Katharina

    2017-11-01

    13 C Metabolic Fluxes Analysis ( 13 C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of 13 C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to 13 C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in 13 C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer. © 2017 Wiley Periodicals, Inc.

  12. Visualizing Summary Statistics and Uncertainty

    KAUST Repository

    Potter, K.

    2010-08-12

    The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution. © 2010 The Eurographics Association and Blackwell Publishing Ltd.

  13. Visualizing Summary Statistics and Uncertainty

    KAUST Repository

    Potter, K.; Kniss, J.; Riesenfeld, R.; Johnson, C.R.

    2010-01-01

    The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use-case of these new plots is presented, demonstrating their ability to present high-level overviews as well as detailed insight into the salient features of the underlying data distribution. © 2010 The Eurographics Association and Blackwell Publishing Ltd.

  14. Statistical Uncertainty in the Medicare Shared Savings...

    Data.gov (United States)

    U.S. Department of Health & Human Services — According to analysis reported in Statistical Uncertainty in the Medicare Shared Savings Program published in Volume 2, Issue 4 of the Medicare and Medicaid Research...

  15. Bayesian analysis applied to statistical uncertainties of extreme response distributions of offshore wind turbines

    NARCIS (Netherlands)

    Cheng, P.W.; Kuik, van G.A.M.; Bussel, van G.J.W.; Vrouwenvelder, A.C.W.M.

    2002-01-01

    Extreme response is an important design variable for wind turbines. The statistical uncertainties concerning the extreme response distribution are simulated here with data concerning physical characteristics obtained from measurements. The extreme responses are the flap moment at the blade root and

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

    Directory of Open Access Journals (Sweden)

    W Ghie

    2016-04-01

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

  17. Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification

    Directory of Open Access Journals (Sweden)

    A. Sarri

    2012-06-01

    Full Text Available Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake. Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the outer product emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the leave-one-out method.

  18. Deterministic uncertainty analysis

    International Nuclear Information System (INIS)

    Worley, B.A.

    1987-01-01

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

  19. Uncertainty analysis of reactor safety systems with statistically correlated failure data

    International Nuclear Information System (INIS)

    Dezfuli, H.; Modarres, M.

    1985-01-01

    The probability of occurrence of the top event of a fault tree is estimated from failure probability of components that constitute the fault tree. Component failure probabilities are subject to statistical uncertainties. In addition, there are cases where the failure data are statistically correlated. Most fault tree evaluations have so far been based on uncorrelated component failure data. The subject of this paper is the description of a method of assessing the probability intervals for the top event failure probability of fault trees when component failure data are statistically correlated. To estimate the mean and variance of the top event, a second-order system moment method is presented through Taylor series expansion, which provides an alternative to the normally used Monte-Carlo method. For cases where component failure probabilities are statistically correlated, the Taylor expansion terms are treated properly. A moment matching technique is used to obtain the probability distribution function of the top event through fitting a Johnson Ssub(B) distribution. The computer program (CORRELATE) was developed to perform the calculations necessary for the implementation of the method developed. The CORRELATE code is very efficient and consumes minimal computer time. This is primarily because it does not employ the time-consuming Monte-Carlo method. (author)

  20. Towards an Industrial Application of Statistical Uncertainty Analysis Methods to Multi-physical Modelling and Safety Analyses

    International Nuclear Information System (INIS)

    Zhang, Jinzhao; Segurado, Jacobo; Schneidesch, Christophe

    2013-01-01

    Since 1980's, Tractebel Engineering (TE) has being developed and applied a multi-physical modelling and safety analyses capability, based on a code package consisting of the best estimate 3D neutronic (PANTHER), system thermal hydraulic (RELAP5), core sub-channel thermal hydraulic (COBRA-3C), and fuel thermal mechanic (FRAPCON/FRAPTRAN) codes. A series of methodologies have been developed to perform and to license the reactor safety analysis and core reload design, based on the deterministic bounding approach. Following the recent trends in research and development as well as in industrial applications, TE has been working since 2010 towards the application of the statistical sensitivity and uncertainty analysis methods to the multi-physical modelling and licensing safety analyses. In this paper, the TE multi-physical modelling and safety analyses capability is first described, followed by the proposed TE best estimate plus statistical uncertainty analysis method (BESUAM). The chosen statistical sensitivity and uncertainty analysis methods (non-parametric order statistic method or bootstrap) and tool (DAKOTA) are then presented, followed by some preliminary results of their applications to FRAPCON/FRAPTRAN simulation of OECD RIA fuel rod codes benchmark and RELAP5/MOD3.3 simulation of THTF tests. (authors)

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-08-15

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

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

    Science.gov (United States)

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

    2010-08-01

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

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  4. Worry, Intolerance of Uncertainty, and Statistics Anxiety

    Science.gov (United States)

    Williams, Amanda S.

    2013-01-01

    Statistics anxiety is a problem for most graduate students. This study investigates the relationship between intolerance of uncertainty, worry, and statistics anxiety. Intolerance of uncertainty was significantly related to worry, and worry was significantly related to three types of statistics anxiety. Six types of statistics anxiety were…

  5. Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

    Science.gov (United States)

    Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

    2018-03-01

    Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre - stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.

  6. Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

    Science.gov (United States)

    Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

    2018-06-01

    Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2-D seismic reflection data processing flow focused on pre-stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching, to estimate the uncertainties of the depths of key horizons near the Deep Sea Drilling Project (DSDP) borehole 258 (DSDP-258) located in the Mentelle Basin, southwest of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ±2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent to the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program, leg 369.

  7. Perspectives on the application of order-statistics in best-estimate plus uncertainty nuclear safety analysis

    International Nuclear Information System (INIS)

    Martin, Robert P.; Nutt, William T.

    2011-01-01

    Research highlights: → Historical recitation on application of order-statistics models to nuclear power plant thermal-hydraulics safety analysis. → Interpretation of regulatory language regarding 10 CFR 50.46 reference to a 'high level of probability'. → Derivation and explanation of order-statistics-based evaluation methodologies considering multi-variate acceptance criteria. → Summary of order-statistics models and recommendations to the nuclear power plant thermal-hydraulics safety analysis community. - Abstract: The application of order-statistics in best-estimate plus uncertainty nuclear safety analysis has received a considerable amount of attention from methodology practitioners, regulators, and academia. At the root of the debate are two questions: (1) what is an appropriate quantitative interpretation of 'high level of probability' in regulatory language appearing in the LOCA rule, 10 CFR 50.46 and (2) how best to mathematically characterize the multi-variate case. An original derivation is offered to provide a quantitative basis for 'high level of probability.' At root of the second question is whether one should recognize a probability statement based on the tolerance region method of Wald and Guba, et al., for multi-variate problems, one explicitly based on the regulatory limits, best articulated in the Wallis-Nutt 'Testing Method', or something else entirely. This paper reviews the origins of the different positions, key assumptions, limitations, and relationship to addressing acceptance criteria. It presents a mathematical interpretation of the regulatory language, including a complete derivation of uni-variate order-statistics (as credited in AREVA's Realistic Large Break LOCA methodology) and extension to multi-variate situations. Lastly, it provides recommendations for LOCA applications, endorsing the 'Testing Method' and addressing acceptance methods allowing for limited sample failures.

  8. Deterministic uncertainty analysis

    International Nuclear Information System (INIS)

    Worley, B.A.

    1987-12-01

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

  9. Investigating lithological and geophysical relationships with applications to geological uncertainty analysis using Multiple-Point Statistical methods

    DEFF Research Database (Denmark)

    Barfod, Adrian

    The PhD thesis presents a new method for analyzing the relationship between resistivity and lithology, as well as a method for quantifying the hydrostratigraphic modeling uncertainty related to Multiple-Point Statistical (MPS) methods. Three-dimensional (3D) geological models are im...... is to improve analysis and research of the resistivity-lithology relationship and ensemble geological/hydrostratigraphic modeling. The groundwater mapping campaign in Denmark, beginning in the 1990’s, has resulted in the collection of large amounts of borehole and geophysical data. The data has been compiled...... in two publicly available databases, the JUPITER and GERDA databases, which contain borehole and geophysical data, respectively. The large amounts of available data provided a unique opportunity for studying the resistivity-lithology relationship. The method for analyzing the resistivity...

  10. A formal statistical approach to representing uncertainty in rainfall-runoff modelling with focus on residual analysis and probabilistic output evaluation - Distinguishing simulation and prediction

    DEFF Research Database (Denmark)

    Breinholt, Anders; Møller, Jan Kloppenborg; Madsen, Henrik

    2012-01-01

    While there seems to be consensus that hydrological model outputs should be accompanied with an uncertainty estimate the appropriate method for uncertainty estimation is not agreed upon and a debate is ongoing between advocators of formal statistical methods who consider errors as stochastic...... and GLUE advocators who consider errors as epistemic, arguing that the basis of formal statistical approaches that requires the residuals to be stationary and conform to a statistical distribution is unrealistic. In this paper we take a formal frequentist approach to parameter estimation and uncertainty...... necessary but the statistical assumptions were nevertheless not 100% justified. The residual analysis showed that significant autocorrelation was present for all simulation models. We believe users of formal approaches to uncertainty evaluation within hydrology and within environmental modelling in general...

  11. Experimental uncertainty estimation and statistics for data having interval uncertainty.

    Energy Technology Data Exchange (ETDEWEB)

    Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)

    2007-05-01

    This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.

  12. 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

  13. Some uncertainty results obtained by the statistical version of the KARATE code system related to core design and safety analysis

    Energy Technology Data Exchange (ETDEWEB)

    Panka, Istvan; Hegyi, Gyoergy; Maraczy, Csaba; Temesvari, Emese [Hungarian Academy of Sciences, Budapest (Hungary). Reactor Analysis Dept.

    2017-11-15

    The best-estimate KARATE code system has been widely used for core design calculations and simulations of slow transients of VVER reactors. Recently there has been an increasing need for assessing the uncertainties of such calculations by propagating the basic input uncertainties of the models through the full calculation chain. In order to determine the uncertainties of quantities of interest during the burnup, the statistical version of the KARATE code system has been elaborated. In the first part of the paper, the main features of the new code system are discussed. The applied statistical method is based on Monte-Carlo sampling of the considered input data taking into account mainly the covariance matrices of the cross sections and/or the technological uncertainties. In the second part of the paper, only the uncertainties of cross sections are considered and an equilibrium cycle related to a VVER-440 type reactor is investigated. The burnup dependence of the uncertainties of some safety related parameters (e.g. critical boron concentration, rod worth, feedback coefficients, assembly-wise radial power and burnup distribution) are discussed and compared to the recently used limits.

  14. Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor

    International Nuclear Information System (INIS)

    Wilson, G.E.

    1992-01-01

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

  15. Uncertainty analysis guide

    International Nuclear Information System (INIS)

    Andres, T.H.

    2002-05-01

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

  16. Uncertainty analysis guide

    Energy Technology Data Exchange (ETDEWEB)

    Andres, T.H

    2002-05-01

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

  17. Improvement of Statistical Decisions under Parametric Uncertainty

    Science.gov (United States)

    Nechval, Nicholas A.; Nechval, Konstantin N.; Purgailis, Maris; Berzins, Gundars; Rozevskis, Uldis

    2011-10-01

    A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Decision-making under uncertainty is a central problem in statistical inference, and has been formally studied in virtually all approaches to inference. The aim of the present paper is to show how the invariant embedding technique, the idea of which belongs to the authors, may be employed in the particular case of finding the improved statistical decisions under parametric uncertainty. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. It allows one to eliminate unknown parameters from the problem and to find the best invariant decision rule, which has smaller risk than any of the well-known decision rules. To illustrate the proposed technique, application examples are given.

  18. Reliability analysis under epistemic uncertainty

    International Nuclear Information System (INIS)

    Nannapaneni, Saideep; Mahadevan, Sankaran

    2016-01-01

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

  19. 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...

  20. The use of Monte-Carlo simulation and order statistics for uncertainty analysis of a LBLOCA transient (LOFT-L2-5)

    International Nuclear Information System (INIS)

    Chojnacki, E.; Benoit, J.P.

    2007-01-01

    Best estimate computer codes are increasingly used in nuclear industry for the accident management procedures and have been planned to be used for the licensing procedures. Contrary to conservative codes which are supposed to give penalizing results, best estimate codes attempt to calculate accidental transients in a realistic way. It becomes therefore of prime importance, in particular for technical organization as IRSN in charge of safety assessment, to know the uncertainty on the results of such codes. Thus, CSNI has sponsored few years ago (published in 1998) the Uncertainty Methods Study (UMS) program on uncertainty methodologies used for a SBLOCA transient (LSTF-CL-18) and is now supporting the BEMUSE program for a LBLOCA transient (LOFT-L2-5). The large majority of BEMUSE participants (9 out of 10) use uncertainty methodologies based on a probabilistic modelling and all of them use Monte-Carlo simulations to propagate the uncertainties through their computer codes. Also, all of 'probabilistic participants' intend to use order statistics to determine the sampling size of the Monte-Carlo simulation and to derive the uncertainty ranges associated to their computer calculations. The first aim of this paper is to remind the advantages and also the assumptions of the probabilistic modelling and more specifically of order statistics (as Wilks' formula) in uncertainty methodologies. Indeed Monte-Carlo methods provide flexible and extremely powerful techniques for solving many of the uncertainty propagation problems encountered in nuclear safety analysis. However it is important to keep in mind that probabilistic methods are data intensive. That means, probabilistic methods cannot produce robust results unless a considerable body of information has been collected. A main interest of the use of order statistics results is to allow to take into account an unlimited number of uncertain parameters and, from a restricted number of code calculations to provide statistical

  1. Sensitivity and uncertainty analysis

    CERN Document Server

    Cacuci, Dan G; Navon, Ionel Michael

    2005-01-01

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

  2. Understanding and reducing statistical uncertainties in nebular abundance determinations

    Science.gov (United States)

    Wesson, R.; Stock, D. J.; Scicluna, P.

    2012-06-01

    Whenever observations are compared to theories, an estimate of the uncertainties associated with the observations is vital if the comparison is to be meaningful. However, many or even most determinations of temperatures, densities and abundances in photoionized nebulae do not quote the associated uncertainty. Those that do typically propagate the uncertainties using analytical techniques which rely on assumptions that generally do not hold. Motivated by this issue, we have developed Nebular Empirical Analysis Tool (NEAT), a new code for calculating chemical abundances in photoionized nebulae. The code carries out a standard analysis of lists of emission lines using long-established techniques to estimate the amount of interstellar extinction, calculate representative temperatures and densities, compute ionic abundances from both collisionally excited lines and recombination lines, and finally to estimate total elemental abundances using an ionization correction scheme. NEATuses a Monte Carlo technique to robustly propagate uncertainties from line flux measurements through to the derived abundances. We show that, for typical observational data, this approach is superior to analytic estimates of uncertainties. NEAT also accounts for the effect of upward biasing on measurements of lines with low signal-to-noise ratio, allowing us to accurately quantify the effect of this bias on abundance determinations. We find not only that the effect can result in significant overestimates of heavy element abundances derived from weak lines, but also that taking it into account reduces the uncertainty of these abundance determinations. Finally, we investigate the effect of possible uncertainties in R, the ratio of selective-to-total extinction, on abundance determinations. We find that the uncertainty due to this parameter is negligible compared to the statistical uncertainties due to typical line flux measurement uncertainties.

  3. Maximum entropy prior uncertainty and correlation of statistical economic data

    NARCIS (Netherlands)

    Dias, Rodriques J.F.

    2016-01-01

    Empirical estimates of source statistical economic data such as trade flows, greenhouse gas emissions or employment figures are always subject to uncertainty (stemming from measurement errors or confidentiality) but information concerning that uncertainty is often missing. This paper uses concepts

  4. Analysis of uncertainties and convergence of the statistical quantities in turbulent wall-bounded flows by means of a physically based criterion

    Science.gov (United States)

    Andrade, João Rodrigo; Martins, Ramon Silva; Thompson, Roney Leon; Mompean, Gilmar; da Silveira Neto, Aristeu

    2018-04-01

    The present paper provides an analysis of the statistical uncertainties associated with direct numerical simulation (DNS) results and experimental data for turbulent channel and pipe flows, showing a new physically based quantification of these errors, to improve the determination of the statistical deviations between DNSs and experiments. The analysis is carried out using a recently proposed criterion by Thompson et al. ["A methodology to evaluate statistical errors in DNS data of plane channel flows," Comput. Fluids 130, 1-7 (2016)] for fully turbulent plane channel flows, where the mean velocity error is estimated by considering the Reynolds stress tensor, and using the balance of the mean force equation. It also presents how the residual error evolves in time for a DNS of a plane channel flow, and the influence of the Reynolds number on its convergence rate. The root mean square of the residual error is shown in order to capture a single quantitative value of the error associated with the dimensionless averaging time. The evolution in time of the error norm is compared with the final error provided by DNS data of similar Reynolds numbers available in the literature. A direct consequence of this approach is that it was possible to compare different numerical results and experimental data, providing an improved understanding of the convergence of the statistical quantities in turbulent wall-bounded flows.

  5. Analysis of uncertainties of thermal hydraulic calculations

    International Nuclear Information System (INIS)

    Macek, J.; Vavrin, J.

    2002-12-01

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

  6. Uncertainty analysis techniques

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  7. 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.

  8. 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

  9. 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...

  10. DS02 uncertainty analysis

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  11. 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...

  12. Incorporation of systematic uncertainties in statistical decision rules

    International Nuclear Information System (INIS)

    Wichers, V.A.

    1994-02-01

    The influence of systematic uncertainties on statistical hypothesis testing is an underexposed subject. Systematic uncertainties cannot be incorporated in hypothesis tests, but they deteriorate the performance of these tests. A wrong treatment of systematic uncertainties in verification applications in safeguards leads to false assessment of the strength of the safeguards measure, and thus undermines the safeguards system. The effects of systematic uncertainties on decision errors in hypothesis testing are analyzed quantitatively for an example from the safeguards practice. (LEU-HEU verification of UF 6 enrichment in centrifuge enrichment plants). It is found that the only proper way to tackle systematic uncertainties is reduction to sufficiently low levels; criteria for these are proposed. Although conclusions were obtained from study of a single practical application, it is believed that they hold generally: for all sources of systematic uncertainties, all statistical decision rules, and all applications. (orig./HP)

  13. The STAT7 Code for Statistical Propagation of Uncertainties In Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors

    Energy Technology Data Exchange (ETDEWEB)

    Dunn, Floyd E. [Argonne National Lab. (ANL), Argonne, IL (United States); Hu, Lin-wen [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Nuclear Reactor Lab.; Wilson, Erik [Argonne National Lab. (ANL), Argonne, IL (United States)

    2016-12-01

    The STAT code was written to automate many of the steady-state thermal hydraulic safety calculations for the MIT research reactor, both for conversion of the reactor from high enrichment uranium fuel to low enrichment uranium fuel and for future fuel re-loads after the conversion. A Monte-Carlo statistical propagation approach is used to treat uncertainties in important parameters in the analysis. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux or departure from nucleate boiling or onset of flow instability; but additional margin is obtained by basing the limiting safety settings on avoiding onset of nucleate boiling. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one stripe of a fuel element. The stripes run the length of the fuel, from the bottom to the top. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis has been performed by analyzing the margin to ONB for each axial node of each stripe of each plate of each element in the core.

  14. Uncertainty Management and Sensitivity Analysis

    DEFF Research Database (Denmark)

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

    2018-01-01

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

  15. 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.

  16. The neurobiology of uncertainty: implications for statistical learning.

    Science.gov (United States)

    Hasson, Uri

    2017-01-05

    The capacity for assessing the degree of uncertainty in the environment relies on estimating statistics of temporally unfolding inputs. This, in turn, allows calibration of predictive and bottom-up processing, and signalling changes in temporally unfolding environmental features. In the last decade, several studies have examined how the brain codes for and responds to input uncertainty. Initial neurobiological experiments implicated frontoparietal and hippocampal systems, based largely on paradigms that manipulated distributional features of visual stimuli. However, later work in the auditory domain pointed to different systems, whose activation profiles have interesting implications for computational and neurobiological models of statistical learning (SL). This review begins by briefly recapping the historical development of ideas pertaining to the sensitivity to uncertainty in temporally unfolding inputs. It then discusses several issues at the interface of studies of uncertainty and SL. Following, it presents several current treatments of the neurobiology of uncertainty and reviews recent findings that point to principles that serve as important constraints on future neurobiological theories of uncertainty, and relatedly, SL. This review suggests it may be useful to establish closer links between neurobiological research on uncertainty and SL, considering particularly mechanisms sensitive to local and global structure in inputs, the degree of input uncertainty, the complexity of the system generating the input, learning mechanisms that operate on different temporal scales and the use of learnt information for online prediction.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

  17. High cumulants of conserved charges and their statistical uncertainties

    Science.gov (United States)

    Li-Zhu, Chen; Ye-Yin, Zhao; Xue, Pan; Zhi-Ming, Li; Yuan-Fang, Wu

    2017-10-01

    We study the influence of measured high cumulants of conserved charges on their associated statistical uncertainties in relativistic heavy-ion collisions. With a given number of events, the measured cumulants randomly fluctuate with an approximately normal distribution, while the estimated statistical uncertainties are found to be correlated with corresponding values of the obtained cumulants. Generally, with a given number of events, the larger the cumulants we measure, the larger the statistical uncertainties that are estimated. The error-weighted averaged cumulants are dependent on statistics. Despite this effect, however, it is found that the three sigma rule of thumb is still applicable when the statistics are above one million. Supported by NSFC (11405088, 11521064, 11647093), Major State Basic Research Development Program of China (2014CB845402) and Ministry of Science and Technology (MoST) (2016YFE0104800)

  18. Fuzzy Uncertainty Evaluation for Fault Tree Analysis

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-05-15

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

  19. The characterisation and evaluation of uncertainty in probabilistic risk analysis

    International Nuclear Information System (INIS)

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

    1980-10-01

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

  20. Mathematical Analysis of Uncertainty

    Directory of Open Access Journals (Sweden)

    Angel GARRIDO

    2016-01-01

    Full Text Available Classical Logic showed early its insufficiencies for solving AI problems. The introduction of Fuzzy Logic aims at this problem. There have been research in the conventional Rough direction alone or in the Fuzzy direction alone, and more recently, attempts to combine both into Fuzzy Rough Sets or Rough Fuzzy Sets. We analyse some new and powerful tools in the study of Uncertainty, as the Probabilistic Graphical Models, Chain Graphs, Bayesian Networks, and Markov Networks, integrating our knowledge of graphs and probability.

  1. A pseudo-statistical approach to treat choice uncertainty: the example of partitioning allocation methods

    NARCIS (Netherlands)

    Mendoza Beltran, A.; Heijungs, R.; Guinée, J.; Tukker, A.

    2016-01-01

    Purpose: Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and statistical methods, no method exists that propagate this source of uncertainty for all relevant processes

  2. 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

  3. Statistical data analysis handbook

    National Research Council Canada - National Science Library

    Wall, Francis J

    1986-01-01

    It must be emphasized that this is not a text book on statistics. Instead it is a working tool that presents data analysis in clear, concise terms which can be readily understood even by those without formal training in statistics...

  4. A statistical approach to determining the uncertainty of peat thickness

    Directory of Open Access Journals (Sweden)

    J. Torppa

    2011-06-01

    Full Text Available This paper presents statistical studies of peat thickness to define its expected maximum variation (∆dm(∆r as a function of separation distance Δr. The aim was to provide an estimate of the observational uncertainty in peat depth due to positioning error, and the prediction uncertainty of the computed model. The data were GPS position and ground penetrating radar depth measurements of six mires in different parts of Finland. The calculated observational uncertainty for Finnish mires in general caused, for example, by a 20 m positioning error, is 43 cm in depth with 95 % confidence. The peat depth statistics differed among the six mires, and it is recommended that the mire specific function ∆dm(∆r is defined for each individual mire to obtain the best estimate of observational uncertainty. Knowledge of the observational error and function ∆dm(∆r should be used in peat depth modelling for defining the uncertainty of depth predictions.

  5. Uncertainty Propagation in Monte Carlo Depletion Analysis

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  6. A statistical view of uncertainty in expert systems

    International Nuclear Information System (INIS)

    Spiegelhalter, D.J.

    1986-01-01

    The constructors of expert systems interpret ''uncertainty'' in a wide sense and have suggested a variety of qualitative and quantitative techniques for handling the concept, such as the theory of ''endorsements,'' fuzzy reasoning, and belief functions. After a brief selective review of procedures that do not adhere to the laws of probability, it is argued that a subjectivist Bayesian view of uncertainty, if flexibly applied, can provide many of the features demanded by expert systems. This claim is illustrated with a number of examples of probabilistic reasoning, and a connection drawn with statistical work on the graphical representation of multivariate distributions. Possible areas of future research are outlined

  7. Uncertainty propagation for statistical impact prediction of space debris

    Science.gov (United States)

    Hoogendoorn, R.; Mooij, E.; Geul, J.

    2018-01-01

    Predictions of the impact time and location of space debris in a decaying trajectory are highly influenced by uncertainties. The traditional Monte Carlo (MC) method can be used to perform accurate statistical impact predictions, but requires a large computational effort. A method is investigated that directly propagates a Probability Density Function (PDF) in time, which has the potential to obtain more accurate results with less computational effort. The decaying trajectory of Delta-K rocket stages was used to test the methods using a six degrees-of-freedom state model. The PDF of the state of the body was propagated in time to obtain impact-time distributions. This Direct PDF Propagation (DPP) method results in a multi-dimensional scattered dataset of the PDF of the state, which is highly challenging to process. No accurate results could be obtained, because of the structure of the DPP data and the high dimensionality. Therefore, the DPP method is less suitable for practical uncontrolled entry problems and the traditional MC method remains superior. Additionally, the MC method was used with two improved uncertainty models to obtain impact-time distributions, which were validated using observations of true impacts. For one of the two uncertainty models, statistically more valid impact-time distributions were obtained than in previous research.

  8. 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

  9. Uncertainty analysis for hot channel

    International Nuclear Information System (INIS)

    Panka, I.; Kereszturi, A.

    2006-01-01

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

  10. LOFT differential pressure uncertainty analysis

    International Nuclear Information System (INIS)

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

    1977-03-01

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

  11. Uncertainty analysis of the FRAP code

    International Nuclear Information System (INIS)

    Peck, S.O.

    1978-01-01

    A user oriented, automated uncertainty analysis capability has been built into the FRAP code (Fuel Rod Analysis Program) and applied to a PWR fuel rod undergoing a LOCA. The method of uncertainty analysis is the Response Surface Method (RSM). (author)

  12. Optimization of FRAP uncertainty analysis option

    International Nuclear Information System (INIS)

    Peck, S.O.

    1979-10-01

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

  13. Uncertainty Quantification and Statistical Engineering for Hypersonic Entry Applications

    Science.gov (United States)

    Cozmuta, Ioana

    2011-01-01

    NASA has invested significant resources in developing and validating a mathematical construct for TPS margin management: a) Tailorable for low/high reliability missions; b) Tailorable for ablative/reusable TPS; c) Uncertainty Quantification and Statistical Engineering are valuable tools not exploited enough; and d) Need to define strategies combining both Theoretical Tools and Experimental Methods. The main reason for this lecture is to give a flavor of where UQ and SE could contribute and hope that the broader community will work with us to improve in these areas.

  14. The Precautionary Principle and statistical approaches to uncertainty

    DEFF Research Database (Denmark)

    Keiding, Niels; Budtz-Jørgensen, Esben

    2004-01-01

    is unhelpful, because lack of significance can be due either to uninformative data or to genuine lack of effect (the Type II error problem). Its inversion, bioequivalence testing, might sometimes be a model for the Precautionary Principle in its ability to "prove the null hypothesis". Current procedures...... for setting safe exposure levels are essentially derived from these classical statistical ideas, and we outline how uncertainties in the exposure and response measurements affect the no observed adverse effect level, the Benchmark approach and the "Hockey Stick" model. A particular problem concerns model...

  15. Validation of Fuel Performance Uncertainty for RIA Safety Analysis

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-10-15

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

  16. [The metrology of uncertainty: a study of vital statistics from Chile and Brazil].

    Science.gov (United States)

    Carvajal, Yuri; Kottow, Miguel

    2012-11-01

    This paper addresses the issue of uncertainty in the measurements used in public health analysis and decision-making. The Shannon-Wiener entropy measure was adapted to express the uncertainty contained in counting causes of death in official vital statistics from Chile. Based on the findings, the authors conclude that metrological requirements in public health are as important as the measurements themselves. The study also considers and argues for the existence of uncertainty associated with the statistics' performative properties, both by the way the data are structured as a sort of syntax of reality and by exclusion of what remains beyond the quantitative modeling used in each case. Following the legacy of pragmatic thinking and using conceptual tools from the sociology of translation, the authors emphasize that by taking uncertainty into account, public health can contribute to a discussion on the relationship between technology, democracy, and formation of a participatory public.

  17. 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.

  18. Correlated statistical uncertainties in coded-aperture imaging

    International Nuclear Information System (INIS)

    Fleenor, Matthew C.; Blackston, Matthew A.; Ziock, Klaus P.

    2015-01-01

    In nuclear security applications, coded-aperture imagers can provide a wealth of information regarding the attributes of both the radioactive and nonradioactive components of the objects being imaged. However, for optimum benefit to the community, spatial attributes need to be determined in a quantitative and statistically meaningful manner. To address a deficiency of quantifiable errors in coded-aperture imaging, we present uncertainty matrices containing covariance terms between image pixels for MURA mask patterns. We calculated these correlated uncertainties as functions of variation in mask rank, mask pattern over-sampling, and whether or not anti-mask data are included. Utilizing simulated point source data, we found that correlations arose when two or more image pixels were summed. Furthermore, we found that the presence of correlations was heightened by the process of over-sampling, while correlations were suppressed by the inclusion of anti-mask data and with increased mask rank. As an application of this result, we explored how statistics-based alarming is impacted in a radiological search scenario

  19. Uncertainty Assessments in Fast Neutron Activation Analysis

    International Nuclear Information System (INIS)

    W. D. James; R. Zeisler

    2000-01-01

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

  20. Probability and Statistics The Science of Uncertainty (Revised Edition)

    CERN Document Server

    Tabak, John

    2011-01-01

    Probability and Statistics, Revised Edition deals with the history of probability, describing the modern concept of randomness and examining "pre-probabilistic" ideas of what most people today would characterize as randomness. This revised book documents some historically important early uses of probability to illustrate some very important probabilistic questions. It goes on to explore statistics and the generations of mathematicians and non-mathematicians who began to address problems in statistical analysis, including the statistical structure of data sets as well as the theory of

  1. Uncertainty analysis in seismic tomography

    Science.gov (United States)

    Owoc, Bartosz; Majdański, Mariusz

    2017-04-01

    Velocity field from seismic travel time tomography depends on several factors like regularization, inversion path, model parameterization etc. The result also strongly depends on an initial velocity model and precision of travel times picking. In this research we test dependence on starting model in layered tomography and compare it with effect of picking precision. Moreover, in our analysis for manual travel times picking the uncertainty distribution is asymmetric. This effect is shifting the results toward faster velocities. For calculation we are using JIVE3D travel time tomographic code. We used data from geo-engineering and industrial scale investigations, which were collected by our team from IG PAS.

  2. LOFT uncertainty-analysis methodology

    International Nuclear Information System (INIS)

    Lassahn, G.D.

    1983-01-01

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

  3. LOFT uncertainty-analysis methodology

    International Nuclear Information System (INIS)

    Lassahn, G.D.

    1983-01-01

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

  4. Statistical Approaches Accomodating Uncertainty in Modern Genomic Data

    DEFF Research Database (Denmark)

    Skotte, Line

    the contributed method applicable to case-control studies as well as mapping of quantitative traits. The contributed method provides a needed association test for quantitative traits in the presence of uncertain genotypes and it further allows correction for population structure in association tests for disease...... the potential of the technological advances. The first of the four papers included in this thesis describes a new method for association mapping that accommodates uncertain genotypes from low-coverage re-sequencing data. The method allows uncertain genotypes using a score statistic based on the joint likelihood...... of the observed phenotypes and the observed sequencing data. This joint likelihood accounts for the genotype uncertainties via the posterior probabilities of each genotype given the observed sequencing data and the phenotype distributions are modelled using a generalised linear model framework which makes...

  5. Statistical-uncertainty-based adaptive filtering of lidar signals

    International Nuclear Information System (INIS)

    Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.

    2000-01-01

    An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H 2 O and the N 2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America

  6. Beginning statistics with data analysis

    CERN Document Server

    Mosteller, Frederick; Rourke, Robert EK

    2013-01-01

    This introduction to the world of statistics covers exploratory data analysis, methods for collecting data, formal statistical inference, and techniques of regression and analysis of variance. 1983 edition.

  7. Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

    Science.gov (United States)

    Fu, Guobin; Charles, Stephen P.; Chiew, Francis H. S.; Ekström, Marie; Potter, Nick J.

    2018-05-01

    The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature.

  8. 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

  9. Systematic Evaluation of Uncertainty in Material Flow Analysis

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-15

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

  11. Applied multivariate statistical analysis

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

  12. Rigorous Statistical Bounds in Uncertainty Quantification for One-Layer Turbulent Geophysical Flows

    Science.gov (United States)

    Qi, Di; Majda, Andrew J.

    2018-04-01

    Statistical bounds controlling the total fluctuations in mean and variance about a basic steady-state solution are developed for the truncated barotropic flow over topography. Statistical ensemble prediction is an important topic in weather and climate research. Here, the evolution of an ensemble of trajectories is considered using statistical instability analysis and is compared and contrasted with the classical deterministic instability for the growth of perturbations in one pointwise trajectory. The maximum growth of the total statistics in fluctuations is derived relying on the statistical conservation principle of the pseudo-energy. The saturation bound of the statistical mean fluctuation and variance in the unstable regimes with non-positive-definite pseudo-energy is achieved by linking with a class of stable reference states and minimizing the stable statistical energy. Two cases with dependence on initial statistical uncertainty and on external forcing and dissipation are compared and unified under a consistent statistical stability framework. The flow structures and statistical stability bounds are illustrated and verified by numerical simulations among a wide range of dynamical regimes, where subtle transient statistical instability exists in general with positive short-time exponential growth in the covariance even when the pseudo-energy is positive-definite. Among the various scenarios in this paper, there exist strong forward and backward energy exchanges between different scales which are estimated by the rigorous statistical bounds.

  13. Uncertainty analysis in safety assessment

    International Nuclear Information System (INIS)

    Lemos, Francisco Luiz de; Sullivan, Terry

    1997-01-01

    Nuclear waste disposal is a very complex subject which requires the study of many different fields of science, like hydro geology, meteorology, geochemistry, etc. In addition, the waste disposal facilities are designed to last for a very long period of time. Both of these conditions make safety assessment projections filled with uncertainty. This paper addresses approaches for treatment of uncertainties in the safety assessment modeling due to the variability of data and some current approaches used to deal with this problem. (author)

  14. Sensitivity analysis and related analysis : A survey of statistical techniques

    NARCIS (Netherlands)

    Kleijnen, J.P.C.

    1995-01-01

    This paper reviews the state of the art in five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when should which type of analysis be applied; which statistical

  15. Including uncertainty in hazard analysis through fuzzy measures

    International Nuclear Information System (INIS)

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

    1997-12-01

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

  16. The Precautionary Principle and statistical approaches to uncertainty

    DEFF Research Database (Denmark)

    Keiding, Niels; Budtz-Jørgensen, Esben

    2003-01-01

    Bayesian model averaging; Benchmark approach to safety standards in toxicology; dose-response relationship; environmental standards; exposure measurement uncertainty; Popper falsification......Bayesian model averaging; Benchmark approach to safety standards in toxicology; dose-response relationship; environmental standards; exposure measurement uncertainty; Popper falsification...

  17. The Precautionary Principle and Statistical Approaches to Uncertainty

    DEFF Research Database (Denmark)

    Keiding, Niels; Budtz-Jørgensen, Esben

    2005-01-01

    Bayesian model averaging; Benchmark approach to safety standars in toxicology; dose-response relationships; environmental standards; exposure measurement uncertainty; Popper falsification......Bayesian model averaging; Benchmark approach to safety standars in toxicology; dose-response relationships; environmental standards; exposure measurement uncertainty; Popper falsification...

  18. Statistical analysis of uncertainties of gamma-peak identification and area calculation in particulate air-filter environment radionuclide measurements using the results of a Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) organized intercomparison, Part I: Assessment of reliability and uncertainties of isotope detection and energy precision using artificial spiked test spectra, Part II: Assessment of the true type I error rate and the quality of peak area estimators in relation to type II errors using large numbers of natural spectra

    International Nuclear Information System (INIS)

    Zhang, W.; Zaehringer, M.; Ungar, K.; Hoffman, I.

    2008-01-01

    In this paper, the uncertainties of gamma-ray small peak analysis have been examined. As the intensity of a gamma-ray peak approaches its detection decision limit, derived parameters such as centroid channel energy, peak area, peak area uncertainty, baseline determination, and peak significance are statistically sensitive. The intercomparison exercise organized by the CTBTO provided an excellent opportunity for this to be studied. Near background levels, the false-positive and false-negative peak identification frequencies in artificial test spectra have been compared to statistically predictable limiting values. In addition, naturally occurring radon progeny were used to compare observed variance against nominal uncertainties. The results infer that the applied fit algorithms do not always represent the best estimator. Understanding the statistically predicted peak-finding limit is important for data evaluation and analysis assessment. Furthermore, these results are useful to optimize analytical procedures to achieve the best results

  19. Uncertainty analysis in safety assessment

    Energy Technology Data Exchange (ETDEWEB)

    Lemos, Francisco Luiz de [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN), Belo Horizonte, MG (Brazil); Sullivan, Terry [Brookhaven National Lab., Upton, NY (United States)

    1997-12-31

    Nuclear waste disposal is a very complex subject which requires the study of many different fields of science, like hydro geology, meteorology, geochemistry, etc. In addition, the waste disposal facilities are designed to last for a very long period of time. Both of these conditions make safety assessment projections filled with uncertainty. This paper addresses approaches for treatment of uncertainties in the safety assessment modeling due to the variability of data and some current approaches used to deal with this problem. (author) 13 refs.; e-mail: lemos at bnl.gov; sulliva1 at bnl.gov

  20. Uncertainty Analysis of Seebeck Coefficient and Electrical Resistivity Characterization

    Science.gov (United States)

    Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

    2014-01-01

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

  1. Uncertainty analysis of the FRAP code

    International Nuclear Information System (INIS)

    Peck, S.O.

    1978-01-01

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

  2. Estimating the measurement uncertainty in forensic blood alcohol analysis.

    Science.gov (United States)

    Gullberg, Rod G

    2012-04-01

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

  3. Uncertainty quantification and error analysis

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-01-01

    UQ studies all sources of error and uncertainty, including: systematic and stochastic measurement error; ignorance; limitations of theoretical models; limitations of numerical representations of those models; limitations on the accuracy and reliability of computations, approximations, and algorithms; and human error. A more precise definition for UQ is suggested below.

  4. Risk Characterization uncertainties associated description, sensitivity analysis

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  5. Statistical considerations on safety analysis

    International Nuclear Information System (INIS)

    Pal, L.; Makai, M.

    2004-01-01

    The authors have investigated the statistical methods applied to safety analysis of nuclear reactors and arrived at alarming conclusions: a series of calculations with the generally appreciated safety code ATHLET were carried out to ascertain the stability of the results against input uncertainties in a simple experimental situation. Scrutinizing those calculations, we came to the conclusion that the ATHLET results may exhibit chaotic behavior. A further conclusion is that the technological limits are incorrectly set when the output variables are correlated. Another formerly unnoticed conclusion of the previous ATHLET calculations that certain innocent looking parameters (like wall roughness factor, the number of bubbles per unit volume, the number of droplets per unit volume) can influence considerably such output parameters as water levels. The authors are concerned with the statistical foundation of present day safety analysis practices and can only hope that their own misjudgment will be dispelled. Until then, the authors suggest applying correct statistical methods in safety analysis even if it makes the analysis more expensive. It would be desirable to continue exploring the role of internal parameters (wall roughness factor, steam-water surface in thermal hydraulics codes, homogenization methods in neutronics codes) in system safety codes and to study their effects on the analysis. In the validation and verification process of a code one carries out a series of computations. The input data are not precisely determined because measured data have an error, calculated data are often obtained from a more or less accurate model. Some users of large codes are content with comparing the nominal output obtained from the nominal input, whereas all the possible inputs should be taken into account when judging safety. At the same time, any statement concerning safety must be aleatory, and its merit can be judged only when the probability is known with which the

  6. 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...

  7. 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

  8. Uncertainty analysis in Monte Carlo criticality computations

    International Nuclear Information System (INIS)

    Qi Ao

    2011-01-01

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

  9. CEC/USDOE workshop on uncertainty analysis

    International Nuclear Information System (INIS)

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

    1990-07-01

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

  10. Mathematica tools for uncertainty analysis

    International Nuclear Information System (INIS)

    Mikhailov, M D; Aibe, V Y

    2015-01-01

    Some functions of the author's Mathematica 9 package are presented. Links are given to the author's interactive demonstrations. Mathematica supports 143 types of statistical distribution covering not only metrology but almost all statistical areas. The Supplement 1 to the ISO GUM mentions that analytical methods are ideal ones, but assume they are applicable only in simple cases, and recommends Monte Carlo method. In contrast, this paper presents Mathematica functions that can explore a multitude of methods in order to find analytical, numerical, or statistical results using (usually) one million random variates

  11. Some reflections on uncertainty analysis and management

    International Nuclear Information System (INIS)

    Aven, Terje

    2010-01-01

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

  12. Estimating annual bole biomass production using uncertainty analysis

    Science.gov (United States)

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

    2007-01-01

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

  13. 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…

  14. Per Object statistical analysis

    DEFF Research Database (Denmark)

    2008-01-01

    of a specific class in turn, and uses as pair of PPO stages to derive the statistics and then assign them to the objects' Object Variables. It may be that this could all be done in some other, simply way, but several other ways that were tried did not succeed. The procedure ouptut has been tested against...

  15. Statistical Analysis and validation

    NARCIS (Netherlands)

    Hoefsloot, H.C.J.; Horvatovich, P.; Bischoff, R.

    2013-01-01

    In this chapter guidelines are given for the selection of a few biomarker candidates from a large number of compounds with a relative low number of samples. The main concepts concerning the statistical validation of the search for biomarkers are discussed. These complicated methods and concepts are

  16. Uncertainties Related to Extreme Event Statistics of Sewer System Surcharge and Overflow

    DEFF Research Database (Denmark)

    Schaarup-Jensen, Kjeld; Johansen, C.; Thorndahl, Søren Liedtke

    2005-01-01

    Today it is common practice - in the major part of Europe - to base design of sewer systems in urban areas on recommended minimum values of flooding frequencies related to either pipe top level, basement level in buildings or level of road surfaces. Thus storm water runoff in sewer systems is only...... proceeding in an acceptable manner, if flooding of these levels is having an average return period bigger than a predefined value. This practice is also often used in functional analysis of existing sewer systems. If a sewer system can fulfil recommended flooding frequencies or not, can only be verified...... by performing long term simulations - using a sewer flow simulation model - and draw up extreme event statistics from the model simulations. In this context it is important to realize that uncertainties related to the input parameters of rainfall runoff models will give rise to uncertainties related...

  17. A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

    Science.gov (United States)

    Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

    2015-01-01

    PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.

  18. Uncertainty analysis for secondary energy distributions

    International Nuclear Information System (INIS)

    Gerstl, S.A.W.

    1978-01-01

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

  19. Approach to uncertainty in risk analysis

    Energy Technology Data Exchange (ETDEWEB)

    Rish, W.R.

    1988-08-01

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

  20. Approach to uncertainty in risk analysis

    International Nuclear Information System (INIS)

    Rish, W.R.

    1988-08-01

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

  1. Uncertainties in thick-target PIXE analysis

    International Nuclear Information System (INIS)

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

    1983-01-01

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

  2. Statistical hot spot analysis of reactor cores

    International Nuclear Information System (INIS)

    Schaefer, H.

    1974-05-01

    This report is an introduction into statistical hot spot analysis. After the definition of the term 'hot spot' a statistical analysis is outlined. The mathematical method is presented, especially the formula concerning the probability of no hot spots in a reactor core is evaluated. A discussion with the boundary conditions of a statistical hot spot analysis is given (technological limits, nominal situation, uncertainties). The application of the hot spot analysis to the linear power of pellets and the temperature rise in cooling channels is demonstrated with respect to the test zone of KNK II. Basic values, such as probability of no hot spots, hot spot potential, expected hot spot diagram and cumulative distribution function of hot spots, are discussed. It is shown, that the risk of hot channels can be dispersed equally over all subassemblies by an adequate choice of the nominal temperature distribution in the core

  3. Image registration with uncertainty analysis

    Science.gov (United States)

    Simonson, Katherine M [Cedar Crest, NM

    2011-03-22

    In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

  4. Embracing Uncertainty: The Interface of Bayesian Statistics and Cognitive Psychology

    Directory of Open Access Journals (Sweden)

    Judith L. Anderson

    1998-06-01

    Full Text Available Ecologists working in conservation and resource management are discovering the importance of using Bayesian analytic methods to deal explicitly with uncertainty in data analyses and decision making. However, Bayesian procedures require, as inputs and outputs, an idea that is problematic for the human brain: the probability of a hypothesis ("single-event probability". I describe several cognitive concepts closely related to single-event probabilities, and discuss how their interchangeability in the human mind results in "cognitive illusions," apparent deficits in reasoning about uncertainty. Each cognitive illusion implies specific possible pitfalls for the use of single-event probabilities in ecology and resource management. I then discuss recent research in cognitive psychology showing that simple tactics of communication, suggested by an evolutionary perspective on human cognition, help people to process uncertain information more effectively as they read and talk about probabilities. In addition, I suggest that carefully considered standards for methodology and conventions for presentation may also make Bayesian analyses easier to understand.

  5. Statistical data analysis

    International Nuclear Information System (INIS)

    Hahn, A.A.

    1994-11-01

    The complexity of instrumentation sometimes requires data analysis to be done before the result is presented to the control room. This tutorial reviews some of the theoretical assumptions underlying the more popular forms of data analysis and presents simple examples to illuminate the advantages and hazards of different techniques

  6. Uncertainty Analysis of In leakage Test for Pressurized Control Room Envelop

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-10-15

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

  7. Uncertainty Analysis of In leakage Test for Pressurized Control Room Envelop

    International Nuclear Information System (INIS)

    Lee, J. B.

    2013-01-01

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

  8. Statistical characterization of roughness uncertainty and impact on wind resource estimation

    DEFF Research Database (Denmark)

    Kelly, Mark C.; Ejsing Jørgensen, Hans

    2017-01-01

    In this work we relate uncertainty in background roughness length (z0) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry...... between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.......-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty...

  9. Uncertainty Principles and Fourier Analysis

    Indian Academy of Sciences (India)

    analysis on the part of the reader. Those who are not fa- miliar with Fourier analysis are encouraged to look up Box. 1 along with [3]. (A) Heisenberg's inequality: Let us measure concentration in terms of standard deviation i.e. for a square integrable func-. 00 tion defined on 1R and normalized so that J If(x)12d,x = 1,. -00. 00.

  10. Uncertainty analysis for Ulysses safety evaluation report

    International Nuclear Information System (INIS)

    Frank, M.V.

    1991-01-01

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

  11. Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning

    International Nuclear Information System (INIS)

    Chetty, Indrin J.; Rosu, Mihaela; Kessler, Marc L.; Fraass, Benedick A.; Haken, Randall K. ten; Kong, Feng-Ming; McShan, Daniel L.

    2006-01-01

    Purpose: To investigate methods of reporting and analyzing statistical uncertainties in doses to targets and normal tissues in Monte Carlo (MC)-based treatment planning. Methods and Materials: Methods for quantifying statistical uncertainties in dose, such as uncertainty specification to specific dose points, or to volume-based regions, were analyzed in MC-based treatment planning for 5 lung cancer patients. The effect of statistical uncertainties on target and normal tissue dose indices was evaluated. The concept of uncertainty volume histograms for targets and organs at risk was examined, along with its utility, in conjunction with dose volume histograms, in assessing the acceptability of the statistical precision in dose distributions. The uncertainty evaluation tools were extended to four-dimensional planning for application on multiple instances of the patient geometry. All calculations were performed using the Dose Planning Method MC code. Results: For targets, generalized equivalent uniform doses and mean target doses converged at 150 million simulated histories, corresponding to relative uncertainties of less than 2% in the mean target doses. For the normal lung tissue (a volume-effect organ), mean lung dose and normal tissue complication probability converged at 150 million histories despite the large range in the relative organ uncertainty volume histograms. For 'serial' normal tissues such as the spinal cord, large fluctuations exist in point dose relative uncertainties. Conclusions: The tools presented here provide useful means for evaluating statistical precision in MC-based dose distributions. Tradeoffs between uncertainties in doses to targets, volume-effect organs, and 'serial' normal tissues must be considered carefully in determining acceptable levels of statistical precision in MC-computed dose distributions

  12. Statistical Analysis Plan

    DEFF Research Database (Denmark)

    Ris Hansen, Inge; Søgaard, Karen; Gram, Bibi

    2015-01-01

    This is the analysis plan for the multicentre randomised control study looking at the effect of training and exercises in chronic neck pain patients that is being conducted in Jutland and Funen, Denmark. This plan will be used as a work description for the analyses of the data collected....

  13. Uncertainty of quantitative microbiological methods of pharmaceutical analysis.

    Science.gov (United States)

    Gunar, O V; Sakhno, N G

    2015-12-30

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

  14. The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization

    KAUST Repository

    Prudencio, Ernesto E.; Schulz, Karl W.

    2012-01-01

    QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently

  15. Propagation of statistical and nuclear data uncertainties in Monte Carlo burn-up calculations

    International Nuclear Information System (INIS)

    Garcia-Herranz, Nuria; Cabellos, Oscar; Sanz, Javier; Juan, Jesus; Kuijper, Jim C.

    2008-01-01

    Two methodologies to propagate the uncertainties on the nuclide inventory in combined Monte Carlo-spectrum and burn-up calculations are presented, based on sensitivity/uncertainty and random sampling techniques (uncertainty Monte Carlo method). Both enable the assessment of the impact of uncertainties in the nuclear data as well as uncertainties due to the statistical nature of the Monte Carlo neutron transport calculation. The methodologies are implemented in our MCNP-ACAB system, which combines the neutron transport code MCNP-4C and the inventory code ACAB. A high burn-up benchmark problem is used to test the MCNP-ACAB performance in inventory predictions, with no uncertainties. A good agreement is found with the results of other participants. This benchmark problem is also used to assess the impact of nuclear data uncertainties and statistical flux errors in high burn-up applications. A detailed calculation is performed to evaluate the effect of cross-section uncertainties in the inventory prediction, taking into account the temporal evolution of the neutron flux level and spectrum. Very large uncertainties are found at the unusually high burn-up of this exercise (800 MWd/kgHM). To compare the impact of the statistical errors in the calculated flux with respect to the cross uncertainties, a simplified problem is considered, taking a constant neutron flux level and spectrum. It is shown that, provided that the flux statistical deviations in the Monte Carlo transport calculation do not exceed a given value, the effect of the flux errors in the calculated isotopic inventory are negligible (even at very high burn-up) compared to the effect of the large cross-section uncertainties available at present in the data files

  16. Propagation of statistical and nuclear data uncertainties in Monte Carlo burn-up calculations

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Herranz, Nuria [Departamento de Ingenieria Nuclear, Universidad Politecnica de Madrid, UPM (Spain)], E-mail: nuria@din.upm.es; Cabellos, Oscar [Departamento de Ingenieria Nuclear, Universidad Politecnica de Madrid, UPM (Spain); Sanz, Javier [Departamento de Ingenieria Energetica, Universidad Nacional de Educacion a Distancia, UNED (Spain); Juan, Jesus [Laboratorio de Estadistica, Universidad Politecnica de Madrid, UPM (Spain); Kuijper, Jim C. [NRG - Fuels, Actinides and Isotopes Group, Petten (Netherlands)

    2008-04-15

    Two methodologies to propagate the uncertainties on the nuclide inventory in combined Monte Carlo-spectrum and burn-up calculations are presented, based on sensitivity/uncertainty and random sampling techniques (uncertainty Monte Carlo method). Both enable the assessment of the impact of uncertainties in the nuclear data as well as uncertainties due to the statistical nature of the Monte Carlo neutron transport calculation. The methodologies are implemented in our MCNP-ACAB system, which combines the neutron transport code MCNP-4C and the inventory code ACAB. A high burn-up benchmark problem is used to test the MCNP-ACAB performance in inventory predictions, with no uncertainties. A good agreement is found with the results of other participants. This benchmark problem is also used to assess the impact of nuclear data uncertainties and statistical flux errors in high burn-up applications. A detailed calculation is performed to evaluate the effect of cross-section uncertainties in the inventory prediction, taking into account the temporal evolution of the neutron flux level and spectrum. Very large uncertainties are found at the unusually high burn-up of this exercise (800 MWd/kgHM). To compare the impact of the statistical errors in the calculated flux with respect to the cross uncertainties, a simplified problem is considered, taking a constant neutron flux level and spectrum. It is shown that, provided that the flux statistical deviations in the Monte Carlo transport calculation do not exceed a given value, the effect of the flux errors in the calculated isotopic inventory are negligible (even at very high burn-up) compared to the effect of the large cross-section uncertainties available at present in the data files.

  17. Uncertainty analysis of neutron transport calculation

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  18. Initial uncertainty impacts statistical learning in sound sequence processing.

    Science.gov (United States)

    Todd, Juanita; Provost, Alexander; Whitson, Lisa; Mullens, Daniel

    2016-11-01

    uncertainty) may trigger value-based learning modulations even in task-irrelevant incidental learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Research design and statistical analysis

    CERN Document Server

    Myers, Jerome L; Lorch Jr, Robert F

    2013-01-01

    Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.  The book's goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations.  Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions.  Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations

  20. Non-parametric order statistics method applied to uncertainty propagation in fuel rod calculations

    International Nuclear Information System (INIS)

    Arimescu, V.E.; Heins, L.

    2001-01-01

    Advances in modeling fuel rod behavior and accumulations of adequate experimental data have made possible the introduction of quantitative methods to estimate the uncertainty of predictions made with best-estimate fuel rod codes. The uncertainty range of the input variables is characterized by a truncated distribution which is typically a normal, lognormal, or uniform distribution. While the distribution for fabrication parameters is defined to cover the design or fabrication tolerances, the distribution of modeling parameters is inferred from the experimental database consisting of separate effects tests and global tests. The final step of the methodology uses a Monte Carlo type of random sampling of all relevant input variables and performs best-estimate code calculations to propagate these uncertainties in order to evaluate the uncertainty range of outputs of interest for design analysis, such as internal rod pressure and fuel centerline temperature. The statistical method underlying this Monte Carlo sampling is non-parametric order statistics, which is perfectly suited to evaluate quantiles of populations with unknown distribution. The application of this method is straightforward in the case of one single fuel rod, when a 95/95 statement is applicable: 'with a probability of 95% and confidence level of 95% the values of output of interest are below a certain value'. Therefore, the 0.95-quantile is estimated for the distribution of all possible values of one fuel rod with a statistical confidence of 95%. On the other hand, a more elaborate procedure is required if all the fuel rods in the core are being analyzed. In this case, the aim is to evaluate the following global statement: with 95% confidence level, the expected number of fuel rods which are not exceeding a certain value is all the fuel rods in the core except only a few fuel rods. In both cases, the thresholds determined by the analysis should be below the safety acceptable design limit. An indirect

  1. Application of status uncertainty analysis methods for AP1000 LBLOCA calculation

    International Nuclear Information System (INIS)

    Zhang Shunxiang; Liang Guoxing

    2012-01-01

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

  2. Uncertainty analysis for geologic disposal of radioactive waste

    International Nuclear Information System (INIS)

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

    1981-01-01

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

  3. Uncertainty Propagation in Hypersonic Vehicle Aerothermoelastic Analysis

    Science.gov (United States)

    Lamorte, Nicolas Etienne

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

  4. Uncertainties in elemental quantitative analysis by PIXE

    International Nuclear Information System (INIS)

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

    1979-01-01

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

  5. Application of extended statistical combination of uncertainties methodology for digital nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    In, Wang Ki; Uh, Keun Sun; Chul, Kim Heui [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1995-02-01

    A technically more direct statistical combinations of uncertainties methodology, extended SCU (XSCU), was applied to statistically combine the uncertainties associated with the DNBR alarm setpoint and the DNBR trip setpoint of digital nuclear power plants. The modified SCU (MSCU) methodology is currently used as the USNRC approved design methodology to perform the same function. In this report, the MSCU and XSCU methodologies were compared in terms of the total uncertainties and the net margins to the DNBR alarm and trip setpoints. The MSCU methodology resulted in the small total penalties due to a significantly negative bias which are quite large. However the XSCU methodology gave the virtually unbiased total uncertainties. The net margins to the DNBR alarm and trip setpoints by the MSCU methodology agree with those by the XSCU methodology within statistical variations. (Author) 12 refs., 17 figs., 5 tabs.

  6. Regularized Statistical Analysis of Anatomy

    DEFF Research Database (Denmark)

    Sjöstrand, Karl

    2007-01-01

    This thesis presents the application and development of regularized methods for the statistical analysis of anatomical structures. Focus is on structure-function relationships in the human brain, such as the connection between early onset of Alzheimer’s disease and shape changes of the corpus...... and mind. Statistics represents a quintessential part of such investigations as they are preluded by a clinical hypothesis that must be verified based on observed data. The massive amounts of image data produced in each examination pose an important and interesting statistical challenge...... efficient algorithms which make the analysis of large data sets feasible, and gives examples of applications....

  7. Uncertainty analysis of LBLOCA for Advanced Heavy Water Reactor

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  8. Systematic Analysis Of Ocean Colour Uncertainties

    Science.gov (United States)

    Lavender, Samantha

    2013-12-01

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

  9. Approach to uncertainty evaluation for safety analysis

    International Nuclear Information System (INIS)

    Ogura, Katsunori

    2005-01-01

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

  10. Erha Uncertainty Analysis: Planning for the future

    International Nuclear Information System (INIS)

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

    2002-01-01

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

  11. Statistical characterization of roughness uncertainty and impact on wind resource estimation

    Directory of Open Access Journals (Sweden)

    M. Kelly

    2017-04-01

    Full Text Available In this work we relate uncertainty in background roughness length (z0 to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We statistically consider roughness and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias as well as for the general case. For estimation of uncertainty in annual energy production (AEP, we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following our developments, we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and we also find that sites with larger background roughness incur relatively larger uncertainties.

  12. Uncertainty Prediction in Passive Target Motion Analysis

    Science.gov (United States)

    2016-05-12

    Number 15/152,696 Filing Date 12 May 2016 Inventor John G. Baylog et al Address any questions concerning this matter to the Office of...300118 1 of 25 UNCERTAINTY PREDICTION IN PASSIVE TARGET MOTION ANALYSIS STATEMENT OF GOVERNMENT INTEREST [0001] The invention described herein...at an unknown location and following an unknown course relative to an observer 12. Observer 12 has a sensor array such as a passive sonar or radar

  13. 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...

  14. Parameter Uncertainty for Repository Thermal Analysis

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-01

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

  15. 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

  16. 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

  17. Uncertainty analysis of nuclear waste package corrosion

    International Nuclear Information System (INIS)

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

    1986-01-01

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

  18. Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel

    Science.gov (United States)

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

    2016-01-01

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

  19. Statistical uncertainty of extreme wind storms over Europe derived from a probabilistic clustering technique

    Science.gov (United States)

    Walz, Michael; Leckebusch, Gregor C.

    2016-04-01

    Extratropical wind storms pose one of the most dangerous and loss intensive natural hazards for Europe. However, due to only 50 years of high quality observational data, it is difficult to assess the statistical uncertainty of these sparse events just based on observations. Over the last decade seasonal ensemble forecasts have become indispensable in quantifying the uncertainty of weather prediction on seasonal timescales. In this study seasonal forecasts are used in a climatological context: By making use of the up to 51 ensemble members, a broad and physically consistent statistical base can be created. This base can then be used to assess the statistical uncertainty of extreme wind storm occurrence more accurately. In order to determine the statistical uncertainty of storms with different paths of progression, a probabilistic clustering approach using regression mixture models is used to objectively assign storm tracks (either based on core pressure or on extreme wind speeds) to different clusters. The advantage of this technique is that the entire lifetime of a storm is considered for the clustering algorithm. Quadratic curves are found to describe the storm tracks most accurately. Three main clusters (diagonal, horizontal or vertical progression of the storm track) can be identified, each of which have their own particulate features. Basic storm features like average velocity and duration are calculated and compared for each cluster. The main benefit of this clustering technique, however, is to evaluate if the clusters show different degrees of uncertainty, e.g. more (less) spread for tracks approaching Europe horizontally (diagonally). This statistical uncertainty is compared for different seasonal forecast products.

  20. Bayesian Inference in Statistical Analysis

    CERN Document Server

    Box, George E P

    2011-01-01

    The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Rob

  1. Uncertainty analysis technique for OMEGA Dante measurementsa)

    Science.gov (United States)

    May, M. J.; Widmann, K.; Sorce, C.; Park, H.-S.; Schneider, M.

    2010-10-01

    The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.

  2. Uncertainty analysis technique for OMEGA Dante measurements

    International Nuclear Information System (INIS)

    May, M. J.; Widmann, K.; Sorce, C.; Park, H.-S.; Schneider, M.

    2010-01-01

    The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.

  3. Uncertainty Analysis Technique for OMEGA Dante Measurements

    International Nuclear Information System (INIS)

    May, M.J.; Widmann, K.; Sorce, C.; Park, H.; Schneider, M.

    2010-01-01

    The Dante is an 18 channel X-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g. hohlraums, etc.) at X-ray energies between 50 eV to 10 keV. It is a main diagnostics installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the X-ray diodes, filters and mirrors and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte-Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.

  4. Uncertainty and sensitivity analysis of the nuclear fuel thermal behavior

    Energy Technology Data Exchange (ETDEWEB)

    Boulore, A., E-mail: antoine.boulore@cea.fr [Commissariat a l' Energie Atomique (CEA), DEN, Fuel Research Department, 13108 Saint-Paul-lez-Durance (France); Struzik, C. [Commissariat a l' Energie Atomique (CEA), DEN, Fuel Research Department, 13108 Saint-Paul-lez-Durance (France); Gaudier, F. [Commissariat a l' Energie Atomique (CEA), DEN, Systems and Structure Modeling Department, 91191 Gif-sur-Yvette (France)

    2012-12-15

    Highlights: Black-Right-Pointing-Pointer A complete quantitative method for uncertainty propagation and sensitivity analysis is applied. Black-Right-Pointing-Pointer The thermal conductivity of UO{sub 2} is modeled as a random variable. Black-Right-Pointing-Pointer The first source of uncertainty is the linear heat rate. Black-Right-Pointing-Pointer The second source of uncertainty is the thermal conductivity of the fuel. - Abstract: In the global framework of nuclear fuel behavior simulation, the response of the models describing the physical phenomena occurring during the irradiation in reactor is mainly conditioned by the confidence in the calculated temperature of the fuel. Amongst all parameters influencing the temperature calculation in our fuel rod simulation code (METEOR V2), several sources of uncertainty have been identified as being the most sensitive: thermal conductivity of UO{sub 2}, radial distribution of power in the fuel pellet, local linear heat rate in the fuel rod, geometry of the pellet and thermal transfer in the gap. Expert judgment and inverse methods have been used to model the uncertainty of these parameters using theoretical distributions and correlation matrices. Propagation of these uncertainties in the METEOR V2 code using the URANIE framework and a Monte-Carlo technique has been performed in different experimental irradiations of UO{sub 2} fuel. At every time step of the simulated experiments, we get a temperature statistical distribution which results from the initial distributions of the uncertain parameters. We then can estimate confidence intervals of the calculated temperature. In order to quantify the sensitivity of the calculated temperature to each of the uncertain input parameters and data, we have also performed a sensitivity analysis using the Sobol' indices at first order.

  5. The EURACOS activation experiments: preliminary uncertainty analysis

    International Nuclear Information System (INIS)

    Yeivin, Y.

    1982-01-01

    A sequence of counting rates of an irradiated sulphur pellet, r(tsub(i)), measured at different times after the end of the irradiation, are fitted to r(t)=Aexp(-lambda t)+B. A standard adjustment procedure is applied to determine the parameters A and B, their standard deviations and correlation, and chi square. It is demonstrated that if the counting-rate uncertainties are entirely due to the counting statistics, the experimental data are totally inconsistent with the ''theoretical'' model. However, assuming an additional systematic error of approximalety 1%, and eliminating a few ''bad'' data, produces a data set quite consistent with the model. The dependence of chi square on the assumed systematic error and the data elimination procedure are discussed in great detail. A review of the adjustment procedure is appended to the report

  6. Uncertainty analysis on probabilistic fracture mechanics assessment methodology

    International Nuclear Information System (INIS)

    Rastogi, Rohit; Vinod, Gopika; Chandra, Vikas; Bhasin, Vivek; Babar, A.K.; Rao, V.V.S.S.; Vaze, K.K.; Kushwaha, H.S.; Venkat-Raj, V.

    1999-01-01

    Fracture Mechanics has found a profound usage in the area of design of components and assessing fitness for purpose/residual life estimation of an operating component. Since defect size and material properties are statistically distributed, various probabilistic approaches have been employed for the computation of fracture probability. Monte Carlo Simulation is one such procedure towards the analysis of fracture probability. This paper deals with uncertainty analysis using the Monte Carlo Simulation methods. These methods were developed based on the R6 failure assessment procedure, which has been widely used in analysing the integrity of structures. The application of this method is illustrated with a case study. (author)

  7. Bootstrap and Order Statistics for Quantifying Thermal-Hydraulic Code Uncertainties in the Estimation of Safety Margins

    Directory of Open Access Journals (Sweden)

    Enrico Zio

    2008-01-01

    Full Text Available In the present work, the uncertainties affecting the safety margins estimated from thermal-hydraulic code calculations are captured quantitatively by resorting to the order statistics and the bootstrap technique. The proposed framework of analysis is applied to the estimation of the safety margin, with its confidence interval, of the maximum fuel cladding temperature reached during a complete group distribution blockage scenario in a RBMK-1500 nuclear reactor.

  8. Statistical Information and Uncertainty: A Critique of Applications in Experimental Psychology

    Directory of Open Access Journals (Sweden)

    Donald Laming

    2010-04-01

    Full Text Available This paper presents, first, a formal exploration of the relationships between information (statistically defined, statistical hypothesis testing, the use of hypothesis testing in reverse as an investigative tool, channel capacity in a communication system, uncertainty, the concept of entropy in thermodynamics, and Bayes’ theorem. This exercise brings out the close mathematical interrelationships between different applications of these ideas in diverse areas of psychology. Subsequent illustrative examples are grouped under (a the human operator as an ideal communications channel, (b the human operator as a purely physical system, and (c Bayes’ theorem as an algorithm for combining information from different sources. Some tentative conclusions are drawn about the usefulness of information theory within these different categories. (a The idea of the human operator as an ideal communications channel has long been abandoned, though it provides some lessons that still need to be absorbed today. (b Treating the human operator as a purely physical system provides a platform for the quantitative exploration of many aspects of human performance by analogy with the analysis of other physical systems. (c The use of Bayes’ theorem to calculate the effects of prior probabilities and stimulus frequencies on human performance is probably misconceived, but it is difficult to obtain results precise enough to resolve this question.

  9. Hydrocoin level 3 - Testing methods for sensitivity/uncertainty analysis

    International Nuclear Information System (INIS)

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

    1991-01-01

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

  10. The statistical analysis of anisotropies

    International Nuclear Information System (INIS)

    Webster, A.

    1977-01-01

    One of the many uses to which a radio survey may be put is an analysis of the distribution of the radio sources on the celestial sphere to find out whether they are bunched into clusters or lie in preferred regions of space. There are many methods of testing for clustering in point processes and since they are not all equally good this contribution is presented as a brief guide to what seems to be the best of them. The radio sources certainly do not show very strong clusering and may well be entirely unclustered so if a statistical method is to be useful it must be both powerful and flexible. A statistic is powerful in this context if it can efficiently distinguish a weakly clustered distribution of sources from an unclustered one, and it is flexible if it can be applied in a way which avoids mistaking defects in the survey for true peculiarities in the distribution of sources. The paper divides clustering statistics into two classes: number density statistics and log N/log S statistics. (Auth.)

  11. 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.

  12. 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

    starting point we built Statistical Fracture Domains whose significance rely exclusively on fracturing statistics, not including explicitly the current Fracture Domains or closeness between one borehole section or the other. Theoretical developments are proposed in order to incorporate the orientation uncertainty and the fracturing variability into a resulting parent distribution density uncertainty. When applied to both sites, it comes that variability prevails in front of uncertainty, thus validating the good level of data accuracy. Moreover, this allows to define a possible range of variation around the mean values of densities. Finally a sorting algorithm is developed for providing, from the initial elementary bricks mentioned above, a division of a site into Statistical Fracture Domains whose internal variability is reduced. The systematic comparison is based on the division of the datasets according to several densities referring to a division of the orientations into 13 subsets (pole zones). The first application of the methodology shows that some main trends can be defined for the orientation/density distributions throughout the site, which are combined with a high level of overlapping. Moreover the final Statistical Fracture Domain definition differ from the Fracture Domains existing at the site. The SFD are an objective comparison of statistical fracturing properties. Several perspectives are proposed in order to bridge the gap between constraints brought by a relevant statistical modeling and modeling specificities of the SKB sites and more generally conditions inherent to geological models

  13. Statistical analysis of environmental data

    International Nuclear Information System (INIS)

    Beauchamp, J.J.; Bowman, K.O.; Miller, F.L. Jr.

    1975-10-01

    This report summarizes the analyses of data obtained by the Radiological Hygiene Branch of the Tennessee Valley Authority from samples taken around the Browns Ferry Nuclear Plant located in Northern Alabama. The data collection was begun in 1968 and a wide variety of types of samples have been gathered on a regular basis. The statistical analysis of environmental data involving very low-levels of radioactivity is discussed. Applications of computer calculations for data processing are described

  14. Treatment of uncertainties in the IPCC: a philosophical analysis

    Science.gov (United States)

    Jebeile, J.; Drouet, I.

    2014-12-01

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

  15. Uncertainty Assessment of Hydrological Frequency Analysis Using Bootstrap Method

    Directory of Open Access Journals (Sweden)

    Yi-Ming Hu

    2013-01-01

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

  16. Uncertainty analysis of a nondestructive radioassay system for transuranic waste

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  17. Assessment of uncertainty in full core reactor physics calculations using statistical methods

    International Nuclear Information System (INIS)

    McEwan, C.

    2012-01-01

    The best estimate method of safety analysis involves choosing a realistic set of input parameters for a proposed safety case and evaluating the uncertainty in the results. Determining the uncertainty in code outputs remains a challenge and is the subject of a benchmarking exercise proposed by the Organization for Economic Cooperation and Development. The work proposed in this paper will contribute to this benchmark by assessing the uncertainty in a depletion calculation of the final nuclide concentrations for an experiment performed in the Fukushima-2 reactor. This will be done using lattice transport code DRAGON and a tool known as DINOSAUR. (author)

  18. Assessment of uncertainty in full core reactor physics calculations using statistical methods

    Energy Technology Data Exchange (ETDEWEB)

    McEwan, C., E-mail: mcewac2@mcmaster.ca [McMaster Univ., Hamilton, Ontario (Canada)

    2012-07-01

    The best estimate method of safety analysis involves choosing a realistic set of input parameters for a proposed safety case and evaluating the uncertainty in the results. Determining the uncertainty in code outputs remains a challenge and is the subject of a benchmarking exercise proposed by the Organization for Economic Cooperation and Development. The work proposed in this paper will contribute to this benchmark by assessing the uncertainty in a depletion calculation of the final nuclide concentrations for an experiment performed in the Fukushima-2 reactor. This will be done using lattice transport code DRAGON and a tool known as DINOSAUR. (author)

  19. Uncertainty Analysis and Expert Judgment in Seismic Hazard Analysis

    Science.gov (United States)

    Klügel, Jens-Uwe

    2011-01-01

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

  20. 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

    and fracturing properties main characteristics. From that starting point we built Statistical Fracture Domains whose significance rely exclusively on fracturing statistics, not including explicitly the current Fracture Domains or closeness between one borehole section or the other. Theoretical developments are proposed in order to incorporate the orientation uncertainty and the fracturing variability into a resulting parent distribution density uncertainty. When applied to both sites, it comes that variability prevails in front of uncertainty, thus validating the good level of data accuracy. Moreover, this allows to define a possible range of variation around the mean values of densities. Finally a sorting algorithm is developed for providing, from the initial elementary bricks mentioned above, a division of a site into Statistical Fracture Domains whose internal variability is reduced.

  1. 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

  2. Risk uncertainty analysis methods for NUREG-1150

    International Nuclear Information System (INIS)

    Benjamin, A.S.; Boyd, G.J.

    1987-01-01

    Evaluation and display of risk uncertainties for NUREG-1150 constitute a principal focus of the Severe Accident Risk Rebaselining/Risk Reduction Program (SARRP). Some of the principal objectives of the uncertainty evaluation are: (1) to provide a quantitative estimate that reflects, for those areas considered, a credible and realistic range of uncertainty in risk; (2) to rank the various sources of uncertainty with respect to their importance for various measures of risk; and (3) to characterize the state of understanding of each aspect of the risk assessment for which major uncertainties exist. This paper describes the methods developed to fulfill these objectives

  3. Statistical analysis of JET disruptions

    International Nuclear Information System (INIS)

    Tanga, A.; Johnson, M.F.

    1991-07-01

    In the operation of JET and of any tokamak many discharges are terminated by a major disruption. The disruptive termination of a discharge is usually an unwanted event which may cause damage to the structure of the vessel. In a reactor disruptions are potentially a very serious problem, hence the importance of studying them and devising methods to avoid disruptions. Statistical information has been collected about the disruptions which have occurred at JET over a long span of operations. The analysis is focused on the operational aspects of the disruptions rather than on the underlining physics. (Author)

  4. A Proposal on the Advanced Sampling Based Sensitivity and Uncertainty Analysis Method for the Eigenvalue Uncertainty Analysis

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  5. Representation of analysis results involving aleatory and epistemic uncertainty.

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-08-01

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

  6. 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

  7. Statistical Methods for Estimating the Uncertainty in the Best Basis Inventories

    International Nuclear Information System (INIS)

    WILMARTH, S.R.

    2000-01-01

    This document describes the statistical methods used to determine sample-based uncertainty estimates for the Best Basis Inventory (BBI). For each waste phase, the equation for the inventory of an analyte in a tank is Inventory (Kg or Ci) = Concentration x Density x Waste Volume. the total inventory is the sum of the inventories in the different waste phases. Using tanks sample data: statistical methods are used to obtain estimates of the mean concentration of an analyte the density of the waste, and their standard deviations. The volumes of waste in the different phases, and their standard deviations, are estimated based on other types of data. The three estimates are multiplied to obtain the inventory estimate. The standard deviations are combined to obtain a standard deviation of the inventory. The uncertainty estimate for the Best Basis Inventory (BBI) is the approximate 95% confidence interval on the inventory

  8. Summary of the CEC/USDOE workshop on uncertainty analysis

    International Nuclear Information System (INIS)

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

    1990-06-01

    There is uncertainty in all aspects of assessing the consequences of accidental releases of radioactive material, from understanding and describing the environmental and biological transfer processes to modeling emergency response. The need for an exchange of views and a comparison of approaches between the diverse disciplines led to the organization of a CEC/USDOE Workshop on Uncertainty Analysis held in Santa Fe, New Mexico, in November 1989. The workshop brought together specialists in a number of disciplines, including those expert in the mathematics and statistics of uncertainty analysis, in expert judgment elicitation and evaluation, and in all aspects of assessing the radiological and environmental consequences of accidental releases of radioactive material. In addition, there was participation from users of the output of accident consequences assessment in decision making and/or regulatory frameworks. The main conclusions that emerged from the workshop are summarized in this paper. These are discussed in the context of three different types of accident consequence assessment: probabilistic assessments of accident consequences undertaken as inputs to risk analyses of nuclear installations, assessments of accident consequences in real time to provide inputs to decisions on the introduction of countermeasures, and the reconstruction of doses and risks resulting form past releases of radioactive material

  9. Statistical Analysis of Protein Ensembles

    Science.gov (United States)

    Máté, Gabriell; Heermann, Dieter

    2014-04-01

    As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

  10. 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).

  11. 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.

  12. Statistical data analysis using SAS intermediate statistical methods

    CERN Document Server

    Marasinghe, Mervyn G

    2018-01-01

    The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitab...

  13. Evaluating Statistical Process Control (SPC) techniques and computing the uncertainty of force calibrations

    Science.gov (United States)

    Navard, Sharon E.

    1989-01-01

    In recent years there has been a push within NASA to use statistical techniques to improve the quality of production. Two areas where statistics are used are in establishing product and process quality control of flight hardware and in evaluating the uncertainty of calibration of instruments. The Flight Systems Quality Engineering branch is responsible for developing and assuring the quality of all flight hardware; the statistical process control methods employed are reviewed and evaluated. The Measurement Standards and Calibration Laboratory performs the calibration of all instruments used on-site at JSC as well as those used by all off-site contractors. These calibrations must be performed in such a way as to be traceable to national standards maintained by the National Institute of Standards and Technology, and they must meet a four-to-one ratio of the instrument specifications to calibrating standard uncertainty. In some instances this ratio is not met, and in these cases it is desirable to compute the exact uncertainty of the calibration and determine ways of reducing it. A particular example where this problem is encountered is with a machine which does automatic calibrations of force. The process of force calibration using the United Force Machine is described in detail. The sources of error are identified and quantified when possible. Suggestions for improvement are made.

  14. Anatomy of the Higgs fits: A first guide to statistical treatments of the theoretical uncertainties

    Directory of Open Access Journals (Sweden)

    Sylvain Fichet

    2016-04-01

    Full Text Available The studies of the Higgs boson couplings based on the recent and upcoming LHC data open up a new window on physics beyond the Standard Model. In this paper, we propose a statistical guide to the consistent treatment of the theoretical uncertainties entering the Higgs rate fits. Both the Bayesian and frequentist approaches are systematically analysed in a unified formalism. We present analytical expressions for the marginal likelihoods, useful to implement simultaneously the experimental and theoretical uncertainties. We review the various origins of the theoretical errors (QCD, EFT, PDF, production mode contamination…. All these individual uncertainties are thoroughly combined with the help of moment-based considerations. The theoretical correlations among Higgs detection channels appear to affect the location and size of the best-fit regions in the space of Higgs couplings. We discuss the recurrent question of the shape of the prior distributions for the individual theoretical errors and find that a nearly Gaussian prior arises from the error combinations. We also develop the bias approach, which is an alternative to marginalisation providing more conservative results. The statistical framework to apply the bias principle is introduced and two realisations of the bias are proposed. Finally, depending on the statistical treatment, the Standard Model prediction for the Higgs signal strengths is found to lie within either the 68% or 95% confidence level region obtained from the latest analyses of the 7 and 8 TeV LHC datasets.

  15. Anatomy of the Higgs fits: A first guide to statistical treatments of the theoretical uncertainties

    Science.gov (United States)

    Fichet, Sylvain; Moreau, Grégory

    2016-04-01

    The studies of the Higgs boson couplings based on the recent and upcoming LHC data open up a new window on physics beyond the Standard Model. In this paper, we propose a statistical guide to the consistent treatment of the theoretical uncertainties entering the Higgs rate fits. Both the Bayesian and frequentist approaches are systematically analysed in a unified formalism. We present analytical expressions for the marginal likelihoods, useful to implement simultaneously the experimental and theoretical uncertainties. We review the various origins of the theoretical errors (QCD, EFT, PDF, production mode contamination…). All these individual uncertainties are thoroughly combined with the help of moment-based considerations. The theoretical correlations among Higgs detection channels appear to affect the location and size of the best-fit regions in the space of Higgs couplings. We discuss the recurrent question of the shape of the prior distributions for the individual theoretical errors and find that a nearly Gaussian prior arises from the error combinations. We also develop the bias approach, which is an alternative to marginalisation providing more conservative results. The statistical framework to apply the bias principle is introduced and two realisations of the bias are proposed. Finally, depending on the statistical treatment, the Standard Model prediction for the Higgs signal strengths is found to lie within either the 68% or 95% confidence level region obtained from the latest analyses of the 7 and 8 TeV LHC datasets.

  16. Imaging mass spectrometry statistical analysis.

    Science.gov (United States)

    Jones, Emrys A; Deininger, Sören-Oliver; Hogendoorn, Pancras C W; Deelder, André M; McDonnell, Liam A

    2012-08-30

    Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research. Copyright © 2012 Elsevier B.V. All rights reserved.

  17. CONFIDENCE LEVELS AND/VS. STATISTICAL HYPOTHESIS TESTING IN STATISTICAL ANALYSIS. CASE STUDY

    Directory of Open Access Journals (Sweden)

    ILEANA BRUDIU

    2009-05-01

    Full Text Available Estimated parameters with confidence intervals and testing statistical assumptions used in statistical analysis to obtain conclusions on research from a sample extracted from the population. Paper to the case study presented aims to highlight the importance of volume of sample taken in the study and how this reflects on the results obtained when using confidence intervals and testing for pregnant. If statistical testing hypotheses not only give an answer "yes" or "no" to some questions of statistical estimation using statistical confidence intervals provides more information than a test statistic, show high degree of uncertainty arising from small samples and findings build in the "marginally significant" or "almost significant (p very close to 0.05.

  18. Stochastic dynamic analysis of marine risers considering Gaussian system uncertainties

    Science.gov (United States)

    Ni, Pinghe; Li, Jun; Hao, Hong; Xia, Yong

    2018-03-01

    This paper performs the stochastic dynamic response analysis of marine risers with material uncertainties, i.e. in the mass density and elastic modulus, by using Stochastic Finite Element Method (SFEM) and model reduction technique. These uncertainties are assumed having Gaussian distributions. The random mass density and elastic modulus are represented by using the Karhunen-Loève (KL) expansion. The Polynomial Chaos (PC) expansion is adopted to represent the vibration response because the covariance of the output is unknown. Model reduction based on the Iterated Improved Reduced System (IIRS) technique is applied to eliminate the PC coefficients of the slave degrees of freedom to reduce the dimension of the stochastic system. Monte Carlo Simulation (MCS) is conducted to obtain the reference response statistics. Two numerical examples are studied in this paper. The response statistics from the proposed approach are compared with those from MCS. It is noted that the computational time is significantly reduced while the accuracy is kept. The results demonstrate the efficiency of the proposed approach for stochastic dynamic response analysis of marine risers.

  19. Code development for eigenvalue total sensitivity analysis and total uncertainty analysis

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  20. The Impact of Uncertainty on Investment. A Meta-Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Koetse, M.J. [Department of Spatial Economics, Vrije Universiteit Amsterdam (Netherlands); De Groot, Henri L.F. [Tinbergen Institute, Amsterdam (Netherlands); Florax, R.J.G.M. [Department of Agricultural Economics, Purdue University, West Lafayette (United States)

    2006-07-01

    In this paper we perform a meta-analysis on empirical estimates of the impact between investment and uncertainty. Since the outcomes of primary studies are largely incomparable with respect to the magnitude of the effect, our analysis focuses on the direction and statistical significance of the relationship. The standard approach in this situation is to estimate an ordered probit model on a categorical estimate, defined in terms of the direction of the effect. The estimates are transformed into marginal effects, in order to represent the changes in the probability of finding a negative significant, insignificant, and positive significant estimate. Although a meta-analysis generally does not allow for inferences on the correctness of model specifications in primary studies, our results give clear directions for model building in empirical investment research. For example, not including factor prices in investment models may seriously affect the model outcomes. Furthermore, we find that Q models produce more negative significant estimates than other models do, ceteris paribus. The outcome of a study is also affected by the type of data used in a primary study. Although it is clear that meta-analysis cannot always give decisive insights into the explanations for the variation in empirical outcomes, our meta-analysis shows that we can explain to a large extent why empirical estimates of the investment uncertainty relationship differ.

  1. 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.

  2. A methodology for uncertainty analysis of reference equations of state

    DEFF Research Database (Denmark)

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

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

  3. Parametric statistical change point analysis

    CERN Document Server

    Chen, Jie

    2000-01-01

    This work is an in-depth study of the change point problem from a general point of view and a further examination of change point analysis of the most commonly used statistical models Change point problems are encountered in such disciplines as economics, finance, medicine, psychology, signal processing, and geology, to mention only several The exposition is clear and systematic, with a great deal of introductory material included Different models are presented in each chapter, including gamma and exponential models, rarely examined thus far in the literature Other models covered in detail are the multivariate normal, univariate normal, regression, and discrete models Extensive examples throughout the text emphasize key concepts and different methodologies are used, namely the likelihood ratio criterion, and the Bayesian and information criterion approaches A comprehensive bibliography and two indices complete the study

  4. Measurement uncertainty analysis techniques applied to PV performance measurements

    International Nuclear Information System (INIS)

    Wells, C.

    1992-10-01

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

  5. Spatial GHG Inventory: Analysis of Uncertainty Sources. A Case Study for Ukraine

    International Nuclear Information System (INIS)

    Bun, R.; Gusti, M.; Kujii, L.; Tokar, O.; Tsybrivskyy, Y.; Bun, A.

    2007-01-01

    A geoinformation technology for creating spatially distributed greenhouse gas inventories based on a methodology provided by the Intergovernmental Panel on Climate Change and special software linking input data, inventory models, and a means for visualization are proposed. This technology opens up new possibilities for qualitative and quantitative spatially distributed presentations of inventory uncertainty at the regional level. Problems concerning uncertainty and verification of the distributed inventory are discussed. A Monte Carlo analysis of uncertainties in the energy sector at the regional level is performed, and a number of simulations concerning the effectiveness of uncertainty reduction in some regions are carried out. Uncertainties in activity data have a considerable influence on overall inventory uncertainty, for example, the inventory uncertainty in the energy sector declines from 3.2 to 2.0% when the uncertainty of energy-related statistical data on fuels combusted in the energy industries declines from 10 to 5%. Within the energy sector, the 'energy industries' subsector has the greatest impact on inventory uncertainty. The relative uncertainty in the energy sector inventory can be reduced from 2.19 to 1.47% if the uncertainty of specific statistical data on fuel consumption decreases from 10 to 5%. The 'energy industries' subsector has the greatest influence in the Donetsk oblast. Reducing the uncertainty of statistical data on electricity generation in just three regions - the Donetsk, Dnipropetrovsk, and Luhansk oblasts - from 7.5 to 4.0% results in a decline from 2.6 to 1.6% in the uncertainty in the national energy sector inventory

  6. Maximum-likelihood fitting of data dominated by Poisson statistical uncertainties

    International Nuclear Information System (INIS)

    Stoneking, M.R.; Den Hartog, D.J.

    1996-06-01

    The fitting of data by χ 2 -minimization is valid only when the uncertainties in the data are normally distributed. When analyzing spectroscopic or particle counting data at very low signal level (e.g., a Thomson scattering diagnostic), the uncertainties are distributed with a Poisson distribution. The authors have developed a maximum-likelihood method for fitting data that correctly treats the Poisson statistical character of the uncertainties. This method maximizes the total probability that the observed data are drawn from the assumed fit function using the Poisson probability function to determine the probability for each data point. The algorithm also returns uncertainty estimates for the fit parameters. They compare this method with a χ 2 -minimization routine applied to both simulated and real data. Differences in the returned fits are greater at low signal level (less than ∼20 counts per measurement). the maximum-likelihood method is found to be more accurate and robust, returning a narrower distribution of values for the fit parameters with fewer outliers

  7. Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods

    Science.gov (United States)

    Davis, A. D.

    2015-12-01

    The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity

  8. Uncertainty as Knowledge: Constraints on Policy Choices Provided by Analysis of Uncertainty

    Science.gov (United States)

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

    2012-12-01

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

  9. Uncertainties in Safety Analysis. A literature review

    International Nuclear Information System (INIS)

    Ekberg, C.

    1995-05-01

    The purpose of the presented work has been to give a short summary of the origins of many uncertainties arising in the designing and performance assessment of a repository for spent nuclear fuel. Some different methods to treat these uncertainties is also included. The methods and conclusions are in many cases general in the sense that they are applicable to many other disciplines where simulations are used. As a conclusion it may be noted that uncertainties of different origin have been discussed and debated, but one large group, e.g. computer simulations, where the methods to make a more explicit investigation exists, have not been investigated in a satisfying way. 50 refs

  10. Uncertainties in Safety Analysis. A literature review

    Energy Technology Data Exchange (ETDEWEB)

    Ekberg, C [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Nuclear Chemistry

    1995-05-01

    The purpose of the presented work has been to give a short summary of the origins of many uncertainties arising in the designing and performance assessment of a repository for spent nuclear fuel. Some different methods to treat these uncertainties is also included. The methods and conclusions are in many cases general in the sense that they are applicable to many other disciplines where simulations are used. As a conclusion it may be noted that uncertainties of different origin have been discussed and debated, but one large group, e.g. computer simulations, where the methods to make a more explicit investigation exists, have not been investigated in a satisfying way. 50 refs.

  11. Sensitivity functions for uncertainty analysis: Sensitivity and uncertainty analysis of reactor performance parameters

    International Nuclear Information System (INIS)

    Greenspan, E.

    1982-01-01

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

  12. Analysis of Uncertainty in Dynamic Processes Development of Banks Functioning

    Directory of Open Access Journals (Sweden)

    Aleksei V. Korovyakovskii

    2013-01-01

    Full Text Available The paper offers the approach to measure of uncertainty estimation in dynamic processes of banks functioning, using statistic data of different banking operations indicators. To calculate measure of uncertainty in dynamic processes of banks functioning the phase images of relevant sets of statistic data are considered. Besides, it is shown that the form of phase image of the studied sets of statistic data can act as a basis of measure of uncertainty estimation in dynamic processes of banks functioning. The set of analytical characteristics are offered to formalize the form of phase image definition of the studied sets of statistic data. It is shown that the offered analytical characteristics consider inequality of changes in values of the studied sets of statistic data, which is one of the ways of uncertainty display in dynamic processes development. The invariant estimates of measure of uncertainty in dynamic processes of banks functioning, considering significant changes in absolute values of the same indicators for different banks were obtained. The examples of calculation of measure of uncertainty in dynamic processes of concrete banks functioning were cited.

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

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  14. Health significance and statistical uncertainty. The value of P-value.

    Science.gov (United States)

    Consonni, Dario; Bertazzi, Pier Alberto

    2017-10-27

    The P-value is widely used as a summary statistics of scientific results. Unfortunately, there is a widespread tendency to dichotomize its value in "P0.05" ("statistically not significant"), with the former implying a "positive" result and the latter a "negative" one. To show the unsuitability of such an approach when evaluating the effects of environmental and occupational risk factors. We provide examples of distorted use of P-value and of the negative consequences for science and public health of such a black-and-white vision. The rigid interpretation of P-value as a dichotomy favors the confusion between health relevance and statistical significance, discourages thoughtful thinking, and distorts attention from what really matters, the health significance. A much better way to express and communicate scientific results involves reporting effect estimates (e.g., risks, risks ratios or risk differences) and their confidence intervals (CI), which summarize and convey both health significance and statistical uncertainty. Unfortunately, many researchers do not usually consider the whole interval of CI but only examine if it includes the null-value, therefore degrading this procedure to the same P-value dichotomy (statistical significance or not). In reporting statistical results of scientific research present effects estimates with their confidence intervals and do not qualify the P-value as "significant" or "not significant".

  15. Computing Statistics under Interval and Fuzzy Uncertainty Applications to Computer Science and Engineering

    CERN Document Server

    Nguyen, Hung T; Wu, Berlin; Xiang, Gang

    2012-01-01

    In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area.   Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 + 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy.   This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to in...

  16. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

    Directory of Open Access Journals (Sweden)

    Stephan Birle

    2016-01-01

    Full Text Available In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the

  17. Methodologies for uncertainty analysis in the level 2 PSA and their implementation procedures

    International Nuclear Information System (INIS)

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

    2002-04-01

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

  18. The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization

    KAUST Repository

    Prudencio, Ernesto E.

    2012-01-01

    QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently abstract in order to handle a large spectrum of models, (b) be algorithmically extensible, allowing an easy insertion of new and improved algorithms, and (c) take advantage of parallel computing, in order to handle realistic models. Such objectives demand a combination of an object-oriented design with robust software engineering practices. QUESO is written in C++, uses MPI, and leverages libraries already available to the scientific community. We describe some UQ concepts, present QUESO, and list planned enhancements.

  19. Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

    Science.gov (United States)

    Qi, D.; Majda, A.

    2017-12-01

    A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-04-15

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

  2. Uncertainty analysis of energy consumption in dwellings

    Energy Technology Data Exchange (ETDEWEB)

    Pettersen, Trine Dyrstad

    1997-12-31

    This thesis presents a comprehensive study of an energy estimation model that can be used to examine the uncertainty of predicted energy consumption in a dwelling. The variation and uncertainty of input parameters due to the outdoor climate, the building construction and the inhabitants are studied as a basis for further energy evaluations. The occurring variations of energy consumption in nominal similar dwellings are also investigated due to verification of the simulated energy consumption. The main topics are (1) a study of expected variations and uncertainties in both input parameters used in energy consumption calculations and the energy consumption in the dwelling, (2) the development and evaluation of a simplified energy calculation model that considers uncertainties due to the input parameters, (3) an evaluation of the influence of the uncertain parameters on the total variation so that the most important parameters can be identified, and (4) the recommendation of a simplified procedure for treating uncertainties or possible deviations from average conditions. 90 refs., 182 figs., 73 tabs.

  3. Survey of sampling-based methods for uncertainty and sensitivity analysis

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  4. Survey of sampling-based methods for uncertainty and sensitivity analysis.

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-06-01

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

  5. Statistical analysis and data management

    International Nuclear Information System (INIS)

    Anon.

    1981-01-01

    This report provides an overview of the history of the WIPP Biology Program. The recommendations of the American Institute of Biological Sciences (AIBS) for the WIPP biology program are summarized. The data sets available for statistical analyses and problems associated with these data sets are also summarized. Biological studies base maps are presented. A statistical model is presented to evaluate any correlation between climatological data and small mammal captures. No statistically significant relationship between variance in small mammal captures on Dr. Gennaro's 90m x 90m grid and precipitation records from the Duval Potash Mine were found

  6. Development of a System Analysis Toolkit for Sensitivity Analysis, Uncertainty Propagation, and Estimation of Parameter Distribution

    International Nuclear Information System (INIS)

    Heo, Jaeseok; Kim, Kyung Doo

    2015-01-01

    Statistical approaches to uncertainty quantification and sensitivity analysis are very important in estimating the safety margins for an engineering design application. This paper presents a system analysis and optimization toolkit developed by Korea Atomic Energy Research Institute (KAERI), which includes multiple packages of the sensitivity analysis and uncertainty quantification algorithms. In order to reduce the computing demand, multiple compute resources including multiprocessor computers and a network of workstations are simultaneously used. A Graphical User Interface (GUI) was also developed within the parallel computing framework for users to readily employ the toolkit for an engineering design and optimization problem. The goal of this work is to develop a GUI framework for engineering design and scientific analysis problems by implementing multiple packages of system analysis methods in the parallel computing toolkit. This was done by building an interface between an engineering simulation code and the system analysis software packages. The methods and strategies in the framework were designed to exploit parallel computing resources such as those found in a desktop multiprocessor workstation or a network of workstations. Available approaches in the framework include statistical and mathematical algorithms for use in science and engineering design problems. Currently the toolkit has 6 modules of the system analysis methodologies: deterministic and probabilistic approaches of data assimilation, uncertainty propagation, Chi-square linearity test, sensitivity analysis, and FFTBM

  7. Development of a System Analysis Toolkit for Sensitivity Analysis, Uncertainty Propagation, and Estimation of Parameter Distribution

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Jaeseok; Kim, Kyung Doo [KAERI, Daejeon (Korea, Republic of)

    2015-05-15

    Statistical approaches to uncertainty quantification and sensitivity analysis are very important in estimating the safety margins for an engineering design application. This paper presents a system analysis and optimization toolkit developed by Korea Atomic Energy Research Institute (KAERI), which includes multiple packages of the sensitivity analysis and uncertainty quantification algorithms. In order to reduce the computing demand, multiple compute resources including multiprocessor computers and a network of workstations are simultaneously used. A Graphical User Interface (GUI) was also developed within the parallel computing framework for users to readily employ the toolkit for an engineering design and optimization problem. The goal of this work is to develop a GUI framework for engineering design and scientific analysis problems by implementing multiple packages of system analysis methods in the parallel computing toolkit. This was done by building an interface between an engineering simulation code and the system analysis software packages. The methods and strategies in the framework were designed to exploit parallel computing resources such as those found in a desktop multiprocessor workstation or a network of workstations. Available approaches in the framework include statistical and mathematical algorithms for use in science and engineering design problems. Currently the toolkit has 6 modules of the system analysis methodologies: deterministic and probabilistic approaches of data assimilation, uncertainty propagation, Chi-square linearity test, sensitivity analysis, and FFTBM.

  8. Statistical analysis of management data

    CERN Document Server

    Gatignon, Hubert

    2013-01-01

    This book offers a comprehensive approach to multivariate statistical analyses. It provides theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications.

  9. 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)

  10. Qualitative uncertainty analysis in probabilistic safety assessment context

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  11. 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 ...

  12. 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

  13. Consideration of statistical uncertainties for the determination of representative values of the specific activity of wastes

    International Nuclear Information System (INIS)

    Barthel, R.

    2008-01-01

    The German Radiation Protection Commission has recommended 'Principles and Methods for the Consideration of Statistical Uncertainties for the Determination of Representative Values of the Specific Activity of NORM wastes' concerning the proof of compliance with supervision limits or dose standards according to paragraph 97 and paragraph 98 of the Radiation Protection Ordinance, respectively. The recommendation comprises a method ensuring the representativeness of estimates for the specific activity of NORM wastes, which also assures the required evidence for conformity with respect to supervision limits or dose standards, respectively. On the basis of a sampling survey, confidence limits for expectation values of specific activities are determined, which will be used to show that the supervision limit or the dose standard is met or exceeded with certainty, or that the performed sampling is not sufficient for the intended assessment. The sampling effort depends on the type and the width of the distribution of specific activities and is determined by the position of the confidence interval with respect to the supervision limit or of the resulting doses with respect to the dose standard. The statistical uncertainties that are described by confidence limits may be reduced by an optimised extension of the sample number, as far as necessary. (orig.)

  14. Durability reliability analysis for corroding concrete structures under uncertainty

    Science.gov (United States)

    Zhang, Hao

    2018-02-01

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

  15. Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

    Energy Technology Data Exchange (ETDEWEB)

    Alwan, Aravind; Aluru, N.R.

    2013-12-15

    This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems.

  16. Improved statistical models for limited datasets in uncertainty quantification using stochastic collocation

    International Nuclear Information System (INIS)

    Alwan, Aravind; Aluru, N.R.

    2013-01-01

    This paper presents a data-driven framework for performing uncertainty quantification (UQ) by choosing a stochastic model that accurately describes the sources of uncertainty in a system. This model is propagated through an appropriate response surface function that approximates the behavior of this system using stochastic collocation. Given a sample of data describing the uncertainty in the inputs, our goal is to estimate a probability density function (PDF) using the kernel moment matching (KMM) method so that this PDF can be used to accurately reproduce statistics like mean and variance of the response surface function. Instead of constraining the PDF to be optimal for a particular response function, we show that we can use the properties of stochastic collocation to make the estimated PDF optimal for a wide variety of response functions. We contrast this method with other traditional procedures that rely on the Maximum Likelihood approach, like kernel density estimation (KDE) and its adaptive modification (AKDE). We argue that this modified KMM method tries to preserve what is known from the given data and is the better approach when the available data is limited in quantity. We test the performance of these methods for both univariate and multivariate density estimation by sampling random datasets from known PDFs and then measuring the accuracy of the estimated PDFs, using the known PDF as a reference. Comparing the output mean and variance estimated with the empirical moments using the raw data sample as well as the actual moments using the known PDF, we show that the KMM method performs better than KDE and AKDE in predicting these moments with greater accuracy. This improvement in accuracy is also demonstrated for the case of UQ in electrostatic and electrothermomechanical microactuators. We show how our framework results in the accurate computation of statistics in micromechanical systems

  17. Uncertainty analysis of dosimetry spectrum unfolding

    International Nuclear Information System (INIS)

    Perey, F.G.

    1977-01-01

    The propagation of uncertainties in the input data is analyzed for the usual dosimetry unfolding solution. A new formulation of the dosimetry unfolding problem is proposed in which the most likely value of the spectrum is obtained. The relationship of this solution to the usual one is discussed

  18. 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.

  19. An introductory guide to uncertainty analysis in environmental and health risk assessment

    International Nuclear Information System (INIS)

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

    1992-10-01

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

  20. Statistical margin to DNB safety analysis approach for LOFT

    International Nuclear Information System (INIS)

    Atkinson, S.A.

    1982-01-01

    A method was developed and used for LOFT thermal safety analysis to estimate the statistical margin to DNB for the hot rod, and to base safety analysis on desired DNB probability limits. This method is an advanced approach using response surface analysis methods, a very efficient experimental design, and a 2nd-order response surface equation with a 2nd-order error propagation analysis to define the MDNBR probability density function. Calculations for limiting transients were used in the response surface analysis thereby including transient interactions and trip uncertainties in the MDNBR probability density

  1. 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)

  2. Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.

    Science.gov (United States)

    Sapsis, Themistoklis P; Majda, Andrew J

    2013-08-20

    A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.

  3. Measurement uncertainty analysis techniques applied to PV performance measurements

    Energy Technology Data Exchange (ETDEWEB)

    Wells, C.

    1992-10-01

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

  4. Measurement uncertainty analysis techniques applied to PV performance measurements

    Energy Technology Data Exchange (ETDEWEB)

    Wells, C

    1992-10-01

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

  5. Statistical uncertainties of nondestructive assay for spent nuclear fuel by using nuclear resonance fluorescence

    Energy Technology Data Exchange (ETDEWEB)

    Shizuma, Toshiyuki, E-mail: shizuma.toshiyuki@jaea.go.jp [Quantum Beam Science Directorate, Japan Atomic Energy Agency, Tokai, Ibaraki 319-1195 (Japan); Hayakawa, Takehito; Angell, Christopher T.; Hajima, Ryoichi [Quantum Beam Science Directorate, Japan Atomic Energy Agency, Tokai, Ibaraki 319-1195 (Japan); Minato, Futoshi; Suyama, Kenya [Nuclear Science and Engineering Directorate, Japan Atomic Energy Agency, Tokai, Ibaraki 319-1195 (Japan); Seya, Michio [Integrated Support Center for Nuclear Nonproliferation and Nuclear Security, Japan Atomic Energy Agency, Tokai, Ibaraki 319-1198 (Japan); Johnson, Micah S. [Lawrence Livermore National Laboratory, 7000 East Ave. Livermore, CA 94550 (United States); Department of Physics and Astronomy, San Jose State University, One Washington Square, San Jose, CA 9519 (United States); McNabb, Dennis P. [Lawrence Livermore National Laboratory, 7000 East Ave. Livermore, CA 94550 (United States)

    2014-02-11

    We estimated statistical uncertainties of a nondestructive assay system using nuclear resonance fluorescence (NRF) for spent nuclear fuel including low-concentrations of actinide nuclei with an intense, mono-energetic photon beam. Background counts from radioactive materials inside the spent fuel were calculated with the ORIGEN2.2-UPJ burn-up computer code. Coherent scattering contribution associated with Rayleigh, nuclear Thomson, and Delbrück scattering was also considered. The energy of the coherent scattering overlaps with that of NRF transitions to the ground state. Here, we propose to measure NRF transitions to the first excited state to avoid the coherent scattering contribution. Assuming that the total NRF cross-sections are in the range of 3–100 eV b at excitation energies of 2.25, 3.5, and 5 MeV, statistical uncertainties of the NRF measurement were estimated. We concluded that it is possible to assay 1% actinide content in the spent fuel with 2.2–3.2% statistical precision during 4000 s measurement time for the total integrated cross-section of 30 eV b at excitation energies of 3.5–5 MeV by using a photon beam with an intensity of 10{sup 6} photons/s/eV. We also examined both the experimental and theoretical NRF cross-sections for actinide nuclei. The calculation based on the quasi-particle random phase approximation suggests the existence of strong magnetic dipole resonances at excitation energies ranging from 2 to 6 MeV with the scattering cross-sections of tens eV b around 5 MeV in {sup 238}U.

  6. Fast computation of statistical uncertainty for spatiotemporal distributions estimated directly from dynamic cone beam SPECT projections

    International Nuclear Information System (INIS)

    Reutter, Bryan W.; Gullberg, Grant T.; Huesman, Ronald H.

    2001-01-01

    The estimation of time-activity curves and kinetic model parameters directly from projection data is potentially useful for clinical dynamic single photon emission computed tomography (SPECT) studies, particularly in those clinics that have only single-detector systems and thus are not able to perform rapid tomographic acquisitions. Because the radiopharmaceutical distribution changes while the SPECT gantry rotates, projections at different angles come from different tracer distributions. A dynamic image sequence reconstructed from the inconsistent projections acquired by a slowly rotating gantry can contain artifacts that lead to biases in kinetic parameters estimated from time-activity curves generated by overlaying regions of interest on the images. If cone beam collimators are used and the focal point of the collimators always remains in a particular transaxial plane, additional artifacts can arise in other planes reconstructed using insufficient projection samples [1]. If the projection samples truncate the patient's body, this can result in additional image artifacts. To overcome these sources of bias in conventional image based dynamic data analysis, we and others have been investigating the estimation of time-activity curves and kinetic model parameters directly from dynamic SPECT projection data by modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view [2-8]. In our previous work we developed a computationally efficient method for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data [5], which extended Formiconi's least squares algorithm for reconstructing temporally static distributions [9]. In addition, we studied the biases that result from modeling various orders temporal continuity and using various time samplings [5]. the present work, we address computational issues associated with evaluating the statistical uncertainty of

  7. Sensitivity and uncertainty analysis for fission product decay heat calculations

    International Nuclear Information System (INIS)

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

    1994-01-01

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

  8. 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...

  9. Uncertainty Analysis of Consequence Management (CM) Data Products.

    Energy Technology Data Exchange (ETDEWEB)

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

    2018-01-01

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

  10. Decisions under uncertainty using Bayesian analysis

    Directory of Open Access Journals (Sweden)

    Stelian STANCU

    2006-01-01

    Full Text Available The present paper makes a short presentation of the Bayesian decions method, where extrainformation brings a great support to decision making process, but also attract new costs. In this situation, getting new information, generally experimentaly based, contributes to diminushing the uncertainty degree that influences decision making process. As a conclusion, in a large number of decision problems, there is the possibility that the decision makers will renew some decisions already taken because of the facilities offered by obtainig extrainformation.

  11. Uncertainty Analysis of Light Water Reactor Fuel Lattices

    Directory of Open Access Journals (Sweden)

    C. Arenas

    2013-01-01

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

  12. Uncertainty Estimation of Neutron Activation Analysis in Zinc Elemental Determination in Food Samples

    International Nuclear Information System (INIS)

    Endah Damastuti; Muhayatun; Diah Dwiana L

    2009-01-01

    Beside to complished the requirements of international standard of ISO/IEC 17025:2005, uncertainty estimation should be done to increase quality and confidence of analysis results and also to establish traceability of the analysis results to SI unit. Neutron activation analysis is a major technique used by Radiometry technique analysis laboratory and is included as scope of accreditation under ISO/IEC 17025:2005, therefore uncertainty estimation of neutron activation analysis is needed to be carried out. Sample and standard preparation as well as, irradiation and measurement using gamma spectrometry were the main activities which could give contribution to uncertainty. The components of uncertainty sources were specifically explained. The result of expanded uncertainty was 4,0 mg/kg with level of confidence 95% (coverage factor=2) and Zn concentration was 25,1 mg/kg. Counting statistic of cuplikan and standard were the major contribution of combined uncertainty. The uncertainty estimation was expected to increase the quality of the analysis results and could be applied further to other kind of samples. (author)

  13. Best-estimate analysis and decision making under uncertainty

    International Nuclear Information System (INIS)

    Orechwa, Y.

    2004-01-01

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

  14. Automated uncertainty analysis methods in the FRAP computer codes

    International Nuclear Information System (INIS)

    Peck, S.O.

    1980-01-01

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

  15. Uncertainty Analysis with Considering Resonance Self-shielding Effect

    International Nuclear Information System (INIS)

    Han, Tae Young

    2016-01-01

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

  16. Uncertainty Analysis with Considering Resonance Self-shielding Effect

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-10-15

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

  17. One Approach to the Fire PSA Uncertainty Analysis

    International Nuclear Information System (INIS)

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

    2002-01-01

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

  18. Uncertainty analysis of constant amplitude fatigue test data employing the six parameters random fatigue limit model

    Directory of Open Access Journals (Sweden)

    Leonetti Davide

    2018-01-01

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

  19. Probabilistic assessment of fatigue life including statistical uncertainties in the S-N curve

    International Nuclear Information System (INIS)

    Sudret, B.; Hornet, P.; Stephan, J.-M.; Guede, Z.; Lemaire, M.

    2003-01-01

    A probabilistic framework is set up to assess the fatigue life of components of nuclear power plants. It intends to incorporate all kinds of uncertainties such as those appearing in the specimen fatigue life, design sub-factor, mechanical model and applied loading. This paper details the first step, which corresponds to the statistical treatment of the fatigue specimen test data. The specimen fatigue life at stress amplitude S is represented by a lognormal random variable whose mean and standard deviation depend on S. This characterization is then used to compute the random fatigue life of a component submitted to a single kind of cycles. Precisely the mean and coefficient of variation of this quantity are studied, as well as the reliability associated with the (deterministic) design value. (author)

  20. A Statistical Analysis of Cryptocurrencies

    Directory of Open Access Journals (Sweden)

    Stephen Chan

    2017-05-01

    Full Text Available We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization, of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.

  1. Uncertainty about probability: a decision analysis perspective

    International Nuclear Information System (INIS)

    Howard, R.A.

    1988-01-01

    The issue of how to think about uncertainty about probability is framed and analyzed from the viewpoint of a decision analyst. The failure of nuclear power plants is used as an example. The key idea is to think of probability as describing a state of information on an uncertain event, and to pose the issue of uncertainty in this quantity as uncertainty about a number that would be definitive: it has the property that you would assign it as the probability if you knew it. Logical consistency requires that the probability to assign to a single occurrence in the absence of further information be the mean of the distribution of this definitive number, not the medium as is sometimes suggested. Any decision that must be made without the benefit of further information must also be made using the mean of the definitive number's distribution. With this formulation, they find further that the probability of r occurrences in n exchangeable trials will depend on the first n moments of the definitive number's distribution. In making decisions, the expected value of clairvoyance on the occurrence of the event must be at least as great as that on the definitive number. If one of the events in question occurs, then the increase in probability of another such event is readily computed. This means, in terms of coin tossing, that unless one is absolutely sure of the fairness of a coin, seeing a head must increase the probability of heads, in distinction to usual thought. A numerical example for nuclear power shows that the failure of one plant of a group with a low probability of failure can significantly increase the probability that must be assigned to failure of a second plant in the group

  2. Uncertainty analysis in the applications of nuclear probabilistic risk assessment

    International Nuclear Information System (INIS)

    Le Duy, T.D.

    2011-01-01

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

  3. Uncertainty analysis of NDA waste measurements using computer simulations

    International Nuclear Information System (INIS)

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

    2000-01-01

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

  4. Assessment and uncertainty analysis of groundwater risk.

    Science.gov (United States)

    Li, Fawen; Zhu, Jingzhao; Deng, Xiyuan; Zhao, Yong; Li, Shaofei

    2018-01-01

    Groundwater with relatively stable quantity and quality is commonly used by human being. However, as the over-mining of groundwater, problems such as groundwater funnel, land subsidence and salt water intrusion have emerged. In order to avoid further deterioration of hydrogeological problems in over-mining regions, it is necessary to conduct the assessment of groundwater risk. In this paper, risks of shallow and deep groundwater in the water intake area of the South-to-North Water Transfer Project in Tianjin, China, were evaluated. Firstly, two sets of four-level evaluation index system were constructed based on the different characteristics of shallow and deep groundwater. Secondly, based on the normalized factor values and the synthetic weights, the risk values of shallow and deep groundwater were calculated. Lastly, the uncertainty of groundwater risk assessment was analyzed by indicator kriging method. The results meet the decision maker's demand for risk information, and overcome previous risk assessment results expressed in the form of deterministic point estimations, which ignore the uncertainty of risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Uncertainty and sensitivity analysis in nuclear accident consequence assessment

    International Nuclear Information System (INIS)

    Karlberg, Olof.

    1989-01-01

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

  6. Uncertainty analysis of geothermal energy economics

    Science.gov (United States)

    Sener, Adil Caner

    This dissertation research endeavors to explore geothermal energy economics by assessing and quantifying the uncertainties associated with the nature of geothermal energy and energy investments overall. The study introduces a stochastic geothermal cost model and a valuation approach for different geothermal power plant development scenarios. The Monte Carlo simulation technique is employed to obtain probability distributions of geothermal energy development costs and project net present values. In the study a stochastic cost model with incorporated dependence structure is defined and compared with the model where random variables are modeled as independent inputs. One of the goals of the study is to attempt to shed light on the long-standing modeling problem of dependence modeling between random input variables. The dependence between random input variables will be modeled by employing the method of copulas. The study focuses on four main types of geothermal power generation technologies and introduces a stochastic levelized cost model for each technology. Moreover, we also compare the levelized costs of natural gas combined cycle and coal-fired power plants with geothermal power plants. The input data used in the model relies on the cost data recently reported by government agencies and non-profit organizations, such as the Department of Energy, National Laboratories, California Energy Commission and Geothermal Energy Association. The second part of the study introduces the stochastic discounted cash flow valuation model for the geothermal technologies analyzed in the first phase. In this phase of the study, the Integrated Planning Model (IPM) software was used to forecast the revenue streams of geothermal assets under different price and regulation scenarios. These results are then combined to create a stochastic revenue forecast of the power plants. The uncertainties in gas prices and environmental regulations will be modeled and their potential impacts will be

  7. Statistical Power in Meta-Analysis

    Science.gov (United States)

    Liu, Jin

    2015-01-01

    Statistical power is important in a meta-analysis study, although few studies have examined the performance of simulated power in meta-analysis. The purpose of this study is to inform researchers about statistical power estimation on two sample mean difference test under different situations: (1) the discrepancy between the analytical power and…

  8. Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions

    Science.gov (United States)

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

    2011-01-01

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

  9. Uncertainty Analysis via Failure Domain Characterization: Polynomial Requirement Functions

    Science.gov (United States)

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

    2011-01-01

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

  10. Statistical methods for astronomical data analysis

    CERN Document Server

    Chattopadhyay, Asis Kumar

    2014-01-01

    This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for ...

  11. 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

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

    International Nuclear Information System (INIS)

    Garcia J, T.; Cardenas V, J.

    2015-09-01

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

  13. Nuclear data sensitivity/uncertainty analysis for XT-ADS

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  14. Preliminary Uncertainty Analysis for SMART Digital Core Protection and Monitoring System

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  16. Statistical analysis with Excel for dummies

    CERN Document Server

    Schmuller, Joseph

    2013-01-01

    Take the mystery out of statistical terms and put Excel to work! If you need to create and interpret statistics in business or classroom settings, this easy-to-use guide is just what you need. It shows you how to use Excel's powerful tools for statistical analysis, even if you've never taken a course in statistics. Learn the meaning of terms like mean and median, margin of error, standard deviation, and permutations, and discover how to interpret the statistics of everyday life. You'll learn to use Excel formulas, charts, PivotTables, and other tools to make sense of everything fro

  17. A statistical kinematic source inversion approach based on the QUESO library for uncertainty quantification and prediction

    Science.gov (United States)

    Zielke, Olaf; McDougall, Damon; Mai, Martin; Babuska, Ivo

    2014-05-01

    Seismic, often augmented with geodetic data, are frequently used to invert for the spatio-temporal evolution of slip along a rupture plane. The resulting images of the slip evolution for a single event, inferred by different research teams, often vary distinctly, depending on the adopted inversion approach and rupture model parameterization. This observation raises the question, which of the provided kinematic source inversion solutions is most reliable and most robust, and — more generally — how accurate are fault parameterization and solution predictions? These issues are not included in "standard" source inversion approaches. Here, we present a statistical inversion approach to constrain kinematic rupture parameters from teleseismic body waves. The approach is based a) on a forward-modeling scheme that computes synthetic (body-)waves for a given kinematic rupture model, and b) on the QUESO (Quantification of Uncertainty for Estimation, Simulation, and Optimization) library that uses MCMC algorithms and Bayes theorem for sample selection. We present Bayesian inversions for rupture parameters in synthetic earthquakes (i.e. for which the exact rupture history is known) in an attempt to identify the cross-over at which further model discretization (spatial and temporal resolution of the parameter space) is no longer attributed to a decreasing misfit. Identification of this cross-over is of importance as it reveals the resolution power of the studied data set (i.e. teleseismic body waves), enabling one to constrain kinematic earthquake rupture histories of real earthquakes at a resolution that is supported by data. In addition, the Bayesian approach allows for mapping complete posterior probability density functions of the desired kinematic source parameters, thus enabling us to rigorously assess the uncertainties in earthquake source inversions.

  18. Complex Visual Data Analysis, Uncertainty, and Representation

    National Research Council Canada - National Science Library

    Schunn, Christian D; Saner, Lelyn D; Kirschenbaum, Susan K; Trafton, J. G; Littleton, Eliza B

    2007-01-01

    ... (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities...

  19. 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

  20. New challenges on uncertainty propagation assessment of flood risk analysis

    Science.gov (United States)

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

    2016-04-01

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

  1. Application of uncertainty analysis in conceptual fusion reactor design

    International Nuclear Information System (INIS)

    Wu, T.; Maynard, C.W.

    1979-01-01

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

  2. Sensitivity and uncertainty analysis of NET/ITER shielding blankets

    International Nuclear Information System (INIS)

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

    1990-09-01

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

  3. Improved Monte Carlo Method for PSA Uncertainty Analysis

    International Nuclear Information System (INIS)

    Choi, Jongsoo

    2016-01-01

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

  4. Improved Monte Carlo Method for PSA Uncertainty Analysis

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-10-15

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

  5. Collecting operational event data for statistical analysis

    International Nuclear Information System (INIS)

    Atwood, C.L.

    1994-09-01

    This report gives guidance for collecting operational data to be used for statistical analysis, especially analysis of event counts. It discusses how to define the purpose of the study, the unit (system, component, etc.) to be studied, events to be counted, and demand or exposure time. Examples are given of classification systems for events in the data sources. A checklist summarizes the essential steps in data collection for statistical analysis

  6. 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.

  7. Uncertainty Instability Risk Analysis of High Concrete Arch Dam Abutments

    Directory of Open Access Journals (Sweden)

    Xin Cao

    2017-01-01

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

  8. Analysis of parameter uncertainties in the assessment of seismic risk for nuclear power plants

    International Nuclear Information System (INIS)

    Yucemen, S.M.

    1981-04-01

    Probabilistic and statistical methods are used to develop a procedure by which the seismic risk at a specific site can be systematically analyzed. The proposed probabilistic procedure provides a consisted method for the modelling, analysis and updating of uncertainties that are involved in the seismic risk analysis for nuclear power plants. Methods are proposed for including these uncertainties in the final value of calculated risks. Two specific case studies are presented in detail to illustrate the application of the probabilistic method of seismic risk evaluation and to investigate the sensitivity of results to different assumptions

  9. 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

  10. Explorations in Statistics: The Analysis of Change

    Science.gov (United States)

    Curran-Everett, Douglas; Williams, Calvin L.

    2015-01-01

    Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…

  11. Simulating fuel behavior under transient conditions using FRAPTRAN and uncertainty analysis using Dakota

    International Nuclear Information System (INIS)

    Gomes, Daniel S.; Teixeira, Antonio S.

    2017-01-01

    Although regulatory agencies have shown a special interest in incorporating best estimate approaches in the fuel licensing process, fuel codes are currently licensed based on only the deterministic limits such as those seen in 10CRF50, and therefore, may yield unrealistic safety margins. The concept of uncertainty analysis is employed to more realistically manage this risk. In this study, uncertainties were classified into two categories: probabilistic and epistemic (owing to a lack of pre-existing knowledge in this area). Fuel rods have three sources of uncertainty: manufacturing tolerance, boundary conditions, and physical models. The first step in successfully analyzing the uncertainties involves performing a statistical analysis on the input parameters used throughout the fuel code. The response obtained from this analysis must show proportional index correlations because the uncertainties are globally propagated. The Dakota toolkit was used to analyze the FRAPTRAN transient fuel code. The subsequent sensitivity analyses helped in identifying the key parameters with the highest correlation indices including the peak cladding temperature and the time required for cladding failures. The uncertainty analysis was performed using an IFA-650-5 fuel rod, which was in line with the tests performed in the Halden Project in Norway. The main objectives of the Halden project included studying the ballooning and rupture processes. The results of this experiment demonstrate the accuracy and applicability of the physical models in evaluating the thermal conductivity, mechanical model, and fuel swelling formulations. (author)

  12. Simulating fuel behavior under transient conditions using FRAPTRAN and uncertainty analysis using Dakota

    Energy Technology Data Exchange (ETDEWEB)

    Gomes, Daniel S.; Teixeira, Antonio S., E-mail: dsgomes@ipen.br, E-mail: teixeira@ipen [Instituto de Pesquisas Energéticas e Nucleares (IPEN/CNEN-SP), São Paulo, SP (Brazil)

    2017-07-01

    Although regulatory agencies have shown a special interest in incorporating best estimate approaches in the fuel licensing process, fuel codes are currently licensed based on only the deterministic limits such as those seen in 10CRF50, and therefore, may yield unrealistic safety margins. The concept of uncertainty analysis is employed to more realistically manage this risk. In this study, uncertainties were classified into two categories: probabilistic and epistemic (owing to a lack of pre-existing knowledge in this area). Fuel rods have three sources of uncertainty: manufacturing tolerance, boundary conditions, and physical models. The first step in successfully analyzing the uncertainties involves performing a statistical analysis on the input parameters used throughout the fuel code. The response obtained from this analysis must show proportional index correlations because the uncertainties are globally propagated. The Dakota toolkit was used to analyze the FRAPTRAN transient fuel code. The subsequent sensitivity analyses helped in identifying the key parameters with the highest correlation indices including the peak cladding temperature and the time required for cladding failures. The uncertainty analysis was performed using an IFA-650-5 fuel rod, which was in line with the tests performed in the Halden Project in Norway. The main objectives of the Halden project included studying the ballooning and rupture processes. The results of this experiment demonstrate the accuracy and applicability of the physical models in evaluating the thermal conductivity, mechanical model, and fuel swelling formulations. (author)

  13. PCT Uncertainty Analysis Using Unscented Transform with Random Orthogonal Matrix

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-05-15

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

  14. Dealing with phenomenological uncertainty in risk analysis

    International Nuclear Information System (INIS)

    Theofanous, T.G.

    1994-01-01

    The Risk-Oriented Accident Analysis Methodology (ROAAM) is summarized and developed further towards a formal definition. The key ideas behind the methodology and these more formal aspects are also presented and discussed

  15. Coupling of system thermal–hydraulics and Monte-Carlo code: Convergence criteria and quantification of correlation between statistical uncertainty and coupled error

    International Nuclear Information System (INIS)

    Wu, Xu; Kozlowski, Tomasz

    2015-01-01

    Highlights: • Coupling of Monte Carlo code Serpent and thermal–hydraulics code RELAP5. • A convergence criterion is developed based on the statistical uncertainty of power. • Correlation between MC statistical uncertainty and coupled error is quantified. • Both UO 2 and MOX single assembly models are used in the coupled simulation. • Validation of coupling results with a multi-group transport code DeCART. - Abstract: Coupled multi-physics approach plays an important role in improving computational accuracy. Compared with deterministic neutronics codes, Monte Carlo codes have the advantage of a higher resolution level. In the present paper, a three-dimensional continuous-energy Monte Carlo reactor physics burnup calculation code, Serpent, is coupled with a thermal–hydraulics safety analysis code, RELAP5. The coupled Serpent/RELAP5 code capability is demonstrated by the improved axial power distribution of UO 2 and MOX single assembly models, based on the OECD-NEA/NRC PWR MOX/UO 2 Core Transient Benchmark. Comparisons of calculation results using the coupled code with those from the deterministic methods, specifically heterogeneous multi-group transport code DeCART, show that the coupling produces more precise results. A new convergence criterion for the coupled simulation is developed based on the statistical uncertainty in power distribution in the Monte Carlo code, rather than ad-hoc criteria used in previous research. The new convergence criterion is shown to be more rigorous, equally convenient to use but requiring a few more coupling steps to converge. Finally, the influence of Monte Carlo statistical uncertainty on the coupled error of power and thermal–hydraulics parameters is quantified. The results are presented such that they can be used to find the statistical uncertainty to use in Monte Carlo in order to achieve a desired precision in coupled simulation

  16. Uncertainty analysis for the BEACON-COLSS core monitoring system application

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  17. The role of sensitivity analysis in assessing uncertainty

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  18. Uncertainty in projected point precipitation extremes for hydrological impact analysis of climate change

    Science.gov (United States)

    Van Uytven, Els; Willems, Patrick

    2017-04-01

    Current trends in the hydro-meteorological variables indicate the potential impact of climate change on hydrological extremes. Therefore, they trigger an increased importance climate adaptation strategies in water management. The impact of climate change on hydro-meteorological and hydrological extremes is, however, highly uncertain. This is due to uncertainties introduced by the climate models, the internal variability inherent to the climate system, the greenhouse gas scenarios and the statistical downscaling methods. In view of the need to define sustainable climate adaptation strategies, there is a need to assess these uncertainties. This is commonly done by means of ensemble approaches. Because more and more climate models and statistical downscaling methods become available, there is a need to facilitate the climate impact and uncertainty analysis. A Climate Perturbation Tool has been developed for that purpose, which combines a set of statistical downscaling methods including weather typing, weather generator, transfer function and advanced perturbation based approaches. By use of an interactive interface, climate impact modelers can apply these statistical downscaling methods in a semi-automatic way to an ensemble of climate model runs. The tool is applicable to any region, but has been demonstrated so far to cases in Belgium, Suriname, Vietnam and Bangladesh. Time series representing future local-scale precipitation, temperature and potential evapotranspiration (PET) conditions were obtained, starting from time series of historical observations. Uncertainties on the future meteorological conditions are represented in two different ways: through an ensemble of time series, and a reduced set of synthetic scenarios. The both aim to span the full uncertainty range as assessed from the ensemble of climate model runs and downscaling methods. For Belgium, for instance, use was made of 100-year time series of 10-minutes precipitation observations and daily

  19. Statistical shape analysis with applications in R

    CERN Document Server

    Dryden, Ian L

    2016-01-01

    A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while reta...

  20. Spatial analysis statistics, visualization, and computational methods

    CERN Document Server

    Oyana, Tonny J

    2015-01-01

    An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies. The book enables readers to: Identify types and characterize non-spatial and spatial data Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results Construct testable hypotheses that require inferential statistical analysis Process spatial data, extract explanatory variables, conduct statisti...

  1. Uncertainty analysis in the task of individual monitoring data

    International Nuclear Information System (INIS)

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

    2003-01-01

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

  2. Analysis and Reduction of Complex Networks Under Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Knio, Omar M

    2014-04-09

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

  3. Uncertainty analysis of power monitoring transit time ultrasonic flow meters

    International Nuclear Information System (INIS)

    Orosz, A.; Miller, D. W.; Christensen, R. N.; Arndt, S.

    2006-01-01

    A general uncertainty analysis is applied to chordal, transit time ultrasonic flow meters that are used in nuclear power plant feedwater loops. This investigation focuses on relationships between the major parameters of the flow measurement. For this study, mass flow rate is divided into three components, profile factor, density, and a form of volumetric flow rate. All system parameters are used to calculate values for these three components. Uncertainty is analyzed using a perturbation method. Sensitivity coefficients for major system parameters are shown, and these coefficients are applicable to a range of ultrasonic flow meters used in similar applications. Also shown is the uncertainty to be expected for density along with its relationship to other system uncertainties. One other conclusion is that pipe diameter sensitivity coefficients may be a function of the calibration technique used. (authors)

  4. 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.

  5. Nordic reference study on uncertainty and sensitivity analysis

    International Nuclear Information System (INIS)

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

    1989-01-01

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

  6. 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.…

  7. Advances in statistical models for data analysis

    CERN Document Server

    Minerva, Tommaso; Vichi, Maurizio

    2015-01-01

    This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

  8. Design optimization and uncertainty analysis of SMA morphing structures

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  9. Uncertainty analysis of light water reactor unit fuel pin cells

    Energy Technology Data Exchange (ETDEWEB)

    Kamerow, S.; Ivanov, K., E-mail: sln107@PSU.EDU, E-mail: kni1@PSU.EDU [Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, PA (United States); Moreno, C. Arenas, E-mail: cristina.arenas@UPC.EDU [Department of Physics and Nuclear Engineering, Technical University of Catalonia, Barcelona (Spain)

    2011-07-01

    The study explored the calculation of uncertainty based on available covariance data and computational tools. Uncertainty due to temperature changes and different fuel compositions are the main focus of this analysis. Selected unit fuel pin cells were analyzed according to the OECD LWR UAM benchmark specifications. Criticality and uncertainty analyses were performed using TSUNAMI-1D sequence in SCALE 6.0. It was found that uncertainties increase with increasing temperature while k{sub eff} decreases. This increase in the uncertainty is due to the increase in sensitivity of the largest contributor of uncertainty, namely nuclide reaction {sup 238}U (n, gamma). The sensitivity grew larger as the capture cross-section of {sup 238}U expanded due to Doppler broadening. In addition, three different compositions (UOx, MOx, and UOxGd{sub 2}O{sub 3}) of fuel cells were analyzed. It showed a remarkable increase in uncertainty in k{sub eff} for the case of the MOx fuel cell and UOxGd{sub 2}O{sub 3} fuel cell. The increase in the uncertainty of k{sub eff} in UOxGd{sub 2}O{sub 3} fuel was nearly twice of that in MOx fuel and almost four times the amount in UOx fuel. The components of the uncertainties in k{sub eff} in each case were examined and it was found that the neutron-nuclide reaction of {sup 238}U, mainly (n,n'), contributed the most to the uncertainties in the cases of MOx and UOxGd{sub 2}O{sub 3}. At higher energy, the covariance coefficient matrix of {sup 238}U (n,n') to {sup 238}U (n,n') and {sup 238}U (n,n') cross-section showed very large values. Further, examination of the UOxGd{sub 2}O{sub 3} case found that the {sup 238}U (n,n') became the dominant contributor to the uncertainty because most of the thermal neutrons in the cell were absorbed by Gadolinium in UOxGd{sub 2}O{sub 3} case and thus shifting the neutron spectrum to higher energy. For the MOx case on other hand, {sup 239}Pu has a very strong absorption cross-section at low energy

  10. Statistical uncertainty of response characteristic of building-appendage system for spectrum-compatible artificial earthquake motion

    International Nuclear Information System (INIS)

    Kurosaki, A.; Kozeki, M.

    1981-01-01

    Spectrum-compatible artificial time histories of ground motions are frequently used for the seismic design of nuclear power plant structures and components. However, statistical uncertainty of the responses of building structures and mechanical components mounted on the building (building-appendage systems) are anticipated, since an artificial time history is no more than one sample from a population of such time histories that match a specified design response spectrum. This uncertainty may spoil the reliability of the seismic design and therefore the extent of the uncertainty of the response characteristic is a matter of great concern. In this paper, above-mentioned uncertainty of the dynamic response characteristics of the building-appendage system to the spectrum-compatible artificial earthquake is investigated. (orig./RW)

  11. STATISTICAL ANALYSIS OF TANK 18F FLOOR SAMPLE RESULTS

    Energy Technology Data Exchange (ETDEWEB)

    Harris, S.

    2010-09-02

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

  12. Statistical Analysis Of Tank 19F Floor Sample Results

    International Nuclear Information System (INIS)

    Harris, S.

    2010-01-01

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

  13. Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

    Science.gov (United States)

    Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

    2017-11-01

    Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize ( Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

  14. Uncertainty analysis technique of dynamic response and cumulative damage properties of piping system

    International Nuclear Information System (INIS)

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

    1982-01-01

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

  15. Uncertainty on faecal analysis on dose assessment

    Energy Technology Data Exchange (ETDEWEB)

    Juliao, Ligia M.Q.C.; Melo, Dunstana R.; Sousa, Wanderson de O.; Santos, Maristela S.; Fernandes, Paulo Cesar P. [Instituto de Radioprotecao e Dosimetria, Comissao Nacional de Energia Nuclear, Av. Salvador Allende s/n. Via 9, Recreio, CEP 22780-160, Rio de Janeiro, RJ (Brazil)

    2007-07-01

    Monitoring programmes for internal dose assessment may need to have a combination of bioassay techniques, e.g. urine and faecal analysis, especially in workplaces where compounds of different solubilities are handled and also in cases of accidental intakes. Faecal analysis may be an important data for assessment of committed effective dose due to exposure to insoluble compounds, since the activity excreted by urine may not be detectable, unless a very sensitive measurement system is available. This paper discusses the variability of the daily faecal excretion based on data from just one daily collection; collection during three consecutive days: samples analysed individually and samples analysed as a pool. The results suggest that just 1 d collection is not appropriate for dose assessment, since the 24 h uranium excretion may vary by a factor of 40. On the basis of this analysis, the recommendation should be faecal collection during three consecutive days, and samples analysed as a pool, it is more economic and faster. (authors)

  16. 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

  17. Best estimate analysis of LOFT L2-5 with CATHARE: uncertainty and sensitivity analysis

    Energy Technology Data Exchange (ETDEWEB)

    JOUCLA, Jerome; PROBST, Pierre [Institute for Radiological Protection and Nuclear Safety, Fontenay-aux-Roses (France); FOUET, Fabrice [APTUS, Versailles (France)

    2008-07-01

    The revision of the 10 CFR50.46 in 1988 has made possible the use of best-estimate codes. They may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. In the safety analysis of the large break loss of coolant accident, it was agreed that the 95. percentile estimated with a high degree of confidence should be lower than the acceptance criteria. It appeared necessary to IRSN, technical support of the French Safety Authority, to get more insight into these strategies which are being developed not only in thermal-hydraulics but in other fields such as in neutronics. To estimate the 95. percentile with a high confidence level, we propose to use rank statistics or bootstrap. Toward the objective of assessing uncertainty, it is useful to determine and to classify the main input parameters. We suggest approximating the code by a surrogate model, the Kriging model, which will be used to make a sensitivity analysis with the SOBOL methodology. This paper presents the application of two new methodologies of how to make the uncertainty and sensitivity analysis on the maximum peak cladding temperature of the LOFT L2-5 test with the CATHARE code. (authors)

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

    International Nuclear Information System (INIS)

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

    1997-01-01

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

  19. Statistical mechanical analysis of LMFBR fuel cladding tubes

    International Nuclear Information System (INIS)

    Poncelet, J.-P.; Pay, A.

    1977-01-01

    The most important design requirement on fuel pin cladding for LMFBR's is its mechanical integrity. Disruptive factors include internal pressure from mixed oxide fuel fission gas release, thermal stresses and high temperature creep, neutron-induced differential void-swelling as a source of stress in the cladding and irradiation creep of stainless steel material, corrosion by fission products. Under irradiation these load-restraining mechanisms are accentuated by stainless steel embrittlement and strength alterations. To account for the numerous uncertainties involved in the analysis by theoretical models and computer codes statistical tools are unavoidably requested, i.e. Monte Carlo simulation methods. Thanks to these techniques, uncertainties in nominal characteristics, material properties and environmental conditions can be linked up in a correct way and used for a more accurate conceptual design. (Auth.)

  20. Planning for robust reserve networks using uncertainty analysis

    Science.gov (United States)

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

    2006-01-01

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

  1. Introductory statistics and random phenomena uncertainty, complexity and chaotic behavior in engineering and science

    CERN Document Server

    Denker, Manfred

    2017-01-01

    Introductory Statistics and Random Phenomena integrates traditional statistical data analysis with new computational experimentation capabilities and concepts of algorithmic complexity and chaotic behavior in nonlinear dynamic systems.  This was the first advanced text/reference to bring together such a comprehensive variety of tools for the study of random phenomena occurring in engineering and the natural, life, and social sciences. The crucial computer experiments are conducted using the readily available computer program Mathematica® Uncertain Virtual Worlds™ software packages which optimize and facilitate the simulation environment.  Brief tutorials are included that explain how to use theMathematica® programs for effective simulation and computer experiments.  Large and original real-life data sets are introduced and analyzed as a model for independent study. This is an excellent classroom tool and self-study guide.  The material is presented in a clear and accessible style providing numerous...

  2. Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, appendices A and B

    International Nuclear Information System (INIS)

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

    1995-01-01

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

  3. Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report

    International Nuclear Information System (INIS)

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

    1995-01-01

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

  4. Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, appendices A and B

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-01-01

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

  5. Uncertainty and sensitivity analysis for the simulation of a station blackout scenario in the Jules Horowitz Reactor

    International Nuclear Information System (INIS)

    Ghione, Alberto; Noel, Brigitte; Vinai, Paolo; Demazière, Christophe

    2017-01-01

    Highlights: • A station blackout scenario in the Jules Horowitz Reactor is analyzed using CATHARE. • Input and model uncertainties relevant to the transient, are considered. • A statistical methodology for the propagation of the uncertainties is applied. • No safety criteria are exceeded and sufficiently large safety margins are estimated. • The most influential uncertainties are determined with a sensitivity analysis. - Abstract: An uncertainty and sensitivity analysis for the simulation of a station blackout scenario in the Jules Horowitz Reactor (JHR) is presented. The JHR is a new material testing reactor under construction at CEA on the Cadarache site, France. The thermal-hydraulic system code CATHARE is applied to investigate the response of the reactor system to the scenario. The uncertainty and sensitivity study was based on a statistical methodology for code uncertainty propagation, and the ‘Uncertainty and Sensitivity’ platform URANIE was used. Accordingly, the input uncertainties relevant to the transient, were identified, quantified, and propagated to the code output. The results show that the safety criteria are not exceeded and sufficiently large safety margins exist. In addition, the most influential input uncertainties on the safety parameters were found by making use of a sensitivity analysis.

  6. Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

    Energy Technology Data Exchange (ETDEWEB)

    Bruschewski, Martin; Schiffer, Heinz-Peter [Technische Universitaet Darmstadt, Institute of Gas Turbines and Aerospace Propulsion, Darmstadt (Germany); Freudenhammer, Daniel [Technische Universitaet Darmstadt, Institute of Fluid Mechanics and Aerodynamics, Center of Smart Interfaces, Darmstadt (Germany); Buchenberg, Waltraud B. [University Medical Center Freiburg, Medical Physics, Department of Radiology, Freiburg (Germany); Grundmann, Sven [University of Rostock, Institute of Fluid Mechanics, Rostock (Germany)

    2016-05-15

    Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75% is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented. (orig.)

  7. Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

    Science.gov (United States)

    Bruschewski, Martin; Freudenhammer, Daniel; Buchenberg, Waltraud B.; Schiffer, Heinz-Peter; Grundmann, Sven

    2016-05-01

    Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75 % is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented.

  8. Uncertainty analysis of the 35% reactor inlet header break in a CANDU 6 reactor using RELAP/SCDAPSIM/MOD4.0 with integrated uncertainty analysis option

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-07-01

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

  9. Uncertainty analysis of the 35% reactor inlet header break in a CANDU 6 reactor using RELAP/SCDAPSIM/MOD4.0 with integrated uncertainty analysis option

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  10. PAPIRUS, a parallel computing framework for sensitivity analysis, uncertainty propagation, and estimation of parameter distribution

    International Nuclear Information System (INIS)

    Heo, Jaeseok; Kim, Kyung Doo

    2015-01-01

    Highlights: • We developed an interface between an engineering simulation code and statistical analysis software. • Multiple packages of the sensitivity analysis, uncertainty quantification, and parameter estimation algorithms are implemented in the framework. • Parallel computing algorithms are also implemented in the framework to solve multiple computational problems simultaneously. - Abstract: This paper introduces a statistical data analysis toolkit, PAPIRUS, designed to perform the model calibration, uncertainty propagation, Chi-square linearity test, and sensitivity analysis for both linear and nonlinear problems. The PAPIRUS was developed by implementing multiple packages of methodologies, and building an interface between an engineering simulation code and the statistical analysis algorithms. A parallel computing framework is implemented in the PAPIRUS with multiple computing resources and proper communications between the server and the clients of each processor. It was shown that even though a large amount of data is considered for the engineering calculation, the distributions of the model parameters and the calculation results can be quantified accurately with significant reductions in computational effort. A general description about the PAPIRUS with a graphical user interface is presented in Section 2. Sections 2.1–2.5 present the methodologies of data assimilation, uncertainty propagation, Chi-square linearity test, and sensitivity analysis implemented in the toolkit with some results obtained by each module of the software. Parallel computing algorithms adopted in the framework to solve multiple computational problems simultaneously are also summarized in the paper

  11. PAPIRUS, a parallel computing framework for sensitivity analysis, uncertainty propagation, and estimation of parameter distribution

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Jaeseok, E-mail: jheo@kaeri.re.kr; Kim, Kyung Doo, E-mail: kdkim@kaeri.re.kr

    2015-10-15

    Highlights: • We developed an interface between an engineering simulation code and statistical analysis software. • Multiple packages of the sensitivity analysis, uncertainty quantification, and parameter estimation algorithms are implemented in the framework. • Parallel computing algorithms are also implemented in the framework to solve multiple computational problems simultaneously. - Abstract: This paper introduces a statistical data analysis toolkit, PAPIRUS, designed to perform the model calibration, uncertainty propagation, Chi-square linearity test, and sensitivity analysis for both linear and nonlinear problems. The PAPIRUS was developed by implementing multiple packages of methodologies, and building an interface between an engineering simulation code and the statistical analysis algorithms. A parallel computing framework is implemented in the PAPIRUS with multiple computing resources and proper communications between the server and the clients of each processor. It was shown that even though a large amount of data is considered for the engineering calculation, the distributions of the model parameters and the calculation results can be quantified accurately with significant reductions in computational effort. A general description about the PAPIRUS with a graphical user interface is presented in Section 2. Sections 2.1–2.5 present the methodologies of data assimilation, uncertainty propagation, Chi-square linearity test, and sensitivity analysis implemented in the toolkit with some results obtained by each module of the software. Parallel computing algorithms adopted in the framework to solve multiple computational problems simultaneously are also summarized in the paper.

  12. Practical Policy Applications of Uncertainty Analysis for National Greenhouse Gas Inventories

    Energy Technology Data Exchange (ETDEWEB)

    Gillenwater, M. [Environmental Resources Trust (United States)], E-mail: mgillenwater@ert.net; Sussman, F.; Cohen, J. [ICF International (United States)

    2007-09-15

    goals precisely in terms of relationships among important variables (such as emissions estimate, commitment level, or statistical confidence); and (3) develop a quantifiable adjustment mechanism that reflects these environmental goals. We recommend that countries implement an investigation-focused (i.e., qualitative) uncertainty analysis that will (1) provide the type of information necessary to develop more substantive, and potentially useful, quantitative uncertainty estimates-regardless of whether those quantitative estimates are used for policy purposes; and (2) provide information needed to understand the likely causes of uncertainty in inventory data and thereby point to ways to improve inventory quality (i.e., accuracy, transparency, completeness, and consistency)

  13. Practical Policy Applications of Uncertainty Analysis for National Greenhouse Gas Inventories

    International Nuclear Information System (INIS)

    Gillenwater, M.; Sussman, F.; Cohen, J.

    2007-01-01

    goals precisely in terms of relationships among important variables (such as emissions estimate, commitment level, or statistical confidence); and (3) develop a quantifiable adjustment mechanism that reflects these environmental goals. We recommend that countries implement an investigation-focused (i.e., qualitative) uncertainty analysis that will (1) provide the type of information necessary to develop more substantive, and potentially useful, quantitative uncertainty estimates-regardless of whether those quantitative estimates are used for policy purposes; and (2) provide information needed to understand the likely causes of uncertainty in inventory data and thereby point to ways to improve inventory quality (i.e., accuracy, transparency, completeness, and consistency)

  14. Classification, (big) data analysis and statistical learning

    CERN Document Server

    Conversano, Claudio; Vichi, Maurizio

    2018-01-01

    This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pul...

  15. Coupled code analysis of uncertainty and sensitivity of Kalinin-3 benchmark

    Energy Technology Data Exchange (ETDEWEB)

    Pasichnyk, Ihor; Zwermann, Winfried; Velkov, Kiril [Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS) gGmbH, Garching (Germany); Nikonov, Sergey [VNIIAES, Moscow (Russian Federation)

    2016-09-15

    An uncertainty and sensitivity analysis is performed for the OECD/NEA coolant transient Benchmark (K-3) on measured data at Kalinin-3 Nuclear Power Plant (NPP). A switch off of one main coolant pump (MCP) at nominal reactor power is calculated using a coupled thermohydraulic and neutron-kinetic ATHLET-PARCS code. The objectives are to study uncertainty of total reactor power and to identify the main sources of reactor power uncertainty. The GRS uncertainty and sensitivity software package XSUSA is applied to propagate uncertainties in nuclear data libraries to the full core coupled transient calculations. A set of most important thermal-hydraulic parameters of the primary circuit is identified and a total of 23 thermohydraulic parameters are statistically varied using GRS code SUSA. The ATHLET model contains also a balance-of-plant (BOP) model which is simulated using ATHLET GCSM module. In particular the operation of the main steam generator regulators is modelled in detail. A set of 200 varied coupled ATHLET-PARCS calculations is analyzed. The results obtained show a clustering effect in the behavior of global reactor parameters. It is found that the GCSM system together with varied input parameters strongly influence the overall nuclear power plant behavior and can even lead to a new scenario. Possible reasons of the clustering effect are discussed in the paper. This work is a step forward in establishing a ''best-estimate calculations in combination with performing uncertainty analysis'' methodology for coupled full core calculations.

  16. 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.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  18. Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model

    KAUST Repository

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

    2016-01-01

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

  19. Statistics and analysis of scientific data

    CERN Document Server

    Bonamente, Massimiliano

    2013-01-01

    Statistics and Analysis of Scientific Data covers the foundations of probability theory and statistics, and a number of numerical and analytical methods that are essential for the present-day analyst of scientific data. Topics covered include probability theory, distribution functions of statistics, fits to two-dimensional datasheets and parameter estimation, Monte Carlo methods and Markov chains. Equal attention is paid to the theory and its practical application, and results from classic experiments in various fields are used to illustrate the importance of statistics in the analysis of scientific data. The main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and proactive use of the material for practical applications. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is us...

  20. Rweb:Web-based Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Jeff Banfield

    1999-03-01

    Full Text Available Rweb is a freely accessible statistical analysis environment that is delivered through the World Wide Web (WWW. It is based on R, a well known statistical analysis package. The only requirement to run the basic Rweb interface is a WWW browser that supports forms. If you want graphical output you must, of course, have a browser that supports graphics. The interface provides access to WWW accessible data sets, so you may run Rweb on your own data. Rweb can provide a four window statistical computing environment (code input, text output, graphical output, and error information through browsers that support Javascript. There is also a set of point and click modules under development for use in introductory statistics courses.

  1. Error Analysis of CM Data Products Sources of Uncertainty

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1997-12-01

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

  3. Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for deposited material and external doses. Volume 2: Appendices

    Energy Technology Data Exchange (ETDEWEB)

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

    1997-12-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-04-01

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

  5. Semiclassical analysis, Witten Laplacians, and statistical mechanis

    CERN Document Server

    Helffer, Bernard

    2002-01-01

    This important book explains how the technique of Witten Laplacians may be useful in statistical mechanics. It considers the problem of analyzing the decay of correlations, after presenting its origin in statistical mechanics. In addition, it compares the Witten Laplacian approach with other techniques, such as the transfer matrix approach and its semiclassical analysis. The author concludes by providing a complete proof of the uniform Log-Sobolev inequality. Contents: Witten Laplacians Approach; Problems in Statistical Mechanics with Discrete Spins; Laplace Integrals and Transfer Operators; S

  6. A statistical approach to plasma profile analysis

    International Nuclear Information System (INIS)

    Kardaun, O.J.W.F.; McCarthy, P.J.; Lackner, K.; Riedel, K.S.

    1990-05-01

    A general statistical approach to the parameterisation and analysis of tokamak profiles is presented. The modelling of the profile dependence on both the radius and the plasma parameters is discussed, and pertinent, classical as well as robust, methods of estimation are reviewed. Special attention is given to statistical tests for discriminating between the various models, and to the construction of confidence intervals for the parameterised profiles and the associated global quantities. The statistical approach is shown to provide a rigorous approach to the empirical testing of plasma profile invariance. (orig.)

  7. Reproducible statistical analysis with multiple languages

    DEFF Research Database (Denmark)

    Lenth, Russell; Højsgaard, Søren

    2011-01-01

    This paper describes the system for making reproducible statistical analyses. differs from other systems for reproducible analysis in several ways. The two main differences are: (1) Several statistics programs can be in used in the same document. (2) Documents can be prepared using OpenOffice or ......Office or \\LaTeX. The main part of this paper is an example showing how to use and together in an OpenOffice text document. The paper also contains some practical considerations on the use of literate programming in statistics....

  8. Foundation of statistical energy analysis in vibroacoustics

    CERN Document Server

    Le Bot, A

    2015-01-01

    This title deals with the statistical theory of sound and vibration. The foundation of statistical energy analysis is presented in great detail. In the modal approach, an introduction to random vibration with application to complex systems having a large number of modes is provided. For the wave approach, the phenomena of propagation, group speed, and energy transport are extensively discussed. Particular emphasis is given to the emergence of diffuse field, the central concept of the theory.

  9. A New Form of Nondestructive Strength-Estimating Statistical Models Accounting for Uncertainty of Model and Aging Effect of Concrete

    International Nuclear Information System (INIS)

    Hong, Kee Jeung; Kim, Jee Sang

    2009-01-01

    As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.

  10. Uncertainty analysis of suppression pool heating during an ATWS in a BWR-5 plant

    International Nuclear Information System (INIS)

    Wulff, W.; Cheng, H.S.; Mallen, A.N.; Johnsen, G.W.; Lellouche, G.S.

    1994-03-01

    The uncertainty has been estimated of predicting the peak temperature in the suppression pool of a BWR power plant, which undergoes an NRC-postulated Anticipated Transient Without Scram (ATWS). The ATWS is initiated by recirculation-pump trips, and then leads to power and flow oscillations as they had occurred at the LaSalle-2 Power Station in March of 1988. After limit-cycle oscillations have been established, the turbines are tripped, but without MSIV closure, allowing steam discharge through the turbine bypass into the condenser. Postulated operator actions, namely to lower the reactor vessel pressure and the level elevation in the downcomer, are simulated by a robot model which accounts for operator uncertainty. All balance of plant and control systems modeling uncertainties were part of the statistical uncertainty analysis that was patterned after the Code Scaling, Applicability and Uncertainty (CSAU) evaluation methodology. The analysis showed that the predicted suppression-pool peak temperature of 329.3 K (133 degrees F) has a 95-percentile uncertainty of 14.4 K (26 degrees F), and that the size of this uncertainty bracket is dominated by the experimental uncertainty of measuring Safety and Relief Valve mass flow rates under critical-flow conditions. The analysis showed also that the probability of exceeding the suppression-pool temperature limit of 352.6 K (175 degrees F) is most likely zero (it is estimated as < 5-104). The square root of the sum of the squares of all the computed peak pool temperatures is 350.7 K (171.6 degrees F)

  11. An estimation of uncertainties in containment P/T analysis using CONTEMPT/LT code

    International Nuclear Information System (INIS)

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

    1991-01-01

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

  12. Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation.

    Science.gov (United States)

    Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González

    2016-01-01

    Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

  13. A Statistical Toolkit for Data Analysis

    International Nuclear Information System (INIS)

    Donadio, S.; Guatelli, S.; Mascialino, B.; Pfeiffer, A.; Pia, M.G.; Ribon, A.; Viarengo, P.

    2006-01-01

    The present project aims to develop an open-source and object-oriented software Toolkit for statistical data analysis. Its statistical testing component contains a variety of Goodness-of-Fit tests, from Chi-squared to Kolmogorov-Smirnov, to less known, but generally much more powerful tests such as Anderson-Darling, Goodman, Fisz-Cramer-von Mises, Kuiper, Tiku. Thanks to the component-based design and the usage of the standard abstract interfaces for data analysis, this tool can be used by other data analysis systems or integrated in experimental software frameworks. This Toolkit has been released and is downloadable from the web. In this paper we describe the statistical details of the algorithms, the computational features of the Toolkit and describe the code validation

  14. An uncertainty analysis using the NRPB accident consequence code Marc

    International Nuclear Information System (INIS)

    Jones, J.A.; Crick, M.J.; Simmonds, J.R.

    1991-01-01

    This paper describes an uncertainty analysis of MARC calculations of the consequences of accidental releases of radioactive materials to atmosphere. A total of 98 parameters describing the transfer of material through the environment to man, the doses received, and the health effects resulting from these doses, was considered. The uncertainties in the numbers of early and late health effects, numbers of people affected by countermeasures, the amounts of food restricted and the economic costs of the accident were estimated. This paper concentrates on the results for early death and fatal cancer for a large hypothetical release from a PWR

  15. Analysis of statistical misconception in terms of statistical reasoning

    Science.gov (United States)

    Maryati, I.; Priatna, N.

    2018-05-01

    Reasoning skill is needed for everyone to face globalization era, because every person have to be able to manage and use information from all over the world which can be obtained easily. Statistical reasoning skill is the ability to collect, group, process, interpret, and draw conclusion of information. Developing this skill can be done through various levels of education. However, the skill is low because many people assume that statistics is just the ability to count and using formulas and so do students. Students still have negative attitude toward course which is related to research. The purpose of this research is analyzing students’ misconception in descriptive statistic course toward the statistical reasoning skill. The observation was done by analyzing the misconception test result and statistical reasoning skill test; observing the students’ misconception effect toward statistical reasoning skill. The sample of this research was 32 students of math education department who had taken descriptive statistic course. The mean value of misconception test was 49,7 and standard deviation was 10,6 whereas the mean value of statistical reasoning skill test was 51,8 and standard deviation was 8,5. If the minimal value is 65 to state the standard achievement of a course competence, students’ mean value is lower than the standard competence. The result of students’ misconception study emphasized on which sub discussion that should be considered. Based on the assessment result, it was found that students’ misconception happen on this: 1) writing mathematical sentence and symbol well, 2) understanding basic definitions, 3) determining concept that will be used in solving problem. In statistical reasoning skill, the assessment was done to measure reasoning from: 1) data, 2) representation, 3) statistic format, 4) probability, 5) sample, and 6) association.

  16. 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.

  17. Decision analysis of shoreline protection under climate change uncertainty

    Science.gov (United States)

    Chao, Philip T.; Hobbs, Benjamin F.

    1997-04-01

    If global warming occurs, it could significantly affect water resource distribution and availability. Yet it is unclear whether the prospect of such change is relevant to water resources management decisions being made today. We model a shoreline protection decision problem with a stochastic dynamic program (SDP) to determine whether consideration of the possibility of climate change would alter the decision. Three questions are addressed with the SDP: (l) How important is climate change compared to other uncertainties?, (2) What is the economic loss if climate change uncertainty is ignored?, and (3) How does belief in climate change affect the timing of the decision? In the case study, sensitivity analysis shows that uncertainty in real discount rates has a stronger effect upon the decision than belief in climate change. Nevertheless, a strong belief in climate change makes the shoreline protection project less attractive and often alters the decision to build it.

  18. Statistical analysis of network data with R

    CERN Document Server

    Kolaczyk, Eric D

    2014-01-01

    Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

  19. Uncertainty theory

    CERN Document Server

    Liu, Baoding

    2015-01-01

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

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

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  1. Uncertainty Analysis of RELAP5-3D

    Energy Technology Data Exchange (ETDEWEB)

    Alexandra E Gertman; Dr. George L Mesina

    2012-07-01

    As world-wide energy consumption continues to increase, so does the demand for the use of alternative energy sources, such as Nuclear Energy. Nuclear Power Plants currently supply over 370 gigawatts of electricity, and more than 60 new nuclear reactors have been commissioned by 15 different countries. The primary concern for Nuclear Power Plant operation and lisencing has been safety. The safety of the operation of Nuclear Power Plants is no simple matter- it involves the training of operators, design of the reactor, as well as equipment and design upgrades throughout the lifetime of the reactor, etc. To safely design, operate, and understand nuclear power plants, industry and government alike have relied upon the use of best-estimate simulation codes, which allow for an accurate model of any given plant to be created with well-defined margins of safety. The most widely used of these best-estimate simulation codes in the Nuclear Power industry is RELAP5-3D. Our project focused on improving the modeling capabilities of RELAP5-3D by developing uncertainty estimates for its calculations. This work involved analyzing high, medium, and low ranked phenomena from an INL PIRT on a small break Loss-Of-Coolant Accident as wall as an analysis of a large break Loss-Of- Coolant Accident. Statistical analyses were performed using correlation coefficients. To perform the studies, computer programs were written that modify a template RELAP5 input deck to produce one deck for each combination of key input parameters. Python scripting enabled the running of the generated input files with RELAP5-3D on INL’s massively parallel cluster system. Data from the studies was collected and analyzed with SAS. A summary of the results of our studies are presented.

  2. Using Pre-Statistical Analysis to Streamline Monitoring Assessments

    International Nuclear Information System (INIS)

    Reed, J.K.

    1999-01-01

    A variety of statistical methods exist to aid evaluation of groundwater quality and subsequent decision making in regulatory programs. These methods are applied because of large temporal and spatial extrapolations commonly applied to these data. In short, statistical conclusions often serve as a surrogate for knowledge. However, facilities with mature monitoring programs that have generated abundant data have inherently less uncertainty because of the sheer quantity of analytical results. In these cases, statistical tests can be less important, and ''expert'' data analysis should assume an important screening role.The WSRC Environmental Protection Department, working with the General Separations Area BSRI Environmental Restoration project team has developed a method for an Integrated Hydrogeological Analysis (IHA) of historical water quality data from the F and H Seepage Basins groundwater remediation project. The IHA combines common sense analytical techniques and a GIS presentation that force direct interactive evaluation of the data. The IHA can perform multiple data analysis tasks required by the RCRA permit. These include: (1) Development of a groundwater quality baseline prior to remediation startup, (2) Targeting of constituents for removal from RCRA GWPS, (3) Targeting of constituents for removal from UIC, permit, (4) Targeting of constituents for reduced, (5)Targeting of monitoring wells not producing representative samples, (6) Reduction in statistical evaluation, and (7) Identification of contamination from other facilities

  3. Statistical analysis of the early phase of SBO accident for PWR

    Energy Technology Data Exchange (ETDEWEB)

    Kozmenkov, Yaroslav, E-mail: y.kozmenkov@hzdr.de; Jobst, Matthias, E-mail: m.jobst@hzdr.de; Kliem, Soeren, E-mail: s.kliem@hzdr.de; Schaefer, Frank, E-mail: f.schaefer@hzdr.de; Wilhelm, Polina, E-mail: p.wilhelm@hzdr.de

    2017-04-01

    Highlights: • Best estimate model of generic German PWR is used in ATHLET-CD simulations. • Uncertainty and sensitivity analysis of the early phase of SBO accident is presented. • Prediction intervals for occurrence of main events are evaluated. - Abstract: A statistical approach is used to analyse the early phase of station blackout accident for generic German PWR with the best estimate system code ATHLET-CD as a computation tool. The analysis is mainly focused on the timescale uncertainties of the accident events which can be detected at the plant. The developed input deck allows variations of all input uncertainty parameters relevant to the case. The list of identified and quantified input uncertainties includes 30 parameters related to the simulated physical phenomena/processes. Time uncertainties of main events as well as the major contributors to these uncertainties are defined. The uncertainty in decay heat has the highest contribution to the uncertainties of the analysed events. A linear regression analysis is used for predicting times of future events from detected times of occurred/past events. An accuracy of event predictions is estimated and verified. The presented statistical approach could be helpful for assessing and improving existing or elaborating additional emergency operating procedures aimed to prevent severe damage of reactor core.

  4. 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

  5. Estimates of uncertainties in analysis of positron lifetime spectra for metals

    International Nuclear Information System (INIS)

    Eldrup, M.; Huang, Y.M.; McKee, B.T.A.

    1978-01-01

    The effects of uncertainties and errors in various constraints used in the analysis of multi-component life-time spectra of positrons annihilating in metals containing defects have been investigated in detail using computer simulated decay spectra and subsequent analysis. It is found that the errors in the fitted values of the main component lifetimes and intensities introduced from incorrect values of the instrumental resolution function and off the source-surface components can easily exceed the statistic uncertainties. The effect of an incorrect resolution function may be reduced by excluding the peak regions of the spectra from the analysis. The influence of using incorrect source-surface components in the analysis may on the other hand be reduced by including the peak regions of the spectra. A main conclusion of the work is that extreme caution should be exercised to avoid introducing large errors through the constraints used in the analysis of experimental lifetime data. (orig.) [de

  6. Statistics and analysis of scientific data

    CERN Document Server

    Bonamente, Massimiliano

    2017-01-01

    The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked,to improve the readabili...

  7. Statistical mechanical analysis of LMFBR fuel cladding tubes

    International Nuclear Information System (INIS)

    Poncelet, J.-P.; Pay, A.

    1977-01-01

    The most important design requirement on fuel pin cladding for LMFBR's is its mechanical integrity. Disruptive factors include internal pressure from mixed oxide fuel fission gas release, thermal stresses and high temperature creep, neutron-induced differential void-swelling as a source of stress in the cladding and irradiation creep of stainless steel material, corrosion by fission products. Under irradiation these load-restraining mechanisms are accentuated by stainless steel embrittlement and strength alterations. To account for the numerous uncertainties involved in the analysis by theoretical models and computer codes statistical tools are unavoidably requested, i.e. Monte Carlo simulation methods. Thanks to these techniques, uncertainties in nominal characteristics, material properties and environmental conditions can be linked up in a correct way and used for a more accurate conceptual design. First, a thermal creep damage index is set up through a sufficiently sophisticated clad physical analysis including arbitrary time dependence of power and neutron flux as well as effects of sodium temperature, burnup and steel mechanical behavior. Although this strain limit approach implies a more general but time consuming model., on the counterpart the net output is improved and e.g. clad temperature, stress and strain maxima may be easily assessed. A full spectrum of variables are statistically treated to account for their probability distributions. Creep damage probability may be obtained and can contribute to a quantitative fuel probability estimation

  8. Statistical analysis on extreme wave height

    Digital Repository Service at National Institute of Oceanography (India)

    Teena, N.V.; SanilKumar, V.; Sudheesh, K.; Sajeev, R.

    -294. • WAFO (2000) – A MATLAB toolbox for analysis of random waves and loads, Lund University, Sweden, homepage http://www.maths.lth.se/matstat/wafo/,2000. 15    Table 1: Statistical results of data and fitted distribution for cumulative distribution...

  9. Applied Behavior Analysis and Statistical Process Control?

    Science.gov (United States)

    Hopkins, B. L.

    1995-01-01

    Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…

  10. The fuzzy approach to statistical analysis

    NARCIS (Netherlands)

    Coppi, Renato; Gil, Maria A.; Kiers, Henk A. L.

    2006-01-01

    For the last decades, research studies have been developed in which a coalition of Fuzzy Sets Theory and Statistics has been established with different purposes. These namely are: (i) to introduce new data analysis problems in which the objective involves either fuzzy relationships or fuzzy terms;

  11. Plasma data analysis using statistical analysis system

    International Nuclear Information System (INIS)

    Yoshida, Z.; Iwata, Y.; Fukuda, Y.; Inoue, N.

    1987-01-01

    Multivariate factor analysis has been applied to a plasma data base of REPUTE-1. The characteristics of the reverse field pinch plasma in REPUTE-1 are shown to be explained by four independent parameters which are described in the report. The well known scaling laws F/sub chi/ proportional to I/sub p/, T/sub e/ proportional to I/sub p/, and tau/sub E/ proportional to N/sub e/ are also confirmed. 4 refs., 8 figs., 1 tab

  12. Statistical analysis of metallicity in spiral galaxies

    Energy Technology Data Exchange (ETDEWEB)

    Galeotti, P [Consiglio Nazionale delle Ricerche, Turin (Italy). Lab. di Cosmo-Geofisica; Turin Univ. (Italy). Ist. di Fisica Generale)

    1981-04-01

    A principal component analysis of metallicity and other integral properties of 33 spiral galaxies is presented; the involved parameters are: morphological type, diameter, luminosity and metallicity. From the statistical analysis it is concluded that the sample has only two significant dimensions and additonal tests, involving different parameters, show similar results. Thus it seems that only type and luminosity are independent variables, being the other integral properties of spiral galaxies correlated with them.

  13. Selected papers on analysis, probability, and statistics

    CERN Document Server

    Nomizu, Katsumi

    1994-01-01

    This book presents papers that originally appeared in the Japanese journal Sugaku. The papers fall into the general area of mathematical analysis as it pertains to probability and statistics, dynamical systems, differential equations and analytic function theory. Among the topics discussed are: stochastic differential equations, spectra of the Laplacian and Schrödinger operators, nonlinear partial differential equations which generate dissipative dynamical systems, fractal analysis on self-similar sets and the global structure of analytic functions.

  14. Statistical evaluation of vibration analysis techniques

    Science.gov (United States)

    Milner, G. Martin; Miller, Patrice S.

    1987-01-01

    An evaluation methodology is presented for a selection of candidate vibration analysis techniques applicable to machinery representative of the environmental control and life support system of advanced spacecraft; illustrative results are given. Attention is given to the statistical analysis of small sample experiments, the quantification of detection performance for diverse techniques through the computation of probability of detection versus probability of false alarm, and the quantification of diagnostic performance.

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  16. A new fast algorithm for the evaluation of regions of interest and statistical uncertainty in computed tomography

    International Nuclear Information System (INIS)

    Huesman, R.H.

    1984-01-01

    A new algorithm for region of interest evaluation in computed tomography is described. Region of interest evaluation is a technique used to improve quantitation of the tomographic imaging process by summing (or averaging) the reconstructed quantity throughout a volume of particular significance. An important application of this procedure arises in the analysis of dynamic emission computed tomographic data, in which the uptake and clearance of radiotracers are used to determine the blood flow and/or physiologica function of tissue within the significant volume. The new algorithm replaces the conventional technique of repeated image reconstructions with one in which projected regions are convolved and then used to form multiple vector inner products with the raw tomographic data sets. Quantitation of regions of interest is made without the need for reconstruction of tomographic images. The computational advantage of the new algorithm over conventional methods is between factors of 20 and of 500 for typical applications encountered in medical science studies. The greatest benefit is the ease with which the statistical uncertainty of the result is computed. The entire covariance matrix for the evaluation of regions of interest can be calculated with relatively few operations. (author)

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

    International Nuclear Information System (INIS)

    Prosek, A.; Mavko, B.

    2003-01-01

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

  18. The deuteron-radius puzzle is alive: A new analysis of nuclear structure uncertainties

    Science.gov (United States)

    Hernandez, O. J.; Ekström, A.; Nevo Dinur, N.; Ji, C.; Bacca, S.; Barnea, N.

    2018-03-01

    To shed light on the deuteron radius puzzle we analyze the theoretical uncertainties of the nuclear structure corrections to the Lamb shift in muonic deuterium. We find that the discrepancy between the calculated two-photon exchange correction and the corresponding experimentally inferred value by Pohl et al. [1] remain. The present result is consistent with our previous estimate, although the discrepancy is reduced from 2.6 σ to about 2 σ. The error analysis includes statistic as well as systematic uncertainties stemming from the use of nucleon-nucleon interactions derived from chiral effective field theory at various orders. We therefore conclude that nuclear theory uncertainty is more likely not the source of the discrepancy.

  19. Statistical Analysis of Data for Timber Strengths

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard

    2003-01-01

    Statistical analyses are performed for material strength parameters from a large number of specimens of structural timber. Non-parametric statistical analysis and fits have been investigated for the following distribution types: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull...... fits to the data available, especially if tail fits are used whereas the Log Normal distribution generally gives a poor fit and larger coefficients of variation, especially if tail fits are used. The implications on the reliability level of typical structural elements and on partial safety factors...... for timber are investigated....

  20. The Statistical Analysis of Time Series

    CERN Document Server

    Anderson, T W

    2011-01-01

    The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

  1. Tolerance analysis in manufacturing using process capability ratio with measurement uncertainty

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  2. Estimates of Uncertainties in Analysis of Positron Lifetime Spectra for Metals

    DEFF Research Database (Denmark)

    Eldrup, Morten Mostgaard; Huang, Y. M.; McKee, B. T. A.

    1978-01-01

    by excluding the peak regions of the spectra from the analysis. The influence of using incorrect source-surface components in the analysis may on the other hand be reduced by including the peak regions of the spectra. A main conclusion of the work is that extreme caution should be exercised to avoid......The effects of uncertainties and errors in various constraints used in the analysis of multi-component life-time spectra of positrons annihilating in metals containing defects have been investigated in detail using computer simulated decay spectra and subsequent analysis. It is found...... that the errors in the fitted values of the main components lifetimes and intensities introduced from incorrect values of the instrumental resolution function and of the source-surface components can easily exceed the statistical uncertainties. The effect of an incorrect resolution function may be reduced...

  3. Impact of statistical uncertainty of the neutron spectrum in the isotopic evolution of fuel

    International Nuclear Information System (INIS)

    Ortega, P.

    2012-01-01

    The results obtained and presented in this study for different calculation conditions (number of stories, number of steps burning, etc.) and their simultaneous impact on neutron spectrum and isotopic composition and a methodology is proposed to determine the minimum parameters for calculation given uncertainty in the results of isotopic composition with high burnup, both UO 2 and MOX fuel.

  4. Transformation & uncertainty : some thoughts on quantum probability theory, quantum statistics, and natural bundles

    NARCIS (Netherlands)

    Janssens, B.

    2010-01-01

    This PHD thesis is concerned partly with uncertainty relations in quantum probability theory, partly with state estimation in quantum stochastics, and partly with natural bundles in differential geometry. The laws of quantum mechanics impose severe restrictions on the performance of measurement.

  5. Developments in statistical analysis in quantitative genetics

    DEFF Research Database (Denmark)

    Sorensen, Daniel

    2009-01-01

    of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...

  6. Statistical Analysis of Big Data on Pharmacogenomics

    Science.gov (United States)

    Fan, Jianqing; Liu, Han

    2013-01-01

    This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

  7. Statistical analysis of next generation sequencing data

    CERN Document Server

    Nettleton, Dan

    2014-01-01

    Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized med...

  8. Robust statistics and geochemical data analysis

    International Nuclear Information System (INIS)

    Di, Z.

    1987-01-01

    Advantages of robust procedures over ordinary least-squares procedures in geochemical data analysis is demonstrated using NURE data from the Hot Springs Quadrangle, South Dakota, USA. Robust principal components analysis with 5% multivariate trimming successfully guarded the analysis against perturbations by outliers and increased the number of interpretable factors. Regression with SINE estimates significantly increased the goodness-of-fit of the regression and improved the correspondence of delineated anomalies with known uranium prospects. Because of the ubiquitous existence of outliers in geochemical data, robust statistical procedures are suggested as routine procedures to replace ordinary least-squares procedures

  9. Quantifying and managing uncertainty in operational modal analysis

    Science.gov (United States)

    Au, Siu-Kui; Brownjohn, James M. W.; Mottershead, John E.

    2018-03-01

    Operational modal analysis aims at identifying the modal properties (natural frequency, damping, etc.) of a structure using only the (output) vibration response measured under ambient conditions. Highly economical and feasible, it is becoming a common practice in full-scale vibration testing. In the absence of (input) loading information, however, the modal properties have significantly higher uncertainty than their counterparts identified from free or forced vibration (known input) tests. Mastering the relationship between identification uncertainty and test configuration is of great interest to both scientists and engineers, e.g., for achievable precision limits and test planning/budgeting. Addressing this challenge beyond the current state-of-the-art that are mostly concerned with identification algorithms, this work obtains closed form analytical expressions for the identification uncertainty (variance) of modal parameters that fundamentally explains the effect of test configuration. Collectively referred as 'uncertainty laws', these expressions are asymptotically correct for well-separated modes, small damping and long data; and are applicable under non-asymptotic situations. They provide a scientific basis for planning and standardization of ambient vibration tests, where factors such as channel noise, sensor number and location can be quantitatively accounted for. The work is reported comprehensively with verification through synthetic and experimental data (laboratory and field), scientific implications and practical guidelines for planning ambient vibration tests.

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

    International Nuclear Information System (INIS)

    Embrechts, M.J.

    1982-02-01

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

  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. Stochastic analysis in production process and ecology under uncertainty

    CERN Document Server

    Bieda, Bogusław

    2014-01-01

    The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. The first chapter defines the Monte Carlo (MC) method and random variables in stochastic models. Chapter two deals with the contamination transport in porous media. Stochastic approach for Municipal Solid Waste transit time contaminants modeling using MC simulation has been worked out. The third chapter describes the risk analysis of the waste to energy facility proposal for Konin city, including the financial aspects. Environmental impact assessment of the ArcelorMittal Steel Power Plant, in Kraków - in the chapter four - is given. Thus, four scenarios of the energy mix production processes were studied. Chapter five contains examples of using ecological Life Cycle Assessment (LCA) - a relatively new method of environmental impact assessment - which help in preparing pro-ecological strategy, and which can lead to reducing t...

  13. Applications of the TSUNAMI sensitivity and uncertainty analysis methodology

    International Nuclear Information System (INIS)

    Rearden, Bradley T.; Hopper, Calvin M.; Elam, Karla R.; Goluoglu, Sedat; Parks, Cecil V.

    2003-01-01

    The TSUNAMI sensitivity and uncertainty analysis tools under development for the SCALE code system have recently been applied in four criticality safety studies. TSUNAMI is used to identify applicable benchmark experiments for criticality code validation, assist in the design of new critical experiments for a particular need, reevaluate previously computed computational biases, and assess the validation coverage and propose a penalty for noncoverage for a specific application. (author)

  14. INTEGRATION OF SYSTEM COMPONENTS AND UNCERTAINTY ANALYSIS - HANFORD EXAMPLES

    International Nuclear Information System (INIS)

    Wood, M.I.

    2009-01-01

    (sm b ullet) Deterministic 'One Off' analyses as basis for evaluating sensitivity and uncertainty relative to reference case (sm b ullet) Spatial coverage identical to reference case (sm b ullet) Two types of analysis assumptions - Minimax parameter values around reference case conditions - 'What If' cases that change reference case condition and associated parameter values (sm b ullet) No conclusions about likelihood of estimated result other than' qualitative expectation that actual outcome should tend toward reference case estimate

  15. Analysis of photon statistics with Silicon Photomultiplier

    International Nuclear Information System (INIS)

    D'Ascenzo, N.; Saveliev, V.; Wang, L.; Xie, Q.

    2015-01-01

    The Silicon Photomultiplier (SiPM) is a novel silicon-based photodetector, which represents the modern perspective of low photon flux detection. The aim of this paper is to provide an introduction on the statistical analysis methods needed to understand and estimate in quantitative way the correct features and description of the response of the SiPM to a coherent source of light

  16. Vapor Pressure Data Analysis and Statistics

    Science.gov (United States)

    2016-12-01

    near 8, 2000, and 200, respectively. The A (or a) value is directly related to vapor pressure and will be greater for high vapor pressure materials...1, (10) where n is the number of data points, Yi is the natural logarithm of the i th experimental vapor pressure value, and Xi is the...VAPOR PRESSURE DATA ANALYSIS AND STATISTICS ECBC-TR-1422 Ann Brozena RESEARCH AND TECHNOLOGY DIRECTORATE

  17. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

    OpenAIRE

    Birle, Stephan;Hussein, Mohamed Ahmed;Becker, Thomas

    2017-01-01

    In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. How...

  18. State analysis of BOP using statistical and heuristic methods

    International Nuclear Information System (INIS)

    Heo, Gyun Young; Chang, Soon Heung

    2003-01-01

    Under the deregulation environment, the performance enhancement of BOP in nuclear power plants is being highlighted. To analyze performance level of BOP, we use the performance test procedures provided from an authorized institution such as ASME. However, through plant investigation, it was proved that the requirements of the performance test procedures about the reliability and quantity of sensors was difficult to be satisfied. As a solution of this, state analysis method that are the expanded concept of signal validation, was proposed on the basis of the statistical and heuristic approaches. Authors recommended the statistical linear regression model by analyzing correlation among BOP parameters as a reference state analysis method. Its advantage is that its derivation is not heuristic, it is possible to calculate model uncertainty, and it is easy to apply to an actual plant. The error of the statistical linear regression model is below 3% under normal as well as abnormal system states. Additionally a neural network model was recommended since the statistical model is impossible to apply to the validation of all of the sensors and is sensitive to the outlier that is the signal located out of a statistical distribution. Because there are a lot of sensors need to be validated in BOP, wavelet analysis (WA) were applied as a pre-processor for the reduction of input dimension and for the enhancement of training accuracy. The outlier localization capability of WA enhanced the robustness of the neural network. The trained neural network restored the degraded signals to the values within ±3% of the true signals

  19. Similarity and uncertainty analysis of the ALLEGRO MOX core

    International Nuclear Information System (INIS)

    Vrban, B.; Hascik, J.; Necas, V.; Slugen, V.

    2015-01-01

    The similarity and uncertainty analysis of the ESNII+ ALLEGRO MOX core has identified specific problems and challenges in the field of neutronic calculations. Similarity assessment identified 9 partly comparable experiments where only one reached ck and E values over 0.9. However the Global Integral Index G remains still low (0.75) and cannot be judge das sufficient. The total uncertainty of calculated k eff induced by XS data is according to our calculation 1.04%. The main contributors to this uncertainty are 239 Pu nubar and 238 U inelastic scattering. The additional margin from uncovered sensitivities was determined to be 0.28%. The identified low number of similar experiments prevents the use of advanced XS adjustment and bias estimation methods. More experimental data are needed and presented results may serve as a basic step in development of necessary critical assemblies. Although exact data are not presented in the paper, faster 44 energy group calculation gives almost the same results in similarity analysis in comparison to more complex 238 group calculation. Finally, it was demonstrated that TSUNAMI-IP utility can play a significant role in the future fast reactor development in Slovakia and in the Visegrad region. Clearly a further Research and Development and strong effort should be carried out in order to receive more complex methodology consisting of more plausible covariance data and related quantities. (authors)

  20. An improved method for statistical analysis of raw accelerator mass spectrometry data

    International Nuclear Information System (INIS)

    Gutjahr, A.; Phillips, F.; Kubik, P.W.; Elmore, D.

    1987-01-01

    Hierarchical statistical analysis is an appropriate method for statistical treatment of raw accelerator mass spectrometry (AMS) data. Using Monte Carlo simulations we show that this method yields more accurate estimates of isotope ratios and analytical uncertainty than the generally used propagation of errors approach. The hierarchical analysis is also useful in design of experiments because it can be used to identify sources of variability. 8 refs., 2 figs

  1. Uncertainties in repository performance from spatial variability of hydraulic conductivities - statistical estimation and stochastic simulation using PROPER

    International Nuclear Information System (INIS)

    Lovius, L.; Norman, S.; Kjellbert, N.

    1990-02-01

    An assessment has been made of the impact of spatial variability on the performance of a KBS-3 type repository. The uncertainties in geohydrologically related performance measures have been investigated using conductivity data from one of the Swedish study sites. The analysis was carried out with the PROPER code and the FSCF10 submodel. (authors)

  2. Statistical analysis of brake squeal noise

    Science.gov (United States)

    Oberst, S.; Lai, J. C. S.

    2011-06-01

    Despite substantial research efforts applied to the prediction of brake squeal noise since the early 20th century, the mechanisms behind its generation are still not fully understood. Squealing brakes are of significant concern to the automobile industry, mainly because of the costs associated with warranty claims. In order to remedy the problems inherent in designing quieter brakes and, therefore, to understand the mechanisms, a design of experiments study, using a noise dynamometer, was performed by a brake system manufacturer to determine the influence of geometrical parameters (namely, the number and location of slots) of brake pads on brake squeal noise. The experimental results were evaluated with a noise index and ranked for warm and cold brake stops. These data are analysed here using statistical descriptors based on population distributions, and a correlation analysis, to gain greater insight into the functional dependency between the time-averaged friction coefficient as the input and the peak sound pressure level data as the output quantity. The correlation analysis between the time-averaged friction coefficient and peak sound pressure data is performed by applying a semblance analysis and a joint recurrence quantification analysis. Linear measures are compared with complexity measures (nonlinear) based on statistics from the underlying joint recurrence plots. Results show that linear measures cannot be used to rank the noise performance of the four test pad configurations. On the other hand, the ranking of the noise performance of the test pad configurations based on the noise index agrees with that based on nonlinear measures: the higher the nonlinearity between the time-averaged friction coefficient and peak sound pressure, the worse the squeal. These results highlight the nonlinear character of brake squeal and indicate the potential of using nonlinear statistical analysis tools to analyse disc brake squeal.

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

    International Nuclear Information System (INIS)

    Brown, J.; Jones, J.A.

    2000-01-01

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

  4. Overview of methods for uncertainty analysis and sensitivity analysis in probabilistic risk assessment

    International Nuclear Information System (INIS)

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

    1985-01-01

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

  5. Stand-alone core sensitivity and uncertainty analysis of ALFRED from Monte Carlo simulations

    International Nuclear Information System (INIS)

    Pérez-Valseca, A.-D.; Espinosa-Paredes, G.; François, J.L.; Vázquez Rodríguez, A.; Martín-del-Campo, C.

    2017-01-01

    Highlights: • Methodology based on Monte Carlo simulation. • Sensitivity analysis of Lead Fast Reactor (LFR). • Uncertainty and regression analysis of LFR. • 10% change in the core inlet flow, the response in thermal power change is 0.58%. • 2.5% change in the inlet lead temperature the response is 1.87% in power. - Abstract: The aim of this paper is the sensitivity and uncertainty analysis of a Lead-Cooled Fast Reactor (LFR) based on Monte Carlo simulation of sizes up to 2000. The methodology developed in this work considers the uncertainty of sensitivities and uncertainty of output variables due to a single-input-variable variation. The Advanced Lead fast Reactor European Demonstrator (ALFRED) is analyzed to determine the behavior of the essential parameters due to effects of mass flow and temperature of liquid lead. The ALFRED core mathematical model developed in this work is fully transient, which takes into account the heat transfer in an annular fuel pellet design, the thermo-fluid in the core, and the neutronic processes, which are modeled with point kinetic with feedback fuel temperature and expansion effects. The sensitivity evaluated in terms of the relative standard deviation (RSD) showed that for 10% change in the core inlet flow, the response in thermal power change is 0.58%, and for 2.5% change in the inlet lead temperature is 1.87%. The regression analysis with mass flow rate as the predictor variable showed statistically valid cubic correlations for neutron flux and linear relationship neutron flux as a function of the lead temperature. No statistically valid correlation was observed for the reactivity as a function of the mass flow rate and for the lead temperature. These correlations are useful for the study, analysis, and design of any LFR.

  6. Analysis of Variance in Statistical Image Processing

    Science.gov (United States)

    Kurz, Ludwik; Hafed Benteftifa, M.

    1997-04-01

    A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

  7. Statistical Analysis of Zebrafish Locomotor Response.

    Science.gov (United States)

    Liu, Yiwen; Carmer, Robert; Zhang, Gaonan; Venkatraman, Prahatha; Brown, Skye Ashton; Pang, Chi-Pui; Zhang, Mingzhi; Ma, Ping; Leung, Yuk Fai

    2015-01-01

    Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling's T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling's T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure.

  8. 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...

  9. Development and application of objective uncertainty measures for nuclear power plant transient analysis[Dissertation 3897

    Energy Technology Data Exchange (ETDEWEB)

    Vinai, P

    2007-10-15

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

  10. Development and application of objective uncertainty measures for nuclear power plant transient analysis

    International Nuclear Information System (INIS)

    Vinai, P.

    2007-10-01

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

  11. Application of intelligence based uncertainty analysis for HLW disposal

    International Nuclear Information System (INIS)

    Kato, Kazuyuki

    2003-01-01

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

  12. A rigorous methodology for development and uncertainty analysis of group contribution based property models

    DEFF Research Database (Denmark)

    Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan

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

  13. On the Statistical Validation of Technical Analysis

    Directory of Open Access Journals (Sweden)

    Rosane Riera Freire

    2007-06-01

    Full Text Available Technical analysis, or charting, aims on visually identifying geometrical patterns in price charts in order to antecipate price "trends". In this paper we revisit the issue of thecnical analysis validation which has been tackled in the literature without taking care for (i the presence of heterogeneity and (ii statistical dependence in the analyzed data - various agglutinated return time series from distinct financial securities. The main purpose here is to address the first cited problem by suggesting a validation methodology that also "homogenizes" the securities according to the finite dimensional probability distribution of their return series. The general steps go through the identification of the stochastic processes for the securities returns, the clustering of similar securities and, finally, the identification of presence, or absence, of informatinal content obtained from those price patterns. We illustrate the proposed methodology with a real data exercise including several securities of the global market. Our investigation shows that there is a statistically significant informational content in two out of three common patterns usually found through technical analysis, namely: triangle, rectangle and head and shoulders.

  14. Statistical trend analysis methods for temporal phenomena

    International Nuclear Information System (INIS)

    Lehtinen, E.; Pulkkinen, U.; Poern, K.

    1997-04-01

    We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods

  15. Statistical trend analysis methods for temporal phenomena

    Energy Technology Data Exchange (ETDEWEB)

    Lehtinen, E.; Pulkkinen, U. [VTT Automation, (Finland); Poern, K. [Poern Consulting, Nykoeping (Sweden)

    1997-04-01

    We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods. 14 refs, 10 figs.

  16. 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

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

    Directory of Open Access Journals (Sweden)

    Chun-Youl Baek

    2017-09-01

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

  18. Biomass Thermogravimetric Analysis: Uncertainty Determination Methodology and Sampling Maps Generation

    Science.gov (United States)

    Pazó, Jose A.; Granada, Enrique; Saavedra, Ángeles; Eguía, Pablo; Collazo, Joaquín

    2010-01-01

    The objective of this study was to develop a methodology for the determination of the maximum sampling error and confidence intervals of thermal properties obtained from thermogravimetric analysis (TG), including moisture, volatile matter, fixed carbon and ash content. The sampling procedure of the TG analysis was of particular interest and was conducted with care. The results of the present study were compared to those of a prompt analysis, and a correlation between the mean values and maximum sampling errors of the methods were not observed. In general, low and acceptable levels of uncertainty and error were obtained, demonstrating that the properties evaluated by TG analysis were representative of the overall fuel composition. The accurate determination of the thermal properties of biomass with precise confidence intervals is of particular interest in energetic biomass applications. PMID:20717532

  19. Validation of statistical models for creep rupture by parametric analysis

    Energy Technology Data Exchange (ETDEWEB)

    Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)

    2012-01-15

    Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).

  20. Sensitivity and uncertainty analysis for Ignalina NPP confinement in case of loss of coolant accident

    International Nuclear Information System (INIS)

    Urbonavicius, E.; Babilas, E.; Rimkevicius, S.

    2003-01-01

    At present the best-estimate approach in the safety analysis of nuclear power plants is widely used around the world. The application of such approach requires to estimate the uncertainty of the calculated results. Various methodologies are applied in order to determine the uncertainty with the required accuracy. One of them is the statistical methodology developed at GRS mbH in Germany and integrated into the SUSA tool, which was applied for the sensitivity and uncertainty analysis of the thermal-hydraulic parameters inside the confinement (Accident Localisation System) of Ignalina NPP with RBMK-1500 reactor in case of Maximum Design Basis Accident (break of 900 mm diameter pipe). Several parameters that could potentially influence the calculated results were selected for the analysis. A set of input data with different initial values of the selected parameters was generated. In order to receive the results with 95 % probability and 95 % accuracy, 100 runs were performed with COCOSYS code developed at GRS mbH. The calculated results were processed with SUSA tool. The performed analysis showed a rather low dispersion of the results and only in the initial period of the accident. Besides, the analysis showed that there is no threat to the building structures of Ignalina NPP confinement in case of the considered accident scenario. (author)

  1. Use of error files in uncertainty analysis and data adjustment

    International Nuclear Information System (INIS)

    Chestnutt, M.M.; McCracken, A.K.; McCracken, A.K.

    1979-01-01

    Some results are given from uncertainty analyses on Pressurized Water Reactor (PWR) and Fast Reactor Theoretical Benchmarks. Upper limit estimates of calculated quantities are shown to be significantly reduced by the use of ENDF/B data covariance files and recently published few-group covariance matrices. Some problems in the analysis of single-material benchmark experiments are discussed with reference to the Winfrith iron benchmark experiment. Particular attention is given to the difficulty of making use of very extensive measurements which are likely to be a feature of this type of experiment. Preliminary results of an adjustment in iron are shown

  2. Uncertainty analysis in calculations of a road accident consequences

    International Nuclear Information System (INIS)

    Bonnefous, S.; Brenot, J.; Hubert, P.

    1995-01-01

    This paper develops a concrete situation witch is the search for an evacuation distance in case of a road accident implying a chlorine tank. The methodological aspect is how implementing uncertainty analysis in deterministic models with random parameters. The study demonstrates a great dispersion in the results. It allows to establish satisfactory decision rules and a hierarchy on parameters witch is useful to define priorities in the search for information and to improve the treatment of these parameters. (authors). 8 refs., 1 fig., 2 tabs

  3. Compliance strategy for statistically based neutron overpower protection safety analysis methodology

    International Nuclear Information System (INIS)

    Holliday, E.; Phan, B.; Nainer, O.

    2009-01-01

    The methodology employed in the safety analysis of the slow Loss of Regulation (LOR) event in the OPG and Bruce Power CANDU reactors, referred to as Neutron Overpower Protection (NOP) analysis, is a statistically based methodology. Further enhancement to this methodology includes the use of Extreme Value Statistics (EVS) for the explicit treatment of aleatory and epistemic uncertainties, and probabilistic weighting of the initial core states. A key aspect of this enhanced NOP methodology is to demonstrate adherence, or compliance, with the analysis basis. This paper outlines a compliance strategy capable of accounting for the statistical nature of the enhanced NOP methodology. (author)

  4. Additional challenges for uncertainty analysis in river engineering

    Science.gov (United States)

    Berends, Koen; Warmink, Jord; Hulscher, Suzanne

    2016-04-01

    the proposed intervention. The implicit assumption underlying such analysis is that both models are commensurable. We hypothesize that they are commensurable only to a certain extent. In an idealised study we have demonstrated that prediction performance loss should be expected with increasingly large engineering works. When accounting for parametric uncertainty of floodplain roughness in model identification, we see uncertainty bounds for predicted effects of interventions increase with increasing intervention scale. Calibration of these types of models therefore seems to have a shelf-life, beyond which calibration does not longer improves prediction. Therefore a qualification scheme for model use is required that can be linked to model validity. In this study, we characterize model use along three dimensions: extrapolation (using the model with different external drivers), extension (using the model for different output or indicators) and modification (using modified models). Such use of models is expected to have implications for the applicability of surrogating modelling for efficient uncertainty analysis as well, which is recommended for future research. Warmink, J. J.; Straatsma, M. W.; Huthoff, F.; Booij, M. J. & Hulscher, S. J. M. H. 2013. Uncertainty of design water levels due to combined bed form and vegetation roughness in the Dutch river Waal. Journal of Flood Risk Management 6, 302-318 . DOI: 10.1111/jfr3.12014

  5. An evaluation of the uncertainty of extreme events statistics at the WMO/CIMO Lead Centre on precipitation intensity

    Science.gov (United States)

    Colli, M.; Lanza, L. G.; La Barbera, P.

    2012-12-01

    the uncertainty budget of modern rain gauges is also shown . The analysis proceeds with the laboratory simulation of the annual maximum rainfall events recorded for different durations at the Villa Cambiaso meteo-station (University of Genova) over the last two decades. Results are reported and discussed in a comparative form involving the derived extreme events statistics. REFERENCES La Barbera P., Lanza L.G. and Stagi L. (2002). Influence of systematic mechanical errors of tipping-bucket rain gauges on the statistics of rainfall extremes. Water Sci. Techn., 45(2), 1-9. Colli M., Lanza L.G., and Chan P.W. (2011). Co-located tipping-bucket and optical drop counter RI measurements and a simulated correction algorithm, Atmos. Res., doi:10.1016/j.atmosres.2011.07.018 Colli M., Lanza L.G., La Barbera P. (2012). Weighing gauges measurement errors and the design rainfall for urban scale applications. 9th International workshop on precipitation in urban areas. St.Moritz, Switzerland, 6-9 December 2012 Lanza L.G. and Vuerich E. (2009). The WMO Field Intercomparison of Rain Intensity Gauges. Atmos. Res., 94, 534-543.

  6. Statistical analysis of solar proton events

    Directory of Open Access Journals (Sweden)

    V. Kurt

    2004-06-01

    Full Text Available A new catalogue of 253 solar proton events (SPEs with energy >10MeV and peak intensity >10 protons/cm2.s.sr (pfu at the Earth's orbit for three complete 11-year solar cycles (1970-2002 is given. A statistical analysis of this data set of SPEs and their associated flares that occurred during this time period is presented. It is outlined that 231 of these proton events are flare related and only 22 of them are not associated with Ha flares. It is also noteworthy that 42 of these events are registered as Ground Level Enhancements (GLEs in neutron monitors. The longitudinal distribution of the associated flares shows that a great number of these events are connected with west flares. This analysis enables one to understand the long-term dependence of the SPEs and the related flare characteristics on the solar cycle which are useful for space weather prediction.

  7. Recent advances in statistical energy analysis

    Science.gov (United States)

    Heron, K. H.

    1992-01-01

    Statistical Energy Analysis (SEA) has traditionally been developed using modal summation and averaging approach, and has led to the need for many restrictive SEA assumptions. The assumption of 'weak coupling' is particularly unacceptable when attempts are made to apply SEA to structural coupling. It is now believed that this assumption is more a function of the modal formulation rather than a necessary formulation of SEA. The present analysis ignores this restriction and describes a wave approach to the calculation of plate-plate coupling loss factors. Predictions based on this method are compared with results obtained from experiments using point excitation on one side of an irregular six-sided box structure. Conclusions show that the use and calculation of infinite transmission coefficients is the way forward for the development of a purely predictive SEA code.

  8. STATISTICS, Program System for Statistical Analysis of Experimental Data

    International Nuclear Information System (INIS)

    Helmreich, F.

    1991-01-01

    1 - Description of problem or function: The package is composed of 83 routines, the most important of which are the following: BINDTR: Binomial distribution; HYPDTR: Hypergeometric distribution; POIDTR: Poisson distribution; GAMDTR: Gamma distribution; BETADTR: Beta-1 and Beta-2 distributions; NORDTR: Normal distribution; CHIDTR: Chi-square distribution; STUDTR : Distribution of 'Student's T'; FISDTR: Distribution of F; EXPDTR: Exponential distribution; WEIDTR: Weibull distribution; FRAKTIL: Calculation of the fractiles of the normal, chi-square, Student's, and F distributions; VARVGL: Test for equality of variance for several sample observations; ANPAST: Kolmogorov-Smirnov test and chi-square test of goodness of fit; MULIRE: Multiple linear regression analysis for a dependent variable and a set of independent variables; STPRG: Performs a stepwise multiple linear regression analysis for a dependent variable and a set of independent variables. At each step, the variable entered into the regression equation is the one which has the greatest amount of variance between it and the dependent variable. Any independent variable can be forced into or deleted from the regression equation, irrespective of its contribution to the equation. LTEST: Tests the hypotheses of linearity of the data. SPRANK: Calculates the Spearman rank correlation coefficient. 2 - Method of solution: VARVGL: The Bartlett's Test, the Cochran's Test and the Hartley's Test are performed in the program. MULIRE: The Gauss-Jordan method is used in the solution of the normal equations. STPRG: The abbreviated Doolittle method is used to (1) determine variables to enter into the regression, and (2) complete regression coefficient calculation. 3 - Restrictions on the complexity of the problem: VARVGL: The Hartley's Test is only performed if the sample observations are all of the same size

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

    International Nuclear Information System (INIS)

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

    1997-07-01

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

  10. Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models.

    Science.gov (United States)

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

    2018-01-01

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

  11. 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

  12. A comparison of uncertainty analysis methods using a groundwater flow model

    International Nuclear Information System (INIS)

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

    1988-06-01

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

  13. Sensitivity/uncertainty analysis for the Hiroshima dosimetry reevaluation effort

    International Nuclear Information System (INIS)

    Broadhead, B.L.; Lillie, R.A.; Pace, J.V. III; Cacuci, D.G.

    1987-01-01

    Uncertainty estimates and cross correlations by range/survivor location have been obtained for the free-in-air (FIA) tissue kerma for the Hiroshima atomic event. These uncertainties in the FIA kerma include contributions due to various modeling parameters and the basic cross section data and are given at three ground ranges, 700, 1000 and 1500 m. The estimated uncertainties are nearly constant over the given ground ranges and are approximately 27% for the prompt neutron kerma and secondary gamma kerma and 35% for the prompt gamma kerma. The total kerma uncertainty is dominated by the secondary gamma kerma uncertainties which are in turn largely due to the modeling parameter uncertainties

  14. Optimism in the face of uncertainty supported by a statistically-designed multi-armed bandit algorithm.

    Science.gov (United States)

    Kamiura, Moto; Sano, Kohei

    2017-10-01

    The principle of optimism in the face of uncertainty is known as a heuristic in sequential decision-making problems. Overtaking method based on this principle is an effective algorithm to solve multi-armed bandit problems. It was defined by a set of some heuristic patterns of the formulation in the previous study. The objective of the present paper is to redefine the value functions of Overtaking method and to unify the formulation of them. The unified Overtaking method is associated with upper bounds of confidence intervals of expected rewards on statistics. The unification of the formulation enhances the universality of Overtaking method. Consequently we newly obtain Overtaking method for the exponentially distributed rewards, numerically analyze it, and show that it outperforms UCB algorithm on average. The present study suggests that the principle of optimism in the face of uncertainty should be regarded as the statistics-based consequence of the law of large numbers for the sample mean of rewards and estimation of upper bounds of expected rewards, rather than as a heuristic, in the context of multi-armed bandit problems. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Rohmer, Jeremy; Verdel, Thierry

    2017-04-01

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

  16. Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis

    International Nuclear Information System (INIS)

    Abdel-Khalik, Hany S.; Bang, Youngsuk; Wang, Congjian

    2013-01-01

    Highlights: ► We overview the state-of-the-art in uncertainty quantification and sensitivity analysis. ► We overview new developments in above areas using hybrid methods. ► We give a tutorial introduction to above areas and the new developments. ► Hybrid methods address the explosion in dimensionality in nonlinear models. ► Representative numerical experiments are given. -- Abstract: The role of modeling and simulation has been heavily promoted in recent years to improve understanding of complex engineering systems. To realize the benefits of modeling and simulation, concerted efforts in the areas of uncertainty quantification and sensitivity analysis are required. The manuscript intends to serve as a pedagogical presentation of the material to young researchers and practitioners with little background on the subjects. We believe this is important as the role of these subjects is expected to be integral to the design, safety, and operation of existing as well as next generation reactors. In addition to covering the basics, an overview of the current state-of-the-art will be given with particular emphasis on the challenges pertaining to nuclear reactor modeling. The second objective will focus on presenting our own development of hybrid subspace methods intended to address the explosion in the computational overhead required when handling real-world complex engineering systems.

  17. Procedures for uncertainty and sensitivity analysis in repository performance assessment

    International Nuclear Information System (INIS)

    Poern, K.; Aakerlund, O.

    1985-10-01

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

  18. Statistical analysis of tourism destination competitiveness

    Directory of Open Access Journals (Sweden)

    Attilio Gardini

    2013-05-01

    Full Text Available The growing relevance of tourism industry for modern advanced economies has increased the interest among researchers and policy makers in the statistical analysis of destination competitiveness. In this paper we outline a new model of destination competitiveness based on sound theoretical grounds and we develop a statistical test of the model on sample data based on Italian tourist destination decisions and choices. Our model focuses on the tourism decision process which starts from the demand schedule for holidays and ends with the choice of a specific holiday destination. The demand schedule is a function of individual preferences and of destination positioning, while the final decision is a function of the initial demand schedule and the information concerning services for accommodation and recreation in the selected destinations. Moreover, we extend previous studies that focused on image or attributes (such as climate and scenery by paying more attention to the services for accommodation and recreation in the holiday destinations. We test the proposed model using empirical data collected from a sample of 1.200 Italian tourists interviewed in 2007 (October - December. Data analysis shows that the selection probability for the destination included in the consideration set is not proportional to the share of inclusion because the share of inclusion is determined by the brand image, while the selection of the effective holiday destination is influenced by the real supply conditions. The analysis of Italian tourists preferences underline the existence of a latent demand for foreign holidays which points out a risk of market share reduction for Italian tourism system in the global market. We also find a snow ball effect which helps the most popular destinations, mainly in the northern Italian regions.

  19. A retrospective dosimetry method and its uncertainty analysis

    International Nuclear Information System (INIS)

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

    2000-01-01

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

  20. Free Vibration Analysis of Composite Plates via Refined Theories Accounting for Uncertainties

    Directory of Open Access Journals (Sweden)

    G. Giunta

    2011-01-01

    Full Text Available The free vibration analysis of composite thin and relatively thick plates accounting for uncertainty is addressed in this work. Classical and refined two-dimensional models derived via Carrera's Unified Formulation (CUF are considered. Material properties and geometrical parameters are supposed to be random. The fundamental frequency related to the first bending eigenmode is stochastically described in terms of the mean value, the standard deviation, the related confidence intervals and the cumulative distribution function. The Monte Carlo Method is employed to account for uncertainty. Cross-ply, simply supported, orthotropic plates are accounted for. Symmetric and anti-symmetric lay-ups are investigated. Displacements based and mixed two-dimensional theories are adopted. Equivalent single layer and layer wise approaches are considered. A Navier type solution is assumed. The conducted analyses have shown that for the considered cases, the fundamental natural frequency is not very sensitive to the uncertainty in the material parameters, while uncertainty in the geometrical parameters should be accounted for. In the case of thin plates, all the considered models yield statistically matching results. For relatively thick plates, the difference in the mean value of the natural frequency is due to the different number of degrees of freedom in the model.

  1. Application of a new methodology to evaluate Dnb limits based on statistical propagation of uncertainties

    International Nuclear Information System (INIS)

    Machado, Marcio Dornellas

    1998-09-01

    One of the most important thermalhydraulics safety parameters is the DNBR (Departure from Nucleate Boiling Ratio). The current methodology in use at Eletronuclear to determine DNBR is extremely conservative and may result in penalties to the reactor power due to an increase plugging level of steam generator tubes. This work uses a new methodology to evaluate DNBR, named mini-RTDP. The standard methodology (STDP) currently in use establishes a limit design value which cannot be surpassed. This limit value is determined taking into account the uncertainties of the empirical correlation used in COBRA IIC/MIT code, modified to Angra 1 conditions. The correlation used is the Westinghouse's W-3 and the minimum DNBR (MDBR) value cannot be less than 1.3. The new methodology reduces the excessive level of conservatism associated with the parameters used in the DNBR calculation, which take most unfavorable values in the STDP methodology, by using their best estimate values. The final goal is to obtain a new DNBR design limit which will provide a margin gain due to more realistic parameters values used in the methodology. (author)

  2. Multivariate statistical analysis of wildfires in Portugal

    Science.gov (United States)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  3. Uncertainty analysis methods for quantification of source terms using a large computer code

    International Nuclear Information System (INIS)

    Han, Seok Jung

    1997-02-01

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

  4. Uncertainty propagation in probabilistic safety analysis of nuclear power plants

    International Nuclear Information System (INIS)

    Fleming, P.V.

    1981-09-01

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

  5. FADTTS: functional analysis of diffusion tensor tract statistics.

    Science.gov (United States)

    Zhu, Hongtu; Kong, Linglong; Li, Runze; Styner, Martin; Gerig, Guido; Lin, Weili; Gilmore, John H

    2011-06-01

    The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure. Copyright © 2011 Elsevier Inc. All rights reserved.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-04-27

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

  7. A statistical analysis of electrical cerebral activity

    International Nuclear Information System (INIS)

    Bassant, Marie-Helene

    1971-01-01

    The aim of this work was to study the statistical properties of the amplitude of the electroencephalographic signal. The experimental method is described (implantation of electrodes, acquisition and treatment of data). The program of the mathematical analysis is given (calculation of probability density functions, study of stationarity) and the validity of the tests discussed. The results concerned ten rabbits. Trips of EEG were sampled during 40 s. with very short intervals (500 μs). The probability density functions established for different brain structures (especially the dorsal hippocampus) and areas, were compared during sleep, arousal and visual stimulus. Using a Χ 2 test, it was found that the Gaussian distribution assumption was rejected in 96.7 per cent of the cases. For a given physiological state, there was no mathematical reason to reject the assumption of stationarity (in 96 per cent of the cases). (author) [fr

  8. Statistical analysis of ultrasonic measurements in concrete

    Science.gov (United States)

    Chiang, Chih-Hung; Chen, Po-Chih

    2002-05-01

    Stress wave techniques such as measurements of ultrasonic pulse velocity are often used to evaluate concrete quality in structures. For proper interpretation of measurement results, the dependence of pulse transit time on the average acoustic impedance and the material homogeneity along the sound path need to be examined. Semi-direct measurement of pulse velocity could be more convenient than through transmission measurement. It is not necessary to assess both sides of concrete floors or walls. A novel measurement scheme is proposed and verified based on statistical analysis. It is shown that Semi-direct measurements are very effective for gathering large amount of pulse velocity data from concrete reference specimens. The variability of measurements is comparable with that reported by American Concrete Institute using either break-off or pullout tests.

  9. Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

    Science.gov (United States)

    Chen, Mingshi; Senay, Gabriel B.; Singh, Ramesh K.; Verdin, James P.

    2016-01-01

    Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within

  10. Probabilistic accident consequence uncertainty analysis: Food chain uncertainty assessment. Volume 1: Main report

    Energy Technology Data Exchange (ETDEWEB)

    Brown, J. [National Radiological Protection Board (United Kingdom); Goossens, L.H.J.; Kraan, B.C.P. [Delft Univ. of Technology (Netherlands)] [and others

    1997-06-01

    This volume is the first of a two-volume document that summarizes a joint project conducted by the US Nuclear Regulatory Commission and the European Commission to assess uncertainties in the MACCS and COSYMA probabilistic accident consequence codes. These codes were developed primarily for estimating the risks presented by nuclear reactors based on postulated frequencies and magnitudes of potential accidents. This document reports on an ongoing project to assess uncertainty in the MACCS and COSYMA calculations for the offsite consequences of radionuclide releases by hypothetical nuclear power plant accidents. A panel of sixteen experts was formed to compile credible and traceable uncertainty distributions for food chain variables that affect calculations of offsite consequences. The expert judgment elicitation procedure and its outcomes are described in these volumes. Other panels were formed to consider uncertainty in other aspects of the codes. Their results are described in companion reports. Volume 1 contains background information and a complete description of the joint consequence uncertainty study. Volume 2 contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures for both panels, (3) the rationales and results for the panels on soil and plant transfer and animal transfer, (4) short biographies of the experts, and (5) the aggregated results of their responses.

  11. Probabilistic accident consequence uncertainty analysis: Food chain uncertainty assessment. Volume 1: Main report

    International Nuclear Information System (INIS)

    Brown, J.; Goossens, L.H.J.; Kraan, B.C.P.

    1997-06-01

    This volume is the first of a two-volume document that summarizes a joint project conducted by the US Nuclear Regulatory Commission and the European Commission to assess uncertainties in the MACCS and COSYMA probabilistic accident consequence codes. These codes were developed primarily for estimating the risks presented by nuclear reactors based on postulated frequencies and magnitudes of potential accidents. This document reports on an ongoing project to assess uncertainty in the MACCS and COSYMA calculations for the offsite consequences of radionuclide releases by hypothetical nuclear power plant accidents. A panel of sixteen experts was formed to compile credible and traceable uncertainty distributions for food chain variables that affect calculations of offsite consequences. The expert judgment elicitation procedure and its outcomes are described in these volumes. Other panels were formed to consider uncertainty in other aspects of the codes. Their results are described in companion reports. Volume 1 contains background information and a complete description of the joint consequence uncertainty study. Volume 2 contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures for both panels, (3) the rationales and results for the panels on soil and plant transfer and animal transfer, (4) short biographies of the experts, and (5) the aggregated results of their responses

  12. Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

    Science.gov (United States)

    Feng, Jinchao; Lansford, Joshua; Mironenko, Alexander; Pourkargar, Davood Babaei; Vlachos, Dionisios G.; Katsoulakis, Markos A.

    2018-03-01

    We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data). The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

  13. Non-parametric correlative uncertainty quantification and sensitivity analysis: Application to a Langmuir bimolecular adsorption model

    Directory of Open Access Journals (Sweden)

    Jinchao Feng

    2018-03-01

    Full Text Available We propose non-parametric methods for both local and global sensitivity analysis of chemical reaction models with correlated parameter dependencies. The developed mathematical and statistical tools are applied to a benchmark Langmuir competitive adsorption model on a close packed platinum surface, whose parameters, estimated from quantum-scale computations, are correlated and are limited in size (small data. The proposed mathematical methodology employs gradient-based methods to compute sensitivity indices. We observe that ranking influential parameters depends critically on whether or not correlations between parameters are taken into account. The impact of uncertainty in the correlation and the necessity of the proposed non-parametric perspective are demonstrated.

  14. Geoengineering to Avoid Overshoot: An Analysis of Uncertainty

    Science.gov (United States)

    Tanaka, Katsumasa; Cho, Cheolhung; Krey, Volker; Patt, Anthony; Rafaj, Peter; Rao-Skirbekk, Shilpa; Wagner, Fabian

    2010-05-01

    ., 2009) is employed to calculate climate responses including associated uncertainty and to estimate geoengineering profiles to cap the warming at 2°C since preindustrial. The inversion setup for the model ACC2 is used to estimate the uncertain parameters (e.g. climate sensitivity) against associated historical observations (e.g. global-mean surface air temperature). Our preliminary results show that under climate and scenario uncertainties, a geoengineering intervention to avoid an overshoot would be with medium intensity in the latter half of this century (≈ 1 Mt. Pinatubo eruption every 4 years in terms of stratospheric sulfur injections). The start year of geoengineering intervention does not significantly influence the long-term geoengineering profile. However, a geoengineering intervention of the medium intensity could bring about substantial environmental side effects such as the destruction of stratospheric ozone. Our results point to the necessity to pursue persistently mainstream mitigation efforts. 2) Pollution Abatement and Geoengineering The second study examines the potential of geoengineering combined with air clean policy. A drastic air pollution abatement might result in an abrupt warming because it would suddenly remove the tropospheric aerosols which partly offset the background global warming (e.g. Andreae et al, 2005, Raddatz and Tanaka, 2010). This study investigates the magnitude of unrealized warming under a range of policy assumptions and associated uncertainties. Then the profile of geoengineering is estimated to suppress the warming that would be accompanied by clean air policy. This study is the first attempt to explore uncertainty in the warming caused by clean air policy - Kloster et al. (2009), which assess regional changes in climate and hydrological cycle, has not however included associated uncertainties in the analysis. A variety of policy assumptions will be devised to represent various degrees of air pollution abatement. These

  15. Uncertainty analysis comes to integrated assessment models for climate change…and conversely

    NARCIS (Netherlands)

    Cooke, R.M.

    2012-01-01

    This article traces the development of uncertainty analysis through three generations punctuated by large methodology investments in the nuclear sector. Driven by a very high perceived legitimation burden, these investments aimed at strengthening the scientific basis of uncertainty quantification.

  16. Analysis and evaluation of regulatory uncertainties in 10 CFR 60 subparts B and E

    International Nuclear Information System (INIS)

    Weiner, R.F.; Patrick, W.C.

    1990-01-01

    This paper presents an attribute analysis scheme for prioritizing the resolution of regulatory uncertainties. Attributes are presented which assist in identifying the need for timeliness and durability of the resolution of an uncertainty

  17. Dynamic Simulation, Sensitivity and Uncertainty Analysis of a Demonstration Scale Lignocellulosic Enzymatic Hydrolysis Process

    DEFF Research Database (Denmark)

    Prunescu, Remus Mihail; Sin, Gürkan

    2014-01-01

    This study presents the uncertainty and sensitivity analysis of a lignocellulosic enzymatic hydrolysis model considering both model and feed parameters as sources of uncertainty. The dynamic model is parametrized for accommodating various types of biomass, and different enzymatic complexes...

  18. Measurements and their uncertainties a practical guide to modern error analysis

    CERN Document Server

    Hughes, Ifan G

    2010-01-01

    This hands-on guide is primarily intended to be used in undergraduate laboratories in the physical sciences and engineering. It assumes no prior knowledge of statistics. It introduces the necessary concepts where needed, with key points illustrated with worked examples and graphic illustrations. In contrast to traditional mathematical treatments it uses a combination of spreadsheet and calculus-based approaches, suitable as a quick and easy on-the-spot reference. The emphasisthroughout is on practical strategies to be adopted in the laboratory. Error analysis is introduced at a level accessible to school leavers, and carried through to research level. Error calculation and propagation is presented though a series of rules-of-thumb, look-up tables and approaches amenable to computer analysis. The general approach uses the chi-square statistic extensively. Particular attention is given to hypothesis testing and extraction of parameters and their uncertainties by fitting mathematical models to experimental data....

  19. 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.

  20. Methods and computer codes for probabilistic sensitivity and uncertainty analysis

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1985-01-01

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

  1. Quantification of Uncertainty in the Flood Frequency Analysis

    Science.gov (United States)

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

    2017-12-01

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

  2. 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.

  3. Statistical Analysis of Data for Timber Strengths

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Hoffmeyer, P.

    Statistical analyses are performed for material strength parameters from approximately 6700 specimens of structural timber. Non-parametric statistical analyses and fits to the following distributions types have been investigated: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull...

  4. On two methods of statistical image analysis

    NARCIS (Netherlands)

    Missimer, J; Knorr, U; Maguire, RP; Herzog, H; Seitz, RJ; Tellman, L; Leenders, K.L.

    1999-01-01

    The computerized brain atlas (CBA) and statistical parametric mapping (SPM) are two procedures for voxel-based statistical evaluation of PET activation studies. Each includes spatial standardization of image volumes, computation of a statistic, and evaluation of its significance. In addition,

  5. Application of descriptive statistics in analysis of experimental data

    OpenAIRE

    Mirilović Milorad; Pejin Ivana

    2008-01-01

    Statistics today represent a group of scientific methods for the quantitative and qualitative investigation of variations in mass appearances. In fact, statistics present a group of methods that are used for the accumulation, analysis, presentation and interpretation of data necessary for reaching certain conclusions. Statistical analysis is divided into descriptive statistical analysis and inferential statistics. The values which represent the results of an experiment, and which are the subj...

  6. Statistical analysis in MSW collection performance assessment.

    Science.gov (United States)

    Teixeira, Carlos Afonso; Avelino, Catarina; Ferreira, Fátima; Bentes, Isabel

    2014-09-01

    The increase of Municipal Solid Waste (MSW) generated over the last years forces waste managers pursuing more effective collection schemes, technically viable, environmentally effective and economically sustainable. The assessment of MSW services using performance indicators plays a crucial role for improving service quality. In this work, we focus on the relevance of regular system monitoring as a service assessment tool. In particular, we select and test a core-set of MSW collection performance indicators (effective collection distance, effective collection time and effective fuel consumption) that highlights collection system strengths and weaknesses and supports pro-active management decision-making and strategic planning. A statistical analysis was conducted with data collected in mixed collection system of Oporto Municipality, Portugal, during one year, a week per month. This analysis provides collection circuits' operational assessment and supports effective short-term municipality collection strategies at the level of, e.g., collection frequency and timetables, and type of containers. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Statistics Analysis Measures Painting of Cooling Tower

    Directory of Open Access Journals (Sweden)

    A. Zacharopoulou

    2013-01-01

    Full Text Available This study refers to the cooling tower of Megalopolis (construction 1975 and protection from corrosive environment. The maintenance of the cooling tower took place in 2008. The cooling tower was badly damaged from corrosion of reinforcement. The parabolic cooling towers (factory of electrical power are a typical example of construction, which has a special aggressive environment. The protection of cooling towers is usually achieved through organic coatings. Because of the different environmental impacts on the internal and external side of the cooling tower, a different system of paint application is required. The present study refers to the damages caused by corrosion process. The corrosive environments, the application of this painting, the quality control process, the measures and statistics analysis, and the results were discussed in this study. In the process of quality control the following measurements were taken into consideration: (1 examination of the adhesion with the cross-cut test, (2 examination of the film thickness, and (3 controlling of the pull-off resistance for concrete substrates and paintings. Finally, this study refers to the correlations of measurements, analysis of failures in relation to the quality of repair, and rehabilitation of the cooling tower. Also this study made a first attempt to apply the specific corrosion inhibitors in such a large structure.

  8. Deterministic sensitivity and uncertainty analysis for large-scale computer models

    International Nuclear Information System (INIS)

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

    1988-01-01

    The fields of sensitivity and uncertainty analysis have traditionally been dominated by statistical techniques when large-scale modeling codes are being analyzed. These methods are able to estimate sensitivities, generate response surfaces, and estimate response probability distributions given the input parameter probability distributions. Because the statistical methods are computationally costly, they are usually applied only to problems with relatively small parameter sets. Deterministic methods, on the other hand, are very efficient and can handle large data sets, but generally require simpler models because of the considerable programming effort required for their implementation. The first part of this paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. This second part of the paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. This paper is applicable to low-level radioactive waste disposal system performance assessment

  9. Reduction of uncertainties in probabilistic seismic hazard analysis

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Jeong Moon; Choun, Young Sun; Choi, In Kil [Korea Atomic Energy Research Institute, Taejon (Korea)

    1999-02-01

    An integrated research for the reduction of conservatism and uncertainties in PSHA in Korea was performed. The research consisted of five technical task areas as follows; Task 1: Earthquake Catalog Development for PSHA. Task 2: Evaluation of Seismicity and Tectonics of the Korea Region. Task 3: Development of a Ground Motion Relationships. Task 4: Improvement of PSHA Modelling Methodology. Task 5: Development of Seismic Source Interpretations for the region of Korea for Inputs to PSHA. A series of tests on an ancient wooden house and an analysis on medium size earthquake in Korea were performed intensively. Signification improvement, especially in the estimation of historical earthquake, ground motion attenuation, and seismic source interpretations, were made through this study. 314 refs., 180 figs., 54 tabs. (Author)

  10. Computational Methods for Sensitivity and Uncertainty Analysis in Criticality Safety

    International Nuclear Information System (INIS)

    Broadhead, B.L.; Childs, R.L.; Rearden, B.T.

    1999-01-01

    Interest in the sensitivity methods that were developed and widely used in the 1970s (the FORSS methodology at ORNL among others) has increased recently as a result of potential use in the area of criticality safety data validation procedures to define computational bias, uncertainties and area(s) of applicability. Functional forms of the resulting sensitivity coefficients can be used as formal parameters in the determination of applicability of benchmark experiments to their corresponding industrial application areas. In order for these techniques to be generally useful to the criticality safety practitioner, the procedures governing their use had to be updated and simplified. This paper will describe the resulting sensitivity analysis tools that have been generated for potential use by the criticality safety community

  11. Reducing the uncertainty in robotic machining by modal analysis

    Science.gov (United States)

    Alberdi, Iñigo; Pelegay, Jose Angel; Arrazola, Pedro Jose; Ørskov, Klaus Bonde

    2017-10-01

    The use of industrial robots for machining could lead to high cost and energy savings for the manufacturing industry. Machining robots offer several advantages respect to CNC machines such as flexibility, wide working space, adaptability and relatively low cost. However, there are some drawbacks that are preventing a widespread adoption of robotic solutions namely lower stiffness, vibration/chatter problems and lower accuracy and repeatability. Normally due to these issues conservative cutting parameters are chosen, resulting in a low material removal rate (MRR). In this article, an example of a modal analysis of a robot is presented. For that purpose the Tap-testing technology is introduced, which aims at maximizing productivity, reducing the uncertainty in the selection of cutting parameters and offering a stable process free from chatter vibrations.

  12. 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)

  13. Parameter uncertainty effects on variance-based sensitivity analysis

    International Nuclear Information System (INIS)

    Yu, W.; Harris, T.J.

    2009-01-01

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

  14. Uncertainty importance analysis using parametric moment ratio functions.

    Science.gov (United States)

    Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

    2014-02-01

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

  15. Transit safety & security statistics & analysis 2002 annual report (formerly SAMIS)

    Science.gov (United States)

    2004-12-01

    The Transit Safety & Security Statistics & Analysis 2002 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

  16. Transit safety & security statistics & analysis 2003 annual report (formerly SAMIS)

    Science.gov (United States)

    2005-12-01

    The Transit Safety & Security Statistics & Analysis 2003 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

  17. Statistical analysis of long term spatial and temporal trends of ...

    Indian Academy of Sciences (India)

    Statistical analysis of long term spatial and temporal trends of temperature ... CGCM3; HadCM3; modified Mann–Kendall test; statistical analysis; Sutlej basin. ... Water Resources Systems Division, National Institute of Hydrology, Roorkee 247 ...

  18. The Uncertainty of Measurement Results

    Energy Technology Data Exchange (ETDEWEB)

    Ambrus, A. [Hungarian Food Safety Office, Budapest (Hungary)

    2009-07-15

    Factors affecting the uncertainty of measurement are explained, basic statistical formulae given, and the theoretical concept explained in the context of pesticide formulation analysis. Practical guidance is provided on how to determine individual uncertainty components within an analytical procedure. An extended and comprehensive table containing the relevant mathematical/statistical expressions elucidates the relevant underlying principles. Appendix I provides a practical elaborated example on measurement uncertainty estimation, above all utilizing experimental repeatability and reproducibility laboratory data. (author)

  19. Statistical approach to partial equilibrium analysis

    Science.gov (United States)

    Wang, Yougui; Stanley, H. E.

    2009-04-01

    A statistical approach to market equilibrium and efficiency analysis is proposed in this paper. One factor that governs the exchange decisions of traders in a market, named willingness price, is highlighted and constitutes the whole theory. The supply and demand functions are formulated as the distributions of corresponding willing exchange over the willingness price. The laws of supply and demand can be derived directly from these distributions. The characteristics of excess demand function are analyzed and the necessary conditions for the existence and uniqueness of equilibrium point of the market are specified. The rationing rates of buyers and sellers are introduced to describe the ratio of realized exchange to willing exchange, and their dependence on the market price is studied in the cases of shortage and surplus. The realized market surplus, which is the criterion of market efficiency, can be written as a function of the distributions of willing exchange and the rationing rates. With this approach we can strictly prove that a market is efficient in the state of equilibrium.

  20. Analysis of Variance: What Is Your Statistical Software Actually Doing?

    Science.gov (United States)

    Li, Jian; Lomax, Richard G.

    2011-01-01

    Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each…

  1. 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

  2. Quality in environmental science for policy: assessing uncertainty as a component of policy analysis

    NARCIS (Netherlands)

    Maxim, L.; van der Sluijs, J.P.

    2011-01-01

    The sheer number of attempts to define and classify uncertainty reveals an awareness of its importance in environmental science for policy, though the nature of uncertainty is often misunderstood. The interdisciplinary field of uncertainty analysis is unstable; there are currently several incomplete

  3. Quantifying reactor safety margins: Application of CSAU [Code Scalability, Applicability and Uncertainty] methodology to LBLOCA: Part 3, Assessment and ranging of parameters for the uncertainty analysis of LBLOCA codes

    International Nuclear Information System (INIS)

    Wulff, W.; Boyack, B.E.; Duffey, R.B.

    1988-01-01

    Comparisons of results from TRAC-PF1/MOD1 code calculations with measurements from Separate Effects Tests, and published experimental data for modeling parameters have been used to determine the uncertainty ranges of code input and modeling parameters which dominate the uncertainty in predicting the Peak Clad Temperature for a postulated Large Break Loss of Coolant Accident (LBLOCA) in a four-loop Westinghouse Pressurized Water Reactor. The uncertainty ranges are used for a detailed statistical analysis to calculate the probability distribution function for the TRAC code-predicted Peak Clad Temperature, as is described in an attendant paper. Measurements from Separate Effects Tests and Integral Effects Tests have been compared with results from corresponding TRAC-PF1/MOD1 code calculations to determine globally the total uncertainty in predicting the Peak Clad Temperature for LBLOCAs. This determination is in support of the detailed statistical analysis mentioned above. The analyses presented here account for uncertainties in input parameters, in modeling and scaling, in computing and in measurements. The analyses are an important part of the work needed to implement the Code Scalability, Applicability and Uncertainty (CSAU) methodology. CSAU is needed to determine the suitability of a computer code for reactor safety analyses and the uncertainty in computer predictions. The results presented here are used to estimate the safety margin of a particular nuclear reactor power plant for a postulated accident. 25 refs., 10 figs., 11 tabs

  4. Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh

    Science.gov (United States)

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

    2014-12-01

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

  5. Scalability on LHS (Latin Hypercube Sampling) samples for use in uncertainty analysis of large numerical models

    International Nuclear Information System (INIS)

    Baron, Jorge H.; Nunez Mac Leod, J.E.

    2000-01-01

    The present paper deals with the utilization of advanced sampling statistical methods to perform uncertainty and sensitivity analysis on numerical models. Such models may represent physical phenomena, logical structures (such as boolean expressions) or other systems, and various of their intrinsic parameters and/or input variables are usually treated as random variables simultaneously. In the present paper a simple method to scale-up Latin Hypercube Sampling (LHS) samples is presented, starting with a small sample and duplicating its size at each step, making it possible to use the already run numerical model results with the smaller sample. The method does not distort the statistical properties of the random variables and does not add any bias to the samples. The results is a significant reduction in numerical models running time can be achieved (by re-using the previously run samples), keeping all the advantages of LHS, until an acceptable representation level is achieved in the output variables. (author)

  6. Information Synthesis in Uncertainty Studies: Application to the Analysis of the BEMUSE Results

    International Nuclear Information System (INIS)

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

    2013-01-01

    To demonstrate that the nuclear power plants are designed to respond safely at numerous postulated accidents computer codes are used. The models of these computer codes are an approximation of the real physical behaviour occurring during an accident. Moreover the data used to run these codes are also known with a limited accuracy. Therefore the code predictions are not exact but uncertain. To deal with these uncertainties, 'best estimate' codes with 'best estimate' input data are used to obtain a best estimate calculation and it is necessary to derive the uncertainty associated to their estimations. For this reason, regulatory authorities demand in particular to technical safety organization such as the French Institut de Radioprotection et de Surete Nucleaire (IRSN) to provide results taking into account all the uncertainty sources to assess safety quantities are below critical values. Uncertainty analysis can be seen as a problem of information treatment and a special effort on four methodological key issues has to be done. The first one is related to information modelling. In safety studies, one can distinguish two kinds of uncertainty. The first type, called aleatory uncertainty, is due to the natural variability of an observed phenomenon and cannot be reduced by the arrival of new information. The second type, called epistemic uncertainty, can arise from imprecision. Contrary to the previous one, this uncertainty can be reduced by increasing the state of knowledge. Performing a relevant information modelling therefore requires to work with a mathematical formalism flexible enough to faithfully treat both types of uncertainties. The second one deals with information propagation through a computer code. It requires to run the codes several times and it is usually achieved thanks to a coupling to a statistical software. The complexity of the propagation is strongly connected to the mathematical framework used for the information modelling. The more general the

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

    International Nuclear Information System (INIS)

    Hernandez-Solis, Augusto

    2010-04-01

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

  8. Eigenvalue sensitivity analysis and uncertainty quantification in SCALE6.2.1 using continuous-energy Monte Carlo Method

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-07-01

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

  9. Data analysis for radiological characterisation: Geostatistical and statistical complementarity

    International Nuclear Information System (INIS)

    Desnoyers, Yvon; Dubot, Didier

    2012-01-01

    Radiological characterisation may cover a large range of evaluation objectives during a decommissioning and dismantling (D and D) project: removal of doubt, delineation of contaminated materials, monitoring of the decontamination work and final survey. At each stage, collecting relevant data to be able to draw the conclusions needed is quite a big challenge. In particular two radiological characterisation stages require an advanced sampling process and data analysis, namely the initial categorization and optimisation of the materials to be removed and the final survey to demonstrate compliance with clearance levels. On the one hand the latter is widely used and well developed in national guides and norms, using random sampling designs and statistical data analysis. On the other hand a more complex evaluation methodology has to be implemented for the initial radiological characterisation, both for sampling design and for data analysis. The geostatistical framework is an efficient way to satisfy the radiological characterisation requirements providing a sound decision-making approach for the decommissioning and dismantling of nuclear premises. The relevance of the geostatistical methodology relies on the presence of a spatial continuity for radiological contamination. Thus geo-statistics provides reliable methods for activity estimation, uncertainty quantification and risk analysis, leading to a sound classification of radiological waste (surfaces and volumes). This way, the radiological characterization of contaminated premises can be divided into three steps. First, the most exhaustive facility analysis provides historical and qualitative information. Then, a systematic (exhaustive or not) surface survey of the contamination is implemented on a regular grid. Finally, in order to assess activity levels and contamination depths, destructive samples are collected at several locations within the premises (based on the surface survey results) and analysed. Combined with

  10. Cassini Spacecraft Uncertainty Analysis Data and Methodology Review and Update/Volume 1: Updated Parameter Uncertainty Models for the Consequence Analysis

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-11-01

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

  11. Uncertainty Analysis of A Flood Risk Mapping Procedure Applied In Urban Areas

    Science.gov (United States)

    Krause, J.; Uhrich, S.; Bormann, H.; Diekkrüger, B.

    In the framework of IRMA-Sponge program the presented study was part of the joint research project FRHYMAP (flood risk and hydrological mapping). A simple con- ceptual flooding model (FLOODMAP) has been developed to simulate flooded areas besides rivers within cities. FLOODMAP requires a minimum of input data (digital el- evation model (DEM), river line, water level plain) and parameters and calculates the flood extent as well as the spatial distribution of flood depths. of course the simulated model results are affected by errors and uncertainties. Possible sources of uncertain- ties are the model structure, model parameters and input data. Thus after the model validation (comparison of simulated water to observed extent, taken from airborne pictures) the uncertainty of the essential input data set (digital elevation model) was analysed. Monte Carlo simulations were performed to assess the effect of uncertain- ties concerning the statistics of DEM quality and to derive flooding probabilities from the set of simulations. The questions concerning a minimum resolution of a DEM re- quired for flood simulation and concerning the best aggregation procedure of a given DEM was answered by comparing the results obtained using all available standard GIS aggregation procedures. Seven different aggregation procedures were applied to high resolution DEMs (1-2m) in three cities (Bonn, Cologne, Luxembourg). Basing on this analysis the effect of 'uncertain' DEM data was estimated and compared with other sources of uncertainties. Especially socio-economic information and monetary transfer functions required for a damage risk analysis show a high uncertainty. There- fore this study helps to analyse the weak points of the flood risk and damage risk assessment procedure.

  12. Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

    Directory of Open Access Journals (Sweden)

    V. E. P. Lemaire

    2016-03-01

    Full Text Available Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology. After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071–2100 for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate of −1.08 (±0.21, −1.03 (±0.32, −0.83 (±0.14 µg m−3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the

  13. Statistical analysis of angular correlation measurements

    International Nuclear Information System (INIS)

    Oliveira, R.A.A.M. de.

    1986-01-01

    Obtaining the multipole mixing ratio, δ, of γ transitions in angular correlation measurements is a statistical problem characterized by the small number of angles in which the observation is made and by the limited statistic of counting, α. The inexistence of a sufficient statistics for the estimator of δ, is shown. Three different estimators for δ were constructed and their properties of consistency, bias and efficiency were tested. Tests were also performed in experimental results obtained in γ-γ directional correlation measurements. (Author) [pt

  14. 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.

  15. Investments in technology subject to uncertainty. Analysis and policy

    DEFF Research Database (Denmark)

    Pedersen, Jørgen Lindgaard

    1997-01-01

    Investments in technology are today of such a magnitude that it matters. In the paper there are three important questions. First on the question in which sense technological uncertainty can be said to be a problem. Second on strategies for diminishing technological uncertainties. Three on policy...

  16. Surface Properties of TNOs: Preliminary Statistical Analysis

    Science.gov (United States)

    Antonieta Barucci, Maria; Fornasier, S.; Alvarez-Cantal, A.; de Bergh, C.; Merlin, F.; DeMeo, F.; Dumas, C.

    2009-09-01

    An overview of the surface properties based on the last results obtained during the Large Program performed at ESO-VLT (2007-2008) will be presented. Simultaneous high quality visible and near-infrared spectroscopy and photometry have been carried out on 40 objects with various dynamical properties, using FORS1 (V), ISAAC (J) and SINFONI (H+K bands) mounted respectively at UT2, UT1 and UT4 VLT-ESO telescopes (Cerro Paranal, Chile). For spectroscopy we computed the spectral slope for each object and searched for possible rotational inhomogeneities. A few objects show features in their visible spectra such as Eris, whose spectral bands are displaced with respect to pure methane-ice. We identify new faint absorption features on 10199 Chariklo and 42355 Typhon, possibly due to the presence of aqueous altered materials. The H+K band spectroscopy was performed with the new instrument SINFONI which is a 3D integral field spectrometer. While some objects show no diagnostic spectral bands, others reveal surface deposits of ices of H2O, CH3OH, CH4, and N2. To investigate the surface properties of these bodies, a radiative transfer model has been applied to interpret the entire 0.4-2.4 micron spectral region. The diversity of the spectra suggests that these objects represent a substantial range of bulk compositions. These different surface compositions can be diagnostic of original compositional diversity, interior source and/or different evolution with different physical processes affecting the surfaces. A statistical analysis is in progress to investigate the correlation of the TNOs’ surface properties with size and dynamical properties.

  17. Time Series Analysis Based on Running Mann Whitney Z Statistics

    Science.gov (United States)

    A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...

  18. On the principled assignment of probabilities for uncertainty analysis

    International Nuclear Information System (INIS)

    Unwin, S.D.; Cook, I.

    1986-01-01

    The authors sympathize with those who raise the questions of inscrutability and over-precision in connection with probabilistic techniques as currently implemented in nuclear PRA. This inscrutability also renders the probabilistic approach, as practiced, open to abuse. They believe that the appropriate remedy is not the discarding of the probabilistic representation of uncertainty in favour of a more simply structured, but logically inconsistent approach such as that of bounding analysis. This would be like forbidding the use of arithmetic in order to prevent the issuing of fraudulent company prospectuses. The remedy, in this analogy, is the enforcement of accounting standards for the valuation of inventory, rates of depreciation etc. They require an analogue of such standards in the PRA domain. What is needed is not the interdiction of probabilistic judgment, but the interdiction of private, inscrutable judgment. Some principles may be conventional in character, as are certain accounting principles. They expound a set of controlling principles which they suggest should govern the formulation of probabilities in nuclear risk analysis. A fuller derivation and consideration of these principles can be found

  19. Uncertainty Analysis of RBMK-Related Experimental Data

    International Nuclear Information System (INIS)

    Urbonas, Rolandas; Kaliatka, Algirdas; Liaukonis, Mindaugas

    2002-01-01

    An attempt to validate state-of-the-art thermal hydraulic code ATHLET (GRS, Germany) on the basis of E-108 test facility was made. Originally this code was developed and validated for different type reactors than RBMK. Since state-of-art thermal hydraulic codes are widely used for simulation of RBMK reactors, further codes' implementation and validation is required. The phenomena associated with channel type flow instabilities and CHF were found to be an important step in the frame of the overall effort of state-of-the-art validation and application for RBMK reactors. In the paper one-channel approach analysis is presented. Thus, the oscillatory behaviour of the system was not detected. The results show dependence on the nodalization used in the heated channels, initial and boundary conditions and code selected models. It is shown that the code is able to predict a sudden heat structure temperature excursion, when critical heat flux is approached. GRS developed uncertainty and sensitivity methodology was employed in the analysis. (authors)

  20. Structural uncertainty in seismic risk analysis. Seismic safety margins research program

    Energy Technology Data Exchange (ETDEWEB)

    Hasselman, T K; Simonian, S S [J.H. Wiggins Company (United States)

    1980-03-01

    This report documents the formulation of a methodology for modeling and evaluating the effects of structural uncertainty on predicted modal characteristics of the major structures and substructures of commercial nuclear power plants. The uncertainties are cast in the form of normalized random variables which represent the demonstrated ability to predict modal frequencies, damping and modal response amplitudes for broad generic types of structures (steel frame, reinforced concrete and prestressed concrete). Data based on observed differences between predicted and measured structural performance at the member, substructure, and/or major structural system levels are used to quantify uncertainties and thus form the data base for statistical analysis. Proper normalization enables data from non-nuclear structures, e.g., office buildings, to be included in the data base. Numerous alternative methods are defined within the general framework of this methodology. The report also documents the results of a data survey to identify, classify and evaluate available data for the required data base. A bibliography of 95 references is included. Deficiencies in the currently identified data base are exposed, and remedial measures suggested. Recommendations are made for implementation of the methodology. (author)