Journal of Verification, Validation and Uncertainty Quantification最新文献_第5页

Comment On “Comparison of the V&V10.1 and V&V20 Modeling Error Quantification Procedures for the V&V10.1 Example” 对“V&V10.1实例中V&V10.1与V&V20建模误差量化程序的比较”的评论

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-07-28 DOI: 10.1115/1.4055105

P. Roache

引用次数: 0

Analytical Sensitivity Analysis of a Spent Nuclear Fuel Cask 乏核燃料桶分析灵敏度分析

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-07-15 DOI: 10.1115/1.4055013

T. Remedes, S. Ramsey, J. Baciak

引用次数: 0

A Methodology for the Efficient Quantification of Parameter and Model Uncertainty 一种有效量化参数和模型不确定性的方法

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Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-05-18 DOI: 10.1115/1.4054575

R. Feldmann, C. M. Gehb, M. Schäffner, T. Melz

{"title":"A Methodology for the Efficient Quantification of Parameter and Model Uncertainty","authors":"R. Feldmann, C. M. Gehb, M. Schäffner, T. Melz","doi":"10.1115/1.4054575","DOIUrl":"https://doi.org/10.1115/1.4054575","url":null,"abstract":"\u0000 Complex structural systems often entail computationally intensive models that require efficient methods for statistical model calibration due to the high number of required model evaluations. In this paper, we present a BAYESIAN inference-based methodology for efficient statistical model calibration that builds upon the combination of the speed in computation of a low-fidelity model with the accuracy of the computationally intensive high-fidelity model. The proposed two-stage method incorporates the adaptive METROPOLIS algorithm and a GAUSSIAN process (GP)-based adaptive surrogate model as low-fidelity model. In order to account for model uncertainty, we incorporate a GP-based discrepancy function into the model calibration. By calibrating the hyperparameters of the discrepancy function alongside the model parameters, we prevent the results of the model calibration to be biased. The methodology is illustrated by the statistical model calibration of a damping parameter in the modular active spring-damper system, a structural system developed within the collaborative research center SFB 805 at the Technical University of Darmstadt. The reduction of parameter and model uncertainty achieved by application of our methodology is quantified and illustrated by assessing the predictive capability of the mathematical model of the modular active spring-damper system.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44161006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Verification of MOOSE/Bison's Heat Conduction Solver Using Combined Spatiotemporal Convergence Analysis 基于时空联合收敛分析的驼鹿/野牛热传导求解方法验证

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-03-29 DOI: 10.1115/1.4054216

A. Toptan, N. Porter, J. Hales, Wen Jiang, B. Spencer, S. Novascone

{"title":"Verification of MOOSE/Bison's Heat Conduction Solver Using Combined Spatiotemporal Convergence Analysis","authors":"A. Toptan, N. Porter, J. Hales, Wen Jiang, B. Spencer, S. Novascone","doi":"10.1115/1.4054216","DOIUrl":"https://doi.org/10.1115/1.4054216","url":null,"abstract":"\u0000 Bison is a computational physics code that uses the finite element method to model the thermo-mechanical response of nuclear fuel. Since Bison is used to inform high-consequence decisions, it is important that its computational results are reliable and predictive. One important step in assessing the reliability and predictive capabilities of a simulation tool is the verification process, which quantifies numerical errors in a discrete solution relative to the exact solution of the mathematical model. One step in the verification process–called code verification–ensures that the implemented numerical algorithm is a faithful representation of the underlying mathematical model, including partial differential or integral equations, initial and boundary conditions, and auxiliary relationships. In this paper, the code verification process is applied to spatiotemporal heat conduction problems in Bison. Simultaneous refinement of the discretization in space and time is employed to reveal any potential mistakes in the numerical algorithms for the interactions between the spatial and temporal components of the solution. For each verification problem, the correct spatial and temporal order of accuracy is demonstrated for both first- and second order accurate finite elements and a variety of time integration schemes. These results provide strong evidence that the Bison numerical algorithm for solving spatiotemporal problems reliably represents the underlying mathematical model in MOOSE. The selected test problems can also be used in other simulation tools that numerically solve for conduction or diffusion.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46357867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Comparison of the V&V10.1 and V&V20 Modeling Error Quantification Procedures for the V&V10.1 Example V&V10.1和V&V20建模误差量化程序的比较

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-02-16 DOI: 10.1115/1.4053881

L. Eça, K. Dowding, D. Moorcroft, U. Ghia

{"title":"Comparison of the V&V10.1 and V&V20 Modeling Error Quantification Procedures for the V&V10.1 Example","authors":"L. Eça, K. Dowding, D. Moorcroft, U. Ghia","doi":"10.1115/1.4053881","DOIUrl":"https://doi.org/10.1115/1.4053881","url":null,"abstract":"\u0000 The determination of the transverse tip deflection of an elastic, hollow, tapered, cantilever, box beam under a uniform loading applied over half the length of the beam presented in the V&V10.1 standard is used to compare the application of the validation procedures presented in the V&V10.1 and V&V20 standards. Both procedures aim to estimate the modeling error of the mathematical/computational model used in the simulations taking into account the variability of the modulus of elasticity of the material used in the beam and the rotational flexibility at the clamped end of the beam.\u0000 The paper discusses the four steps of the two error quantification procedures: 1- characterization of the problem including all the assumptions and approximations made to obtain the experimental and simulation data; 2-selection of the validation variable; 3- determination of the different quantities required by the validation metrics in the two error quantification procedures; 4- outcome of the two validation procedures and its discussion. The paper also discusses the inclusion of experimental, input and numerical uncertainties (assumed or demonstrated to be negligible in V&V10.1) in the two validation approaches. This simple exercise shows that different choices are made in the two alternative approaches, which lead to different ways of characterizing the modeling error. The topics of accuracy requirements and validation comparisons (model acceptance/rejection) for engineering applications are not addressed in this paper.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44039917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inferring Extreme Values from Measured Averages Under Deep Uncertainty 从深度不确定性下的测量平均值推断极值

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-01-26 DOI: 10.1115/1.4053411

Y. Ben-Haim

{"title":"Inferring Extreme Values from Measured Averages Under Deep Uncertainty","authors":"Y. Ben-Haim","doi":"10.1115/1.4053411","DOIUrl":"https://doi.org/10.1115/1.4053411","url":null,"abstract":"\u0000 Averages are measured in many circumstances for diagnostic, predictive, or surveillance purposes. Examples include: average stress along a beam, average speed along a section of highway, average alcohol consumption per month, average GDP over a large region, a student's average grade over 4 years of study. However, the average value of a variable reveals nothing about fluctuations of the variable along the path that is averaged. Extremes – stress concentrations, speeding violations, binge drinking, poverty and wealth, intellectual incompetence in particular topics – may be more significant than the average. This paper explores the choice of design variables and performance requirements to achieve robustness against uncertainty when interpreting an average, in face of uncertain fluctuations of the averaged variable. Extremes are not observed, but robustness against those extremes enhances the ability to interpret the observed average in terms of the extremes. The opportuneness from favorable uncertainty is also explored. We examine the design of a cantilever beam with uncertain loads. We derive 4 generic propositions, based on info-gap decision theory, that establish necessary and sufficient conditions for robust or opportune dominance, and for sympathetic relations between robustness to pernicious uncertainty and opportuneness from propitious uncertainty.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44561673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Estimating Discretization Error with Preset Orders of Accuracy and Fractional Refinement Ratios 预估离散化误差的精度和分数精化比

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2022-01-01 DOI: 10.1115/1.4056491

S. C. Y. Lo

{"title":"Estimating Discretization Error with Preset Orders of Accuracy and Fractional Refinement Ratios","authors":"S. C. Y. Lo","doi":"10.1115/1.4056491","DOIUrl":"https://doi.org/10.1115/1.4056491","url":null,"abstract":"\u0000 Solution verification is crucial for establishing the reliability of simulations. A central challenge is to estimate the discretization error accurately and reliably. Many approaches to this estimation are based on the observed order of accuracy; however, it may fail when the numerical solutions lie outside the asymptotic range. Here we propose a grid refinement method which adopts constant orders given by the user, called the Prescribed Orders Expansion Method (POEM). Through an iterative procedure, the user is guaranteed to obtain the dominant orders of the discretization error. The user can also compare the corresponding terms to quantify the degree of asymptotic convergence of the numerical solutions. These features ensure that the estimation of the discretization error is accurate and reliable. Moreover, the implementation of POEM is the same for any dimensions and refinement paths. We demonstrate these capabilities using some advection and diffusion problems and standard refinement paths. The computational cost of using POEM is lower if the refinement ratio is larger; however, the number of shared grid points where POEM applies also decreases, causing greater uncertainty in the global estimates of the discretization error. We find that the proportion of shared grid points is maximized when the refinement ratios are in a certain form of fractions. Furthermore, we develop the Method of Interpolating Differences between Approximate Solutions (MIDAS) for creating shared grid points in the domain. These approaches allow users of POEM to obtain a global estimate of the discretization error of lower uncertainty at a reduced computational cost.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63503457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Verification of a Specialized Hydrodynamic Simulation Code for Modeling Deflagration and Detonation of High Explosives 高烈度炸药爆燃爆轰模拟专用水动力模拟程序的验证

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2021-12-21 DOI: 10.1115/1.4053340

Stephen A. Andrews, T. Aslam

引用次数: 4

Uncertainty Quantification of Time-Dependent Quantities in a System with Adjustable Level of Smoothness 平滑度可调系统中时变量的不确定性量化

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2021-12-06 DOI: 10.1115/1.4053161

Marks Legkovskis, P. Thomas, M. Auinger

{"title":"Uncertainty Quantification of Time-Dependent Quantities in a System with Adjustable Level of Smoothness","authors":"Marks Legkovskis, P. Thomas, M. Auinger","doi":"10.1115/1.4053161","DOIUrl":"https://doi.org/10.1115/1.4053161","url":null,"abstract":"\u0000 We summarise the results of a computational study involved with Uncertainty Quantification (UQ) in a benchmark turbulent burner flame simulation. UQ analysis of this simulation enables one to analyse the convergence performance of one of the most widely-used uncertainty propagation techniques, Polynomial Chaos Expansion (PCE) at varying levels of system smoothness. This is possible because in the burner flame simulations, the smoothness of the time-dependent temperature, which is the study's QoI is found to evolve with the flame development state. This analysis is deemed important as it is known that PCE cannot accurately surrogate non-smooth QoIs and thus perform convergent UQ. While this restriction is known and gets accounted for, there is no understanding whether there is a quantifiable scaling relationship between the PCE's convergence metrics and the level of QoI's smoothness. It is found that the level of QoI-smoothness can be quantified by its standard deviation allowing to observe the effect of QoI's level of smoothness on the PCE's convergence performance. It is found that for our flow scenario, there exists a power-law relationship between a comparative parameter, defined to measure the PCE's convergence performance relative to Monte Carlo sampling, and the QoI's standard deviation, which allows us to make a more weighted decision on the choice of the uncertainty propagation technique.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45278630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing Uncertainty Quantification Strategies in Electromagnetic Problems Involving Highly Resonant Cavities 在涉及高谐振腔的电磁问题中发展不确定性量化策略

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2021-12-01 DOI: 10.1115/1.4051906

S. Campione, J. A. Stephens, Nevin Martin, Aubrey Eckert, L. Warne, Gabriel Huerta, R. Pfeiffer, Adam Jones

引用次数: 0