Model validation and predictive capability for the thermal challenge problem

被引:213
作者
Ferson, Scott
Oberkampf, William L. [1 ]
Ginzburg, Lev [2 ]
机构
[1] Sandia Natl Labs, Validat & Uncertainty Estimat Dept, Dept 1544, Albuquerque, NM 87185 USA
[2] SUNY Stony Brook, Dept Ecol & Evolut, Stony Brook, NY 11794 USA
关键词
validation; predictive capability; thermal challenge problem; area metric; UNCERTAINTY; PROPAGATION;
D O I
10.1016/j.cma.2007.07.030
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We address the thermal problem posed at the Sandia Validation Challenge Workshop. Unlike traditional approaches that confound calibration with validation and prediction, our approach strictly distinguishes these activities, and produces a quantitative measure of model-form uncertainty in the face of available data. We introduce a general validation metric that can be used to characterize the disagreement between the quantitative predictions from a model and relevant empirical data when either or both predictions and data are expressed as probability distributions. By considering entire distributions, this approach generalizes traditional approaches to validation that focus only on the mean behaviors of predictions and observations. The proposed metric has several desirable properties that should make it practically useful in engineering, including objectiveness and robustness, retaining the units of the data themselves, and generalizing the deterministic difference. The metric can be used to assess the overall performance of a model against all the experimental observations in the validation domain and it can be extrapolated to express predictive capability of the model under conditions for which direct experimental observations are not available. We apply the metric and the scheme for characterizing predictive capability to the thermal problem. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:2408 / 2430
页数:23
相关论文
共 40 条
[1]   THE PROBABILITY INTEGRAL TRANSFORM AND RELATED RESULTS [J].
ANGUS, JE .
SIAM REVIEW, 1994, 36 (04) :652-654
[2]  
[Anonymous], 2004, Tech. Rep. SAND2004-3072
[3]  
*ASME, 102006 ASME VV
[4]  
Berger James O., 1985, Statistical decision theory and Bayesian analysis
[5]  
Box GEP., 1987, EMPIRICAL MODEL BUIL
[6]   Model validation via uncertainty propagation and data transformations [J].
Chen, W ;
Baghdasaryan, L ;
Buranathiti, T ;
Cao, J .
AIAA JOURNAL, 2004, 42 (07) :1406-1415
[7]  
CHEN W, 2006, P ASME 2006 INT DES
[8]  
CHEN W, 2008, J MECH DES, V130
[9]  
Computational Fluid Dynamics Committee, 1998, G07719982002 AIAA CO
[10]  
Cullen A.C., 1999, Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs