The process of establishing credibility in computational model predictions via verification and validation (V&V) encompasses a wide range of activities. Those activities are focused on collecting evidence that the model is adequate for the intended application and that the errors and uncertainties are quantified. In this work, we use the predictive capability maturity model (PCMM) as an organizing framework for evidence collection activities and summarizing our credibility assessment. We discuss our approaches to sensitivity analysis, model calibration, model validation, and uncertainty quantification and how they support our assessments in the solution verification, model validation, and uncertainty quantification elements of the PCMM. For completeness, we also include some limited assessment discussion for the remaining PCMM elements. Because the computational cost of performing V&V and the ensuing predictive calculations is substantial, we include discussion of our approach to addressing computational resource considerations, primarily through the use of response surface surrogates and multiple mesh fidelities.
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March 2016
Research-Article
Sandia Verification and Validation Challenge Problem: A PCMM-Based Approach to Assessing Prediction Credibility
Lauren L. Beghini,
Lauren L. Beghini
Multi-Physics Modeling and Simulation,
Sandia National Laboratories,
P.O. Box 969, MS 9042,
Livermore, CA 94550-0969
e-mail: llbeghi@sandia.gov
Sandia National Laboratories,
P.O. Box 969, MS 9042,
Livermore, CA 94550-0969
e-mail: llbeghi@sandia.gov
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Patricia D. Hough
Patricia D. Hough
Quantitative Modeling and Analysis,
Sandia National Laboratories,
P.O. Box 969, MS 9159,
Livermore, CA 94550-0969
e-mail: pdhough@sandia.gov
Sandia National Laboratories,
P.O. Box 969, MS 9159,
Livermore, CA 94550-0969
e-mail: pdhough@sandia.gov
Search for other works by this author on:
Lauren L. Beghini
Multi-Physics Modeling and Simulation,
Sandia National Laboratories,
P.O. Box 969, MS 9042,
Livermore, CA 94550-0969
e-mail: llbeghi@sandia.gov
Sandia National Laboratories,
P.O. Box 969, MS 9042,
Livermore, CA 94550-0969
e-mail: llbeghi@sandia.gov
Patricia D. Hough
Quantitative Modeling and Analysis,
Sandia National Laboratories,
P.O. Box 969, MS 9159,
Livermore, CA 94550-0969
e-mail: pdhough@sandia.gov
Sandia National Laboratories,
P.O. Box 969, MS 9159,
Livermore, CA 94550-0969
e-mail: pdhough@sandia.gov
1Corresponding author.
Manuscript received February 7, 2015; final manuscript received December 18, 2015; published online February 19, 2016. Guest Editor: Kenneth Hu.
J. Verif. Valid. Uncert. Mar 2016, 1(1): 011002 (10 pages)
Published Online: February 19, 2016
Article history
Received:
February 7, 2015
Revised:
December 18, 2015
Citation
Beghini, L. L., and Hough, P. D. (February 19, 2016). "Sandia Verification and Validation Challenge Problem: A PCMM-Based Approach to Assessing Prediction Credibility." ASME. J. Verif. Valid. Uncert. March 2016; 1(1): 011002. https://doi.org/10.1115/1.4032369
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