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Research Papers

Sandia Verification and Validation Challenge Problem: A PCMM-Based Approach to Assessing Prediction Credibility

[+] Author and Article Information
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

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

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 1(1), 011002 (Feb 19, 2016) (10 pages) Paper No: VVUQ-15-1009; doi: 10.1115/1.4032369 History: Received February 07, 2015; Revised December 18, 2015

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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References

Figures

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Fig. 1

This figure shows the flow of information from experimental data through to predictions about tank failure. Key activities in the collection of credibility evidence are noted.

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Fig. 4

Calibration yielded parameter values for Young's modulus and thickness that were inconsistent with experimentally measured material properties, calling into question both data and model

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Fig. 5

Relative errors between model and data displacement values at all measurement locations. Computational studies consistently showed the largest stresses and displacements along the bottom of the tank. The model to experiment comparisons closest to the bottom occur at ϕ=30, which corresponds to locations 1, 6, 11, and 16.

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Fig. 7

Yield stress thresholds used for probability of failure computations are overlaid on experimental yield stress measurements. Given scatter in the data, failure threshold is considered to be uncertain, so we chose three possible values that span the data.

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Fig. 6

Laboratory tests showing the deformed shape of tank 1 under pressure loading

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Fig. 8

Deformed shape and corresponding stresses along the bottom of the tank

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