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This special issue presents the results of the Sandia organized Model Validation Challenge Workshop, held May 2006. The workshop brought together researchers from different fields to present various approaches to model validation, and focused on the methodological elements of model validation rather than on model building. Three problems were defined in the disciplines of structural statics, structural dynamics, and heat transfer, all with a uniform structure. The workshop was specifically designed to investigate the relative merits of different approaches to hierarchal model validation through application to these problems. This paper describes a hierarchal approach in the challenge problems, presents the uniform conceptual framework that was used for the challenge problem definitions, and provides an overview of the organization of this special issue.

This paper summarizes the approaches used to address the thermal validation challenge problem. The approaches differ in their characterization of the thermal properties and uncertainty, the definitions and use of validation metrics, the use of validation experimental data to characterize or improve the model predictions, and the assessment of regulatory compliance. All approaches estimated regulatory failure with the resulting estimated probabilities varying by an order of magnitude.

This paper presents an engineering approach to the thermal challenge problem defined by Dowding et al. (this issue). This approach to model validation is based on a multivariate validation metric that accounts for model parameter uncertainty and correlation between multiple measurement/prediction differences. The effect of model parameter uncertainty is accounted for through first-order sensitivity analysis for the ensemble/validation tests, and first-order sensitivity analysis and Monte-Carlo analysis for the regulatory prediction. While sensitivity based approaches are less computational expensive than Monte-Carlo approaches, they are less likely to capture the far tail behavior of even mildly nonlinear models. The application of the sensitivity based validation metric provided strong evidence that the tested model was not consistent with the experimental data. The use of a temperature dependent effective conductivity with the linear model resulted in model predictions that were consistent with the data. The correlation structure of the model was used to pool the prediction/measurement differences to evaluate the corresponding cumulative density function (CDF). Both the experimental CDF and the predicted CDFs indicated that the regulatory criterion was not met.

This paper describes the thermal problem and presents the experimental data for validation. The thermal problem involves validating a model for heat conduction in a solid. The mathematical model is based on one-dimensional, linear heat conduction in a solid slab, with heat flux boundary conditions. Experimental data from a series of material characterization, validation, and accreditation experiments related to the mathematical model are provided. The objective is to use the series of experiments to assess the model, and then use the model to predict regulatory performance relative to a regulatory requirement. The regulatory requirement is defined in terms of the probability that a surface temperature not exceed a specified temperature at the regulatory conditions.