Model verification and validation (V&V) is an enabling methodology for the development of computational models that can be used to make engineering predictions with quantified confidence. Model V&V procedures are needed by government and industry to reduce the time, cost, and risk associated with full-scale testing of products, materials, and weapon systems. Quantifying the confidence and predictive accuracy of model calculations provides the decision-maker with the information necessary for making high-consequence decisions. The development of guidelines and procedures for conducting a model V&V program are currently being defined by a broad spectrum of researchers. This report reviews the concepts involved in such a program.Model V&V is a current topic of great interest to both government and industry. In response to a ban on the production of new strategic weapons and nuclear testing, the Department of Energy (DOE) initiated the ScienceBased Stockpile Stewardship Program (SSP). An objective of the SSP is to maintain a high level of confidence in the safety, reliability, and performance of the existing nuclear weapons stockpile in the absence of nuclear testing.This objective has challenged the national laboratories to develop high-confidence tools and methods that can be used to provide credible models needed for stockpile certification via numerical simulation.There has been a significant increase in activity recently to define V&V methods and procedures. The U.S. Model V&V is fundamentally different from software V&V. Code developers developing computer programs perform software V&V to ensure code correctness, reliability, and robustness. In model V&V, the end product is a predictive model based on fundamental physics of the problem being solved. In all applications of practical interest, the calculations involved in obtaining solutions with the model require a computer code, e.g., finite element or finite difference analysis. Therefore, engineers seeking to develop credible predictive models critically need model V&V guidelines and procedures.The expected outcome of the model V&V process is the quantified level of agreement between experimental data and model prediction, as well as the predictive accuracy of the model. This report attempts to describe the general philosophy, definitions, concepts, and processes for conducting a successful V&V program. This objective is motivated by the need for highly accurate numerical models for making predictions to support the SSP, and also by the lack of guidelines, standards and procedures for performing V&V for complex numerical models.

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The developing field of probabilistic design has matured to the point where several classes of analysis methods have been proven to be useful for engineering analysis of large high fidelity structures with uncertain parameters. However, several barriers still stand in the way of widespread acceptance of probabilistic methods into the design practice. These include lack of data for characterizing probabilistic inputs, lack of understanding of analysis methodologies and associated limitations, lack of commercially available tools for performing probabilistic analysis, and lack of verification and validation examples. As a first step towards developing more computationally robust tools for performing probabilistic analysis and improving user understanding of analysis assumptions and limitations, this paper identifies the various types of error that can be encountered when performing probabilistic analysis and offers some suggestions for mitigating or eliminating them from the analysis.

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