Computer simulations of physical processes are being relied on to an increasing degree for design, performance, reliability, and safety of engineered systems. Computational analyses have addressed the operation of systems at design conditions, off-design conditions, and accident scenarios. For example, the safety aspects of products or systems can represent an important, sometimes dominant, element of numerical simulations. The potential legal and liability costs of hardware failures can be staggering to a company, the environment, or the public. This consideration is especially crucial, given that we may be interested in high-consequence systems that cannot ever be physically tested, including the catastrophic failure of a full-scale containment building for a nuclear power plant, explosive damage to a high-rise office building, ballistic missile defense systems, and a nuclear weapon involved in a transportation accident. Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue.
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