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Engineering and geosciences organizations must increasingly rely on computational simulation for the design, predicted response, and performance of manmade and natural systems. Computational analysts, decision makers, and regulatory authorities who rely on simulation should have practical techniques and methods or assessing simulation credibility. This webinar presents an introduction to the topics of verification, validation, and predictive capability. These topics are applicable to essentially all engineering and science applications, including fluid dynamics, heat transfer, solid mechanics, and all fields related to Earth sciences. The mathematical models for the systems of interest are typically given by partial differential or integral equations representing initial value and boundary value problems. The computer codes that implement the mathematical models can be developed by commercial, corporate, government, or research organizations; but they should all be subjected to rigorous testing by way of verification procedures. The accuracy of the mathematical models coded into software are assessed by way of comparisons of results with experimental measurements; referred to as validation activities. The webinar will sketch a framework for incorporating a wide range of error and uncertainty sources identified during the mathematical modeling, verification, validation, and calibration processes, with the goal of estimating the total predictive uncertainty of the simulation. This is referred to as estimating predictive capability because typically no experimental measurements are available for the systems at the application conditions of interest.
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