Abstract

Advancements in predictive computational science make it possible to increase reliance of numerical simulation, necessitating fewer physical experiments for substantial savings in time and costs of product development projects. The first and perhaps the most challenging obstacle to full realization of the benefits of predictive computational science is a widespread misunderstanding of what numerical simulation is. Most managers and many individuals who present themselves as experts in numerical simulation confuse numerical simulation with ?finite element modeling? or ?numerical modeling?. Those are outdated concepts, responsible for much of the disappointing results that caused widespread loss of confidence in the usefulness and reliability of numerical simulation. Current simulation and data management practices will have to be revised in order to meet the technical requirements of predictive computational science. The presentation will focus on the central role of simulation governance and management in the coordination of experimental and analytical work necessary for proper use of the tools and techniques of predictive computational science with the objective to maximize the reliability of computed information. The presentation will outline the methodology of model development in the applied sciences, the essential constituents of which are the formulation, calibration and ranking of mathematical models, data and solution verification, validation and uncertainty quantification. It will be shown that consideration of the size of the domain of calibration is essential. Without such consideration just about any model, even pseudoscientific models, can be calibrated on a sufficiently small domain of calibration. The presentation will also highlight the differences between numerical simulation and finite element modeling. Understanding these concepts and procedures is an indispensable prerequisite to any successful implementation of a Simulation Governance plan. Recognizing that technology changes and the available information increases over time, planning must incorporate data management and systematic updates of simulation practices so as to take advantage of new information and advancements in technology.