Simulation Uncertainty in Multidisciplinary Design

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This grant provides funding for the development of a decision based design framework for the design of complex engineering systems such as automobiles, aircraft or consumer products. The design of these engineering systems involves extensive use of computer simulation design tools. Each computer simulation model is an abstraction of reality and has some uncertainty associated with its performance predictions. This research will investigate how these uncertainties propagate through a multidisciplinary system comprised of many discipline-specific simulation tools. A goal of this research is to be able to quantify these propagated uncertainties for use in a decision-based design framework. This investigation will evaluate techniques available from experimental data analysis for estimating the worst case propagated uncertainty in complex engineering systems and investigate the utility of performing Monte Carlo simulations on response surface approximations of system performance for quantifying the distributions of the propagated uncertainties. Having quantified propagated uncertainties in complex systems, the second phase of this research effort will be devoted to the implementation of a decision-based design framework. An optimization algorithm based on a trust region managed augmented Lagrangian approach for concurrent subspace design/optimization will be used to drive the decision-based design framework. This optimization will be based on artificial neural network response surface approximations of the system performance. The optimizer will maximize the expected utility of the net revenue for the total life cycle cost of the product subject to stochastic performance constraints. It is anticipated that the results of this research will lead to improved product performances as well as reduced product development times at reduced cost and risk. The development of a decision-based design framework will demonstrate that designers can effectively manage both the uncertainty and risk associated with the simulation based design of new products.