Collaborative Research: Statistical Decision-Theoretic Methods for Robust Design Optimization

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Engineers increasingly rely on computer simulation to develop new products, such as, airfoils, and to understand emerging technologies, for instance, fusion capsules. In practice, this process is permeated with uncertainty: manufactured products deviate from designed products; actual products must perform under a variety of operating conditions. The problem of robust design is the problem of optimizing complex, simulation-based engineered systems in the presence of uncertainty about manufacturing and operating conditions.This research project will address the problem of developing rigorous, computationally tractable methods for robust design. Statistical decision theory, specifically the Bayes principle, provides a conceptual framework for quantifying the uncertainties. The application of statistical decision theory to robust design has been infrequently attempted and lies at the frontier of current engineering practice; the proposed project will extend that frontier by developing more efficient computational methods.The methods to be developed will be demonstrated on aerodynamic design optimization problems of vital interest at NASA Langley Research Center. These problems provide an ideal platform for the development of robust design methods. Beyond our focus on aerodynamic design, uncertainty-based design methods hold great promise for a wide range of applications, e.g., the design of fusion capsules tested at Sandia National Laboratories. The potential benefits include increased confidence in analysis tools; reductions in design cycle time, risk, and cost; increasingly robust designs; and improved system performance with ensured reliability. Undergraduates from across mathematics, computer science and engineering will be involved with this research.