Bayesian collaborative sampling: adaptive learning for multidisciplinary design
Lee, Chung Hyun
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A Bayesian adaptive sampling method is developed for highly coupled multidisciplinary design problems. The method addresses a major challenge in aerospace design: exploration of a design space with computationally expensive analysis tools such as computational fluid dynamics (CFD) or finite element analysis. With a limited analysis budget, it is often impossible to optimize directly or to explore a design space with off-line design of experiments (DoE) and surrogate models. This difficulty is magnified in multidisciplinary problems with feedbacks between disciplines because each design point may require iterative analyses to converge on a compatible solution between different disciplines. Bayesian Collaborative Sampling (BCS) is a bi-level architecture for adaptive sampling that simulataneously - concentrates disciplinary analyses in regions of a design space that are favorable to a system-level objective - guides analyses to regions where interdisciplinary coupling variables are probably compatible BCS uses Bayesian models and sequential sampling techniques along with elements of the collaborative optimization (CO) architecture for multidisciplinary optimization. The method is tested with the aero-structural design of a glider wing and the aero-propulsion design of a turbojet engine nacelle.