A probabilistic framework for multidisciplinary design: Application to the hydrostructural optimization of supercavitating hydrofoils
Luca Bonfiglio, Paris Perdikaris, José del Águila Ferrandis, George Em Karniadakis · International Journal for Numerical Methods in Engineering · 2018
Summary The analysis and optimization of complex multiphysics systems presents a series of challenges that limit the practical use of computational tools. Specifically, the optimization of such systems involves multiple interconnected components with competing quantities of interest and high‐dimensional spaces and necessitates the use of costly high‐fidelity solvers to accurately simulate the coupled multiphysics. In this paper, we put forth a data‐driven framework to address these challenges leveraging recent advances in machine learning. We combine multifidelity Gaussian process regression and Bayesian optimization to construct probabilistic surrogate models for given quantities of interest and explore high‐dimensional design spaces in a cost‐effective manner. The synergistic use of these computational tools gives rise to a tractable and general framework for tackling realistic multidisciplinary optimization problems. To demonstrate the specific merits of our approach, we have chosen a challenging large‐scale application involving the hydrostructural optimization of three‐dimensional supercavitating hydrofoils. To this end, we have developed an automated workflow for performing multiresolution simulations of turbulent multiphase flows and multifidelity structural mechanics (combining three‐dimensional and one‐dimensional finite element results), the results of which drive our machine learning analysis in pursuit of the optimal hydrofoil shape.