Abstract
We present an effective multi-fidelity framework for shape optimization of super-cavitating hydrofoils using viscous solvers. We employ state-of-the-art machine learning tools such as multi-fidelity Gaussian process regression and Bayesian optimization to synthesize data obtained from multi-resolution simulations, and efficiently identify optimal configurations in the design space. We validate our simulation results against experimental data, and showcase the efficiency of the proposed work-flow in a realistic design problem involving the shape optimization of a three-dimensional super-cavitating hydrofoil parametrized by 17 design variables.
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