Optimising hydrofoils using automated multi-fidelity surrogate models

Abstract

Lifting hydrofoils are gaining importance, since they drastically reduce the wetted surface area of a ship, thus decreasing resistance. To attain efficient hydrofoils, the geometries can be obtained from an automated optimisation process. However, hydrofoil simulations are computationally demanding, since fine meshes are needed to accurately capture the pressure field and the boundary layer on the hydrofoil. Simulation-based optimisation can therefore be very expensive. To speed up the fully automated hydrofoil optimisation procedure, we propose a multi-fidelity framework which takes advantage of both an efficient low-fidelity potential flow solver dedicated to hydrofoils and a high-fidelity RANS solver enhanced with adaptive grid refinement and dedicated foil-aligned overset meshes, to attain high accuracy with a limited computational budget. Both solvers are shown to be reliable for automatic simulation, and remarkable correlation between potential-flow and RANS results is obtained. Two different multi-fidelity frameworks are compared for a realistic hydrofoil: only RANS based and potential-RANS based. According to the optimisation results, the drag is able to be reduced by 17% and 8% in these frameworks, within a realistic time frame. Thus, industrial optimisation of hydrofoils appears possible. Finally, critical areas of future improvement regarding the robustness and efficiency of the optimisation procedure are discussed in this study.