Shape optimization of underwater wings with a new multi-fidelity bi-level strategy

Abstract

This paper proposes a new multi-fidelity bi-level optimization (MFBLO) strategy for shape designs of underwater wings. Firstly, hydrodynamic analyses of the wing planform and sections are decoupled for constructing a bi-level shape optimization frame, which includes an upper-level task merely concerning the wing planform design and several lower-level tasks only related to the section designs. By doing this, the shape design optimization gets remarkable benefits from the reduction of dimension and computational costs. Secondly, the bridge function method combined with three multi-fidelity data fusion approaches CC1, CC2, and CC3 are proposed to conduct the bi-level optimization, respectively. After comparison analyses, CC2 shows higher computational efficiency and accuracy, which is more appropriate for the bi-level shape optimization frame. Finally, compared with the single-level optimization with the fixed planform or sections and the conventional high-dimensional optimization, the proposed MFBLO needs less computation budget and gets higher lift-drag ratio, showing its outstanding advantages in handling the shape optimization of underwater wings.