Dual-Objective Pareto Optimization Method of Flapping Hydrofoil Propulsion Performance Based on MLP and Double DQN

To address the inherent complexities of underwater operating environments and achieve the design of a highly efficient, energy-saving flapping hydrofoil, this paper proposes an intelligent agent-based model for real-time parametric optimization. A non-parametric surrogate model based on a Multilayer Perceptron (MLP) is established using data samples of multi-dimensional flapping hydrofoil geometric parameters obtained through Computational Fluid Dynamics (CFD) simulations. An improved Double Deep Q-Network (DDQN) algorithm incorporating Pareto frontier information is deployed within the surrogate model to obtain the Pareto optimal solution set for propulsion efficiency and average input power, and a set of propulsion parameter combinations with error ranges between 0.24% and 1.27% across continuous intervals was obtained. Experimental results demonstrate that the proposed MLP-DDQN method is capable of learning the domain-wide optimal solution within the experimental environment, satisfying the Pareto optimality between propulsion efficiency and average input power. Further analysis of the flow field around the flapping hydrofoil under the obtained optimal parameter combination revealed that the presence of stable and continuously attached vortex structures on the wing surface is the intrinsic mechanism responsible for its superior propulsion performance.