This paper proposes a prediction strategy for the hydrodynamic performance of bionic fish. The major challenges are meshing and building prediction model. The NACA0012 airfoil is used to replace the fish driven by the body and/or caudal fin (BCF), and a two-dimensional swimming geometric model is constructed. The geometric model is divided into hybrid meshes using the overset mesh method. The classical traveling wave model is studied using Matlab, and an improved self-propelled motion model is established. The hydrodynamic performance of the self-propelled model is numerically simulated based on computational fluid dynamics (CFD). In this strategy, the geometric model and the self-propelled motion model are integrated with user defined functions (UDF). The influences of the parameters such as inflow velocity, frequency, wavelength, and head fluctuation amplitude on the motion performance are studied. The results show that when inflow velocity is uniform, the self-propelled motion will eventually reach a quasi-steady state. According to the numerical simulation results, a hydrodynamic performance prediction model is established based on multilayer perceptron (MLP). The model is used to predict the performance of the optimized traveling wave parameters, and the error is within 3$\mathrm{\%}$. The accuracy and generalization ability of the MLP prediction model is verified.

本文提出了一种仿生鱼水动力性能的预测策略。主要挑战是网格划分和构建预测模型。使用NACA0012翼型代替由体和/或尾鳍（BCF）驱动的鱼，构​​建二维游泳几何模型。使用重叠网格方法将几何模型划分为混合网格。利用Matlab对经典行波模型进行了研究，建立了改进的自航运动模型。基于计算流体动力学（CFD）对自航模型的水动力性能进行数值模拟。在该策略中，几何模型和自走运动模型与用户定义函数（UDF）相结合。流入速度、频率、波长等参数的影响，研究了头部波动幅度对运动性能的影响。结果表明，当流入速度均匀时，自航运动最终会达到准稳态。根据数值模拟结果，建立了基于多层感知器（MLP）的水动力性能预测模型。该模型用于预测优化后行波参数的性能，误差在3以内$\mathrm{\%}$. 验证了 MLP 预测模型的准确性和泛化能力。