Hydrodynamic numerical simulation and prediction of bionic fish based on computational fluid dynamics and multilayer perceptron

Abstract

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. The accuracy and generalization ability of the MLP prediction model is verified.