Research on High-Precision Prediction of Vertical Hydrodynamic Coefficients for AUV Based on CFD and GA-BP Neural Network

Aiming at the engineering pain points of insufficient sample size, inadequate working condition coverage, and poor generalization performance in traditional solution methods of vertical hydrodynamic coefficients for Autonomous Underwater Vehicles (AUVs), this paper constructs a high-precision prediction framework for vertical hydrodynamic coefficients Computational Fluid Dynamics (CFD) numerical simulation and a genetic algorithm-optimized back-propagation (GA-BP) neural network. Firstly, the overset grid technology and User-Defined Function (UDF) are adopted to simulate the pure heave motion of the Planar Motion Mechanism (PMM), completing the unsteady flow field numerical calculation. Secondly, 200 working condition simulation datasets covering different motion amplitudes, heave frequencies and inflow velocities are constructed, and a three-input and two-output BP neural network basic prediction model is established. Subsequently, the basic model is optimized by 5-fold cross-validation and genetic algorithm respectively, and the prediction performances of BP, K-fold-BP and GA-BP models are compared and analyzed. The results show that the determination coefficients of the GA-BP model for the dimensionless vertical hydrodynamic coefficients Za and Zb reach 0.9976 and 0.9975 respectively, and the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are significantly lower than those of the other two models, with the optimal prediction accuracy and generalization performance. Finally, 1000 sets of full-working-condition hydrodynamic coefficient prediction are completed based on the GA-BP model, and the optimized dimensionless added mass coefficient and fluid damping coefficient are obtained by fitting. The prediction framework constructed in this paper can provide technical support for the rapid and high-precision acquisition of AUV full-working-condition hydrodynamic coefficients, and provide a reliable parameter basis for dynamic modeling and motion control system design.