Fast prediction method of propeller hydrodynamic performance based on SPF-LSTM

Abstract

In order to realize fast and accurate prediction of hydrodynamic performance of UUV propeller, a hydrodynamic performance prediction model based on SPF-LSTM neural network was proposed in this paper, which combined with real propeller flow test and CFD numerical simulation technology. The model comprehensively considered the test data of real paddle and the simulation data based on CFD numerical simulation, and adopted the Sample penalty factor (SPF) to optimize the initial weight of the model, adjust the sensitivity of the model to different samples, and further improve the prediction accuracy. The experimental results show that the prediction results of this model are in good agreement with the test data of real propeller, and the calculation period is very short and the efficiency is high, which meets the requirement of real-time and accurate prediction of the open water performance of propeller.