Fast prediction of propeller dynamic wake based on deep learning

Abstract

Efficiently predicting the wake of propellers is of great importance for achieving propeller design optimization. In this work, the deep learning (DL) method called propeller wake convolutional neural networks (PWCNN) is proposed, which combines the transformer encoder and dilated convolutional block to capture the multi-scale characteristics of wakes. Computational fluid dynamics (CFD) simulations are conducted using the delayed detached eddy simulation model for the wake to generate extensive high-fidelity wake data of the propeller operating under different operating conditions required for DL. PWCNN takes the wake predicted at the previous time step to update input and iteratively predicts the wake at future time steps to achieve dynamic wake prediction. The good agreement between DL prediction and CFD simulation results, with the mean relative error of the velocity components less than 2.36% for 15 future time steps, proves that PWCNN can efficiently capture the spatiotemporal evolution characteristic of dynamic wakes. Furthermore, PWCNN can predict the wake dynamic changes with reasonable accuracy under unseen operating conditions, further confirming the generality of the proposed model in forecasting the spatiotemporal evolution of propeller wake.