A novel model with an improved loss function to predict the velocity field from the pressure on the surface of the hydrofoil

Abstract

Reconstructing the flow field from limited observations is critical, as it can be used in marine applications which monitor flow changes with limited observations. With the development of deep learning, prediction models based on neural networks have been used for rapid prediction of flow fields. The work proposes models based on U-structure networks (U-net) and Signed Distance Function (SDF) to predict the velocity field from sparse pressure on the surface of the hydrofoil. An improved loss function is employed to improve the prediction accuracy. Through training and testing on a numerical simulation dataset, it is found that the prediction of models has a good agreement with the true values. The improved loss function redirects attention of the model to areas with poor prediction accuracy near the surface of the hydrofoil. For the dataset unknown to models, the prediction ability of the model is limited as input features are not enough to support strong generalization.