Multi-path deep learning framework on discrete pressure points to predict velocity field of pump-jet propulsor

Abstract

The research has provided a fast and accurate deep learning framework, which can predict the wake velocity field of pump-jet propulsor based on discrete pressure points at different conditions. Once the model is trained, the velocity field can be obtained by discrete pressure points quickly, which the pressure point can be obtained by pressure sensors in actual engineering or experiments. The bicubic interpolation method and hybrid multi-path deep learning model were used to realize the spatiotemporal mapping relationship between pressure point and velocity fields. The predicted difference for different numbers of discrete pressure points and the probability distribution of difference were investigated. The differences between the predicted velocity field and the real flow field have been analyzed by the error cloud map. The structural similarity index (SSIM), correlation coefficient and peak signal noise ratio (PSNR) were used to show the prediction accuracy and generalization ability of the method. The results show that the predicted value of the velocity field is in good agreement with the CFD velocity field whether it is the velocity profile or the change trend of the velocity gradient at different conditions. The highest PSNR value and SSIM value reached 40.085 dB and 0.9902, and the correlation coefficient exceeded 0.96, which shows that the predicted value has a strong correlation with real field and it is convincing.