Transient velocity field prediction and uncertainty quantification of pump-jet propulsor using variational Bayesian neural networks

Abstract

The study provides a fast and accurate variational Bayesian convolution neural network (VB–CNN) framework to obtain the pump-jet propulsor (PJP) transient velocity field. In practical engineering or experiments, it can obtain the transient velocity field of PJP only by discrete pressure points for different times conditions. Compared with the computational fluid dynamics (CFD) method, it can save a lot of computing time and computing resources. This study also uses the VB-CNN method to solve the problem of poor performance of CNN in small data-sets, VB-CNN method can consider the prior knowledge of PJP transient data to improve the prediction accuracy. The predicted values, errors and variances of the PJP transient wake velocity field and stator domain at different times and different rotating speed conditions are investigated. The uncertainty of the transient flow field is analyzed using 95% confidence intervals. It also uses GPU technology to improve training speed. The research results show that VB-CNN and CNN methods can better predict the PJP transient flow field, while the VB-CNN method has higher accuracy than CNN method. The velocity gradient and velocity profile of the PJP transient velocity field are predicted accurately, the predicted velocity field is in good agreement with the CFD flow field. All linear correlation coefficients exceeds 0.99. The value of structural similarity index (SSIM) at different times exceeds 0.95.