Flow field prediction of S-shaped shock vectoring nozzle with rear deck based on deep learning

Abstract

The S-shaped shock wave vectoring nozzle with afterdeck can significantly improve the overall performance of the exhaust system, taking into account Thrust vectoring, infrared stealth and afterbody fusion. One of the technical difficulties in its design process lies in the complex flow field characteristics under different operating conditions. Currently, the mainstream method is to obtain nozzle flow field characteristics through CFD numerical simulation, but the CFD method is time-consuming and costly. Therefore, based on the depth learning principle, a depth Convolutional neural network based on U-NET framework is established to quickly predict the flow field of S-shaped shock wave vectoring nozzle with afterdeck. Using CFD data for training, the results show that the depth learning model has high prediction accuracy and can clearly predict the flow field characteristics inside the nozzle, especially the secondary flow and the complex wave structure near the afterdeck. The correction Coefficient of determination of the prediction model is greater than 0.97. And the time consumption is about 0.0689% of that of a conventional solver. It has good application prospects in quickly evaluating the flow field of S-shaped nozzles.