Flow field reconstruction and shock train leading edge position detection of scramjet isolation section based on a small amount of CFD data

Abstract

AbstractScramjet is the main power device of hypersonic vehicles. With the gradual expansion of wide velocity domain, shock wave/shock wave and shock wave/boundary layer are the main phenomena in scramjet isolator. When the leading edge of the shock train is pushed out from the inlet of the isolator, the engine will not start. Therefore, it is very important to detect the flow field structure in the isolator and the leading edge position of the shock train. The traditional shock train detection methods have low detection accuracy and slow detection speed. This paper describes a method based on deep learning to reconstruct the flow field in the isolator and detect the leading edge of the shock train. Under various back pressure conditions, the flow field images of computational fluid dynamics (CFD) data and the corresponding upper and lower wall pressure data were obtained, and a data set corresponding to pressure and flow field was constructed. By constructing and training convolutional neural networks, a mapping model with pressure information as input and flow field image as output is obtained, and then the leading edge position of shock train is detected on the output flow field image. The experimental results show that the average structure similarity (SSIM) between the reconstructed flow field image and the CFD flow field image is 0.902, the average peak signal-to-noise ratio (PSNR) is 25.289, the average correlation coefficient (CORR) is 0.956, and the root mean square error of shock train leading edge detection is 3.28 mm. Moreover, if the total pressure input is appropriately reduced, the accuracy of flow field reconstruction does not decline significantly, which means that the model has a certain robustness. Finally, in order to improve the detection accuracy of the leading edge position, we fine tuned the model and obtained another detection method, which reduced the root mean square error of the detection results to 1.87 mm.