Dual-path flow field reconstruction for a scramjet combustor based on deep learning

A flow field reconstruction algorithm based on deep learning is an effective method to detect the evolution of wave structure in a scramjet combustor, which is of great significance for accurately predicting the operating performance of the scramjet. This paper proposes a dual-branch fusion model based on a multi-head attention mechanism to reconstruct the flow field schlieren image in a supersonic combustor. The proposed model adopts a dual-path fusion mode. One branch is composed of transposed convolution and conventional convolution, forming a symmetrical structure for dimension enhancement and feature extraction. The other is formed by a multi-head attention mechanism and a full connection layer in series. It utilizes the same attention mechanism to obtain different sensitive features and enhance the global model perception. The proposed model was trained and tested on a dataset constructed from hydrogen-fueled scramjet experiments in a direct-connected supersonic pulse combustion wind tunnel at Mach 2.5. Numerous experiments prove that the model can effectively reconstruct the basic wave system structure of a complex flow field, and it is in good agreement with the original flow field. The average peak signal-to-noise ratio, structural similarity, and average linear correlation coefficient of the proposed model are reached 20.92, 0.602, and 0.943, respectively, which verify the effectiveness of the proposed model in reconstructing the supersonic flow field.

Automated summary

This paper proposes a flow field reconstruction algorithm based on deep learning, which utilizes a dual-branch fusion model with a multi-head attention mechanism to accurately reconstruct the flow field structure in a supersonic combustor. It has great significance for predicting the operating performance of a scramjet engine.