Fast prediction of flow field in scramjet combustor based on physical information neural network under wide Mach number

The numerical calculation method has greatly promoted the process of optimal design of scramjet, but it still needs extremely heavy calculation for the model with complex thermochemical reaction. Data-driven deep learning relies heavily on a large amount of data in the face of complex nonlinear features. Therefore, combining “data-driven model” and “Navier-Stokes equation”, an intelligent prediction model of supersonic combustion flow process is constructed. This algorithm integrates the theory priors of combustion flow into the neural network model, and uses convolutional grouping and rearrangement to reduce the feature redundancy calculation, so as to achieve high-precision and high-efficiency prediction of velocity, density, pressure and temperature fields. This study makes a comprehensive comparison from two aspects of performance and efficiency. Unsteady scramjet multi-physical field dataset is constructed under different incoming Mach number conditions. The experimental results show that compared with other methods, the proposed algorithm can achieve the maximum Peak Signal-to-Noise Ratio (PSNR) improvement of 38.75% and Learned Perceptual Image Patch Similarity (LPIPS) improvement of 68.13% in predicting the average quality of images, and the computational cost of the model is reduced by 30.36% compared with other models. In addition, the high model can also effectively predict the unknown incoming flow condition.