Reconstruction of Supersonic Flowfield Using Physical Neural Network Based on Channel Interaction

Abstract

The evolution of shock waves in a combustor is a complex process comprising multiple time series, which results in a long computational cycle for unsteady numerical simulations. Current data-driven methods integrating physical information provide novel opportunities to rapidly and accurately predict the flowfield in the combustor. In this study, we construct a dataset under Mach numbers from 2 to 5 based on a self-designed scramjet combustor model and propose a physical convolutional neural network flowfield reconstruction method based on channel interaction (PICNNCI). This method embeds Euler equations that characterize the flow characteristics of inviscid fluids as physical constraints to solve the low-accuracy problem of traditional convolutional neural networks arising due to insufficient prior information. The model first performs dimensional transformations on temporal and spatial information input and converts a one-dimensional column vector into a two-dimensional vector to meet the convolution operation rules. Then, convolution is used for feature extraction to output the predicted flowfield, including velocity, pressure, and density fields. Finally, the predicted flowfield is optimized using the Euler equation and compared with the results obtained by numerical calculation and traditional neural network methods. The average peak signal-to-noise ratio of PICNNCI reaches 34.01 dB, and the linear correlation reaches 0.994.