A physics-constrained and data-driven method for modeling supersonic flow

Abstract

A fast solution of supersonic flow is one of the crucial challenges in engineering applications of supersonic flight. This article introduces a deep learning framework, the supersonic physics-constrained network (SPC), for the rapid solution of unsteady supersonic flow problems. SPC integrates deep convolutional neural networks with physics-constrained methods based on the Euler equation to derive a new loss function that can accurately calculate the flow fields by considering the spatial and temporal characteristics of the flow fields at the previous moment. Compared to purely data-driven methods, SPC significantly reduces the dependency on training data volume by incorporating physical constraints. Additionally, the training process of SPC is more stable than that of data-driven methods. Taking the classic supersonic forward step flow as an example, SPC can accurately calculate strong discontinuities in the flow fields, while reducing the data volume by approximately 60%. In the generalization test experiment for forward step flow and compression ramp flow, SPC also demonstrates good predictive accuracy and generalization capability under different geometric configurations and inflow conditions.