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

Tong Zhao, Jian An, Yuming Xu, Guoqiang He, Fei Qin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Article number066118
JournalPhysics of Fluids
Volume36
Issue number6
DOIs
StatePublished - 1 Jun 2024

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