Physics-Guided Prior Graph Neural Network for Chemical Nonequilibrium Aerodynamic Heating Prediction
Abstract
Accurate and efficient prediction of chemical nonequilibrium aerodynamic heating remains a fundamental challenge in spacecraft design. High-fidelity computational fluid dynamics (CFD) simulations are computationally prohibitive due to stiff chemical source terms, whereas purely data-driven approaches often lack physical interpretability and generalize poorly across varying catalytic wall conditions. To address these limitations, a physics-guided prior graph neural network is proposed. The central idea of the proposed framework is a physics-based multifidelity fusion strategy, in which a computationally inexpensive perfect-gas solution is employed as a low-fidelity physical model before capturing the topological structure of the flowfield. A dual-branch graph-based architecture is constructed to explicitly decouple the total wall heat flux into a conductive component, governed primarily by macroscopic thermal gradients, and a diffusive component driven by surface catalytic reactions. Numerical experiments conducted on double-ellipsoid and lifting-body configurations indicate that the proposed framework provides a sevenfold speedup relative to chemical-nonequilibrium CFD simulations while maintaining high predictive fidelity. The model exhibits robust performance even under complex regimes involving Mach number and wall temperature extrapolation alongside catalytic efficiency interpolation. This work effectively bridges the gap between physical mechanism decoupling and data-driven efficiency, offering a robust tool for rapid aerothermal design.
