Prediction of shock and boundary layer interaction in supersonic/hypersonic flow over a compression ramp using deep neural networks

This study investigates the accurate prediction of supersonic and hypersonic flow fields over a compression ramp using deep neural networks. While deep learning methods have demonstrated effectiveness in flow field prediction, challenges remain in resolving fine-scale features characteristic of supersonic and hypersonic flows, such as Shock Wave Boundary Layer Interaction (SWBLI). To address this, a flow field modeling method using Vision Transformer (ViT) and U-Net Convolutional Neural Network (CNN) based on the coordinate transformation is employed. This strategy reduces information loss near the wall region and enhances the prediction accuracy of boundary layer flow fields. Meanwhile, a comparative analysis between the two surrogate models reveals that ViT outperforms U-Net CNN applied in this study, achieving reductions in errors of 72.6 % and 69.5 % for streamwise and normal velocities, respectively. Furthermore, physics-informed loss functions – including wavelet loss and pressure gradient-related loss – are introduced to improve prediction accuracy in shock-induced boundary layer separation and reattachment regions. The results demonstrate that models incorporating physics-informed losses capture more detailed flow features; however, discontinuities between adjacent patches still impose limitations on accuracy. To overcome this, the proposed patch prior method effectively addresses patch discontinuity issues, enabling accurate wall pressure predictions while maintaining a separation length error of approximately 6 % compared to Computational Fluid Dynamics (CFD) results. Overall, the findings indicate that the developed model possesses strong capability in predicting supersonic and hypersonic flow fields over compression ramps.