Data-driven reduced-order modeling of hydrogen-fueled supersonic combustion

Efficient modeling and simulation of supersonic combustion processes are crucial in aerospace applications, requiring rapid prediction of complex multi-physics interactions in irregular computational domains. In this paper, we present a novel residual variational autoencoder-transformer (ResVAE-Trans) model, which is a data-driven method for dimensionality reduction and prediction of multi-physics fields in hydrogen-fueled supersonic combustion. The ResVAE projects high-dimensional dynamic systems onto a low-dimensional latent space, while the transformer constructs a reduced-order model within this space. Before applying the ResVAE-Trans model for dimensionality reduction and prediction, the proposed framework maps multi-physics data from irregular domains onto a structured grid and normalizes it. The framework is demonstrated through hydrogen-fueled supersonic combustion simulations of scramjet engines at the German Aerospace Center (DLR). This approach offers a solution for reduced-order modeling of multi-physics fields in irregular computational domains. Results show that the method successfully achieves dimensionality reduction and prediction of multi-physics fields. It enhances computational efficiency while maintaining prediction accuracy.