Flow-field reconstruction in rotating detonation combustor based on physics-informed neural network

Abstract

The flow-field reconstruction of a rotating detonation combustor (RDC) is essential to understand the stability mechanism and performance of rotating detonation engines. This study embeds a reduced-order model of an RDC into a neural network (NN) to construct a physics-informed neural network (PINN) to achieve the full-dimensional high-resolution reconstruction of the combustor flow field based on partially observed data. Additionally, the unobserved physical fields are extrapolated through the NN-embedded physical model. The influence of the residual point sampling strategy and observation point spatial-temporal sampling resolution on the reconstruction results are studied. As a surrogate model of the RDC, the PINN fills the gap that traditional computational fluid dynamics methods have difficulty solving, such as inverse problems, and has engineering value for the flow-field reconstruction of RDCs.