Physics-informed recurrent super-resolution generative reconstruction in rotating detonation combustor

The high-resolution flow field information is crucial for understanding flow systems. Nevertheless, due to limitations in sensor technology and measurement methods, it is challenging to obtain high-resolution data in engineering applications. This study introduces a novel model called physics-informed recurrent generative adversarial network (PIRGAN) to obtain super-resolution results from low-resolution inputs, which is an inverse problem that is difficult to solve using traditional computational fluid dynamics (CFD) methods. The model integrates recurrent framework and embedding physical constraints into its architecture to perform superresolution generative reconstruction. During the generation process, this model calculates temporal derivatives based on low-resolution inputs and then updates the previous timestep's results by temporal derivatives to obtain the current timestep's results. The proposed PIRGAN model is demonstrated through its application to the reconstruction of flow fields in Rotating Detonation Combustors (RDC). Notably, PIRGAN successfully reconstructs detailed flow structure inside RDC, which is often indiscernible in low-resolution or interpolated results. Besides, the introduction of a recurrent framework offers the model's ability to capture the temporal characteristics and handle noisy inputs. As a surrogate model that extends the measurement capabilities within reactive flow systems, the model introduced in this study holds noticeable engineering significance.