Super-resolution reconstruction and prediction of flow fields based on deep learning and modal decomposition

The flow field obtained through numerical simulations often exhibits distortion after data processing steps, such as super-resolution (SR) reconstruction or flow field prediction. This study presents a flow field reconstruction method based on deep learning. The physics-informed convolutional neural network (PICNN) model, combining the convolutional neural network (CNN) and the physics-informed neural network (PINN), is specifically designed to address distortion issues present in data processing. The study shows that in the SR reconstruction of the square cavity flow field, the PICNN model increases the resolution of the velocity field by 16, 36, 64, and even 256 times with an error range significantly superior to traditional interpolation methods. However, in regions where the velocity changes are particularly abrupt, the super-resolution reconstruction performance of the PICNN model is suboptimal. At the same time, combined with the sparsity promoting dynamic mode decomposition (SPDMD) algorithm, the PICNN model significantly optimizes the flow field prediction of the SPDMD algorithm, even in the case of a small number of retained modes.