Prediction modeling of unsteady flow field aimed at high-order DG numerical scheme

As a numerical method, the high-order discontinuous Galerkin (DG) method has the characteristics of high precision and is suitable for complex geometries. Meanwhile, due to its good dispersion and dissipation properties, the high-order DG method is well suited for implicit large eddy simulations. However, it usually takes a long time for solving unsteady flow fields, and how to reduce the computation cost is still a challenge. To tackle this issue, a deep neural network consisted with the three-dimensional convolution, the two-dimensional residual network and the attention mechanism has been proposed, which can extract the implied spatio-temporal characteristics of the flow field from the data. The numerical simulation for flow around a cylinder at different Reynolds numbers is carried out to obtain the data set for training, which is then used to predict the flow field for the future period. The results show that the deep neural network has a satisfactory ability of modeling the flow around a cylinder. The flow fields predicted by the deep neural network is in good agreement with those directly calculated by the CFD solver.