Convolutional Neural Network Predictions for Unsteady Reynolds-Averaged Navier–Stokes-Based Numerical Simulations

Abstract

The application of computational fluid dynamics (CFD) to turbulent flow has been a considerable topic of research for many years. Nonetheless, using CFD tools results in a large computational cost, which implies that, for some applications, CFD may be unviable. To date, several authors have carried out research applying deep learning (DL) techniques to CFD-based simulations. One of the main applications of DL with CFD is in the use of convolutional neural networks (CNNs) to predict which samples will have the desired magnitude. In this study, a CNN which predicts the streamwise and vertical velocities and the pressure fields downstream of a circular cylinder for a series of time instants is presented. The CNN was trained using a signed distance function (SDF), a flow region channel (FRC) and the t-1 sample as inputs, and the ground-truth CFD data as the output. The results showed that the CNN was able to predict multiple time instants with low error rates for turbulent flows with variable input velocities to the domain.