A neural-network-based subgrid-scale model trained on backward-facing-step flow for large-eddy simulation of unseen turbulent flows

A neural-network (NN)-based subgrid-scale (SGS) model, trained only with data from flow over a backward-facing step (BFS), is assessed in large-eddy simulation (LES) of three untrained flows: turbulent channel flow, flow over a circular cylinder, and flow over an Ahmed body. The NN is trained to predict the SGS stress tensor from the filtered strain-rate tensor scaled by a Vreman-type eddy viscosity. To account for different orientations of boundary layers and separated regions in the test flows, the training dataset is augmented through streamwise-axis rotations. During training, the input and output variables are normalized by the free-stream velocity of the BFS flow, whereas spatially local normalization based on the true SGS stress magnitude does not provide improved predictions in the present LES. For the channel flow at Reτ≈393 and 710, the NN predicts the mean velocity profiles accurately, with rms velocity fluctuations similar to those from the dynamic Smagorinsky model (DSM) and the dynamic global model (DGM). For the circular-cylinder flow at Red=3900, the NN provides improved predictions of the recirculation length and streamwise velocity fluctuations compared with DSM and DGM. Furthermore, for the Ahmed body flow with a slant angle of 25° at ReH = 96 000, the NN reproduces the separation bubble on the slanted surface, with predictions similar to those from DGM for the drag coefficient and instantaneous vortical structures. These results suggest that BFS training data, combined with rotation-based augmentation, provide useful SGS information for developing an NN-based SGS model applicable to wall-bounded and separated flows.