A data-driven wall model for LES of flow over periodic hills

In wall-modeled large-eddy simulation (WMLES), wall models are often employed to provide wall shear stress for outer flow simulations. However, conventional wall models based on the equilibrium hypothesis are not able to accurately predict the wall shear stress for flows with separation and reattachment. In this work, we propose a data-driven wall model based on the physics-informed feedforward neural network (FNN) and wall-resolved LES (WRLES) data for flow over periodic hills. In the proposed FNN wall model, we employ the wall-normal distance, near-wall velocities and pressure gradients as input features and the wall shear stresses as output labels, respectively. The trained FNN wall model is applied to different snapshots and spanwise slices for both training and testing datasets. For the instantaneous wall shear stress, the correlation coefficients between the predicted results and WRLES data are larger than 0.6 and the relative errors are smaller than 0.3 at most streamwise locations. For the time-averaged wall shear stress, the predictions from the FNN wall model and the WRLES data agree well with each other for both training and testing datasets, demonstrating the outstanding generalization capacity of the FNN wall model.