A data-driven approach using CNN for wall modeling in Large Eddy Simulation

Wall modeling in large eddy simulation (LES) is of great importance as it can make the computational cost of the LES of wall-bounded flows remarkably lower as there will be no need to resolve the near-wall region. One of the methods with computationally low cost for wall modeling in LES is the approximate boundary condition which applies the wall shear stress as the boundary condition at the wall. However, in this approach it is crucial to find an accurate model for the wall shear stress. Due to the ability of the data-driven approaches in extracting features, they can be considered as a proper candidate for deriving a model for the wall shear stress. Data-driven approaches have already been widely used for subgrid-scale (SGS) modeling in LES; however, there are not many attempts at wall-modeling in LES using a data-driven approach. In this work, a study has been performed on wall-modeling in LES using convolutional neural network (CNN). Initially, a wall model is developed using the data of channel flow at Re_τ= 400. Then, the model is tested for the channel flow at higher Reynolds number. The results show that the model has a reasonable accuracy in predicting the wall shear stress and establishing a wall model.