Deep Learning parameterization of subgrid scales in wall-bounded turbulent flows

Understand

An innovative \textit{deep learning} approach has been adopted to formulate the eddy-viscosity for large eddy simulation (LES) of wall-bounded turbulent flows.

• A deep neural network (DNN) is developed which learns to evaluate the eddy-viscosity from a dataset generated for a channel flow at friction Reynolds number $Re_{\tau} = 395$ using the Dynamic Smagorinsky subgrid-scale model.
• Later this DNN is employed to predict the eddy-viscosity for a number of grid configurations for channel flow at $Re_{\tau} = 395$ and $590$.
• The statistics computed from the DNN based LES model show an excellent match with direct numerical simulations (DNS).