A data-driven wall model for the prediction of turbulent flow separation over periodic hills

Direct Numerical Simulations and even wall resolved Large Eddy Simulations remain computationally intractable for full blade span computations when going to large Reynolds numbers. The cost of representing turbulence near the wall motivates the development of wall-modeled LES. The present work proposes the use of Deep Neural Nets (DNN) to link the wall shear stress components to volume data extracted at multiple wall-normal distances $h_{wm}$ and wall-parallel locations. The developed data-driven wall model focuses on the prediction of separation, which is a frequently observed phenomenon in modern low-pressure turbines. The model is trained using a high-fidelity database of the two-dimensional periodic hill flow, which exhibits separation and is affordable to compute on modern clusters.