Turbulence is the most dominant characteristic of a turbulent flow. Therefore, successful modeling of turbulence can significantly improve the results of numerical simulation. Large Eddy Simulation (LES) computation of turbulent flows has been achieved a great attention recently since post-processing of LES results yields information of both mean flow and statistics of resolved fluctuations which is unique to LES and hence can model flows where persistent large-scale vortices results in flow development, e.g. flow behind bluff bodies, tumble swirl in engine combustion chambers and prediction of noise from high-speed flows (Versteeg 2009). However, this requires to address some issues to control the error sources and generate robust LES methodology for industrial applications.

In this study, a deep-learning approach is used to augment existing LES models using the relevant flow features. To this end, Random Forest Regression is developed to map relevant statistical flow-features within the LES solution to errors in calculated statistics such as sub-grid scale stresses. In this context, the exact solution is given by Direct Numerical Simulation (DNS) data. The capability of the proposed framework is examined by posteriori tests.