Separated flow prediction and assessment using LES and machine learning

Abstract

Large Eddy Simulation is a predictive technology that has the potential to revolutionise CFD. Significant effort is now being put into improving lower order models based on high fidelity data. The current work contrasts LES and RANS for a low Reynolds number ribbed channel flow relevant to turbine and electronics cooling. The anisotropy of turbulence is chosen as a starting point to compare RANS modelling deficiencies, and it is found that there are significant differences between the anisotropy predicted by RANS and LES. In the LES, a spreading shear layer introduces anisotropic content into the passage. Downstream of the rib, scouring eddies shed from the rib destroy the classical boundary layer flow. A machine learning classifier trained on a database of similar flows is used to predict the anisotropy in the ribbed passage. The classifier is shown to be capable of predicting many of the flow features identified in the LES, demonstrating the potential of such approaches for application to this category of flows.