Machine Learning to Improve RANS Turbulent Kinetic Energy Transport Equation

Conventional Reynolds Averaged Navier-Stokes (RANS) models are not predictive for 3D separated flows. Inaccuracies in the predicted turbulent kinetic energy are a major source of error in the computed Reynolds stresses. A neural network machine learning approach is used to improve the realizable k-epsilon model by modifying terms in the turbulent kinetic energy transport equation. The network is trained on Large Eddy Simulation (LES) data of a smooth three-dimensional bump flow and is coupled in a RANS solver to continually update the model predictions as the solution converges. Inputs to the model are complex invariant functions of the strain rate, rotation rate, and wall distance Reynolds number. The machine-learned model is tested on a wall-mounted cube and shows improved turbulent kinetic energy and mean velocities compared to the baseline RANS. Additional comparisons in other separated flows are underway.