Machine learning-augmented turbulence modeling for RANS simulations of massively separated flows

We combine data assimilation and machine learning to correct the RANS Spalart-Allmaras turbulence model. The final neural-network contribution is a Boussinesq-correction, rather than turbulent eddy-viscosity adjustment. Flows over periodic hills at distinct Reynolds numbers geometries were selected demonstrate potential gain of learning-augmented models.