Machine learning-augmented turbulence modeling for RANS simulations of massively separated flows
Pedro Stefanin Volpiani, M. Meyer, Lucas Franceschini, Julien Dandois, Florent Renac, E. Dale Martin, Olivier Marquet, Denis Sipp
Abstract
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 a turbulent eddy-viscosity adjustment. Flows over periodic hills at distinct Reynolds numbers and geometries were selected to demonstrate the potential gain of machine learning-augmented turbulence models.
Topics & Concepts
Reynolds-averaged Navier–Stokes equationsTurbulenceTurbulence modelingArtificial neural networkReynolds stress equation modelK-epsilon turbulence modelMechanicsLarge eddy simulationComputer scienceStatistical physicsPhysicsK-omega turbulence modelMeteorologyArtificial intelligenceFluid Dynamics and Turbulent FlowsModel Reduction and Neural NetworksMeteorological Phenomena and Simulations