Improving the Vector Basis Neural Network for RANS Equations Using Separate Trainings

Abstract

We present a new data-driven turbulence model for Reynolds-averaged Navier-Stokes equations called ${\nu }_t$-Vector Basis Neural Network. This new model, grounded on the already existing Vector Basis Neural Network, predicts separately the turbulent viscosity ${\nu }_t$ and the contribution of the Reynolds force vector that is not already accounted in ${\nu }_t$. Numerical experiments on the flow in a Square Duct show the better accuracy of the new model compared to the reference one.