Data-driven turbulence model for flow separation over the Boeing Gaussian Bump

Existing RANS models have struggled to predict the smooth-body separation on the Boeing Gaussian Bump. We create a new RANS model that is based on a transport equation for the RANS eddy viscosity. We use a neural network to learn the eddy viscosity source terms using training data from DNS and experimental data published in the literature. We further discuss the applicability of this effort to develop models that generalize to predicting flow separation for airfoils at angles of attack near the stall condition.