Data-enabled, progressive recalibration of the Spalart-Allmaras model for general purposes

We develop data-enabled augmentation for the Spalart-Allmaras (SA) model. Our approach is progressive. We successively consider free shear flow, log layer, and wall layer. The training data is limited to channel flow and Couette-Poiseuille flows. The process makes use of Bayesian optimization and feed-forward neural networks. We test the resulting model in 10 flows, all outside the training dataset. We show that the data-enabled augmentation does not "break" the baseline model: Galilean invariance and the law of the wall are preserved. Furthermore, the resulting augmentation is "universal": it does not lead to degradation in free shear flows and gives more accurate results in wall-bounded flows. Specifically, the present augmentation gives more accurate predictions of the mean flow in the buffer layer and the skin friction coefficients in the wall-mounted hump and the back-facing step cases. Last, an attempt is made to explain the observed improvements in separated flows physically.