Data-Enabled Recalibration of the Spalart–Allmaras Model

We use experimental and simulation data to recalibrate the standard Spalart–Allmaras model. Free-shear flow, the buffer layer, the log layer, and flows with adverse pressure gradients are targeted. In this process, the recalibration does not affect untargeted flows. Our approach uses Bayesian optimization and feedforward neural networks. The recalibrated model is implemented in two codes and tested in 11 flows: the majority of which are outside the training dataset and have geometries that are distinctly different from those in the training dataset. We show that the data-enabled recalibration offers improvements while preserving the model’s existing good behavior. In particular, our recalibration improves the model’s behavior in separated flows while preserving its existing good behaviors in flatplate boundary-layer flows and channel flows. Further analysis indicates that the improvements in separated flow are mainly due to the recalibrated f_w function and the resulting, more precise representation of the “slingshot” effect.