Robust experimental data assimilation for the Spalart-Allmaras turbulence model

The presented methodology fuses computational models with experimental data to enhance the Spalart-Allmaras (SA) turbulence model for Reynolds-averaged Navier-Stokes equations. By leveraging the Ensemble Kalman filtering approach (EnKF), this study refines the SA model's coefficients, ensuring improved performance on separated flows without any accuracy trade-off on flows already well captured by SA. Validated on different flow conditions, including a backward-facing step and a NASA wall-mounted hump, the recalibrated model demonstrates significant improvements in key metrics.