A rubber-band approach to Reynolds-averaged Navier-Stokes modeling

The constants and functions in Reynolds-averaged Navier Stokes (RANS) turbulence models are coupled. Consequently, modifications of a RANS model often negatively impact its basic calibrations, which is why machine-learned augmentations are often detrimental outside the training dataset. A solution to this is to identify the degrees of freedom that do not affect the basic calibrations and only modify these identified degrees of freedom when "stretching" the baseline model to accommodate a specific application. This approach is termed the rubber-band approach. For illustration purposes, we identify the degrees of freedom in the Spalart-Allmaras (SA) model that do not affect the log law calibration. By interfacing data-based methods with these degrees of freedom, we train models to solve historically challenging flow scenarios, including the round-jet/plane-jet anomaly, airfoil stall, secondary flow separation, and recovery after separation. The trained models perform similarly to the baseline model outside the training dataset.