  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
re-calibrating the baseline model to accommodate a specific application. This
approach is colloquially known as the "rubber-band" approach, which we formally
call "constrained model re-calibration" in this article. To illustrate the
efficacy of the approach, we identify the degrees of freedom in the
Spalart-Allmaras (SA) model that do not affect the log law calibration. By
subsequently 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. In addition to good performance inside the training
dataset, the trained models yield similar performance as the baseline model
outside the training dataset.
