Constrained Recalibration of Reynolds-Averaged Navier-Stokes Models

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 recalibrating the baseline model to accommodate a specific application. This approach is colloquially known as the rubber-band approach, which we formally call constrained model recalibration in this paper. To illustrate the efficacy of the approach, we identify the degrees of freedom in the Spalart-Allmaras 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.

Automated summary

This paper introduces a constrained model recalibration method in RANS turbulence models to address the negative impact of modifications on basic calibrations. By identifying the degrees of freedom that do not affect the basic calibrations and only modifying them when necessary, models that perform well in historically challenging flow scenarios can be trained.