Toward More General Turbulence Models via Multicase Computational-Fluid-Dynamics-Driven Training

The accuracy of machine-learned turbulence models often diminishes when applied to flow cases outside the training data set. In an effort to improve the predictive accuracy of data-driven models for an expanded set of cases, an extension of a computational fluid dynamics (CFD)-driven training framework consisting of three key steps is proposed. Firstly, a list of candidate flow-related parameters is selected to supplement Pope's general tensor basis hypothesis. Secondly, modeling an additional production term may benefit the overall predictions in certain situations. Finally, the Reynolds-averaged Navier-Stokes (RANS) evaluations of candidate models are performed on several different flows simultaneously during the model training iterations. Five free-shear and five wall-bounded flow cases are chosen to train or test data-driven turbulence models. It is shown that the machine-learned models from the present multicase CFD-driven framework can significantly improve the predictive accuracy for the test cases where the baseline RANS results showed significant error from the ground truth. Meanwhile, for cases in which the baseline produced good results, the new models do not perform worse. Further analysis shows that the new models can adapt to opposite trends of turbulent diffusion required for the different cases with a common correction. Moreover, the trained models can be simplified and still achieve similar improvement as the whole expressions.

Automated summary

In order to improve the predictive accuracy of data-driven models for a wider range of cases, an extension of a computational fluid dynamics (CFD)-driven training framework is proposed. This framework consists of three key steps, including selecting candidate flow-related parameters, modeling an additional production term, and performing Reynolds-averaged Navier-Stokes (RANS) evaluations on multiple different flows simultaneously during model training iterations. The results show that the machine-learned models from this multicase CFD-driven framework can significantly improve the prediction accuracy for test cases with significant errors from the baseline RANS results.