Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils

摘要

A modeling paradigm is developed to augment predictive models of turbulence by effectively using limited data generated from physical experiments. The key components of the current approach involve inverse modeling to infer the spatial distribution of model discrepancies and machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. The methodology is applied to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart-Allmaras model using adjoint-based full-field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks and embedded within a standard solver, it is shown that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The neural-network-augmented Spalart-Allmaras model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial, finite element solver. The broader vision is that, by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.