Application of Multitask Learning for Enhancement of Spalart-Allmaras Turbulence Model

The chapter focuses on enhancing the Spalart-Allmaras turbulence model using multitask learning, addressing its limitations in high Reynolds number flows. By introducing a data-driven correction term, the model's predictive accuracy is significantly improved. The methodology involves solving inverse problems to estimate optimal correction functions and training a machine learning model to predict these corrections in real time. The framework is validated through numerical simulations on different airfoil configurations, demonstrating substantial improvements in lift coefficient predictions. The approach is not only applicable to the Spalart-Allmaras model but can be extended to other turbulence models, making it a promising advancement in the field of computational fluid dynamics.