Data-driven augmentation of a RANS turbulence model for transonic flow prediction

Abstract

PurposeThis paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML model.Design/methodology/approachThe field inversion and machine learning (FIML) approach is applied to the negative Spalart-Allmaras turbulence model for transonic flows over an airfoil where shock-induced separation occurs.FindingsOptimal input features and an ML model are developed, which improve the existing negative Spalart-Allmaras turbulence model with respect to shock-induced flow separation.Originality/valueA comprehensive workflow is demonstrated that yields insights on which input features and which ML model should be used in the context of the FIML approach