Cornelia Grabe, Florian Jäckel, Parv Khurana and Richard P. Dwight
This 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…
Abstract
Purpose
This 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/approach
The 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.
Findings
Optimal 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/value
A 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