Data-driven augmentation of a RANS turbulence model for transonic flow prediction
Cornelia Grabe (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Florian Jäckel (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Parv Khurana (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
R. P. Dwight (TU Delft - Aerodynamics)
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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.