Data-driven augmentation of a RANS turbulence model for transonic flow prediction
International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Article publication date: 17 April 2023
Issue publication date: 24 April 2023
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
Keywords
Acknowledgements
This work was funded by the sixth Federal Aeronautical Research Programme Germany in the project DIGIFly – Digital Flight of Air Vehicles under grant number 20X1909A. The authors are grateful to AIRBUS for providing the RWC.01 aerodynamic database.
Citation
Grabe, C., Jäckel, F., Khurana, P. and Dwight, R.P. (2023), "Data-driven augmentation of a RANS turbulence model for transonic flow prediction", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 33 No. 4, pp. 1544-1561. https://doi.org/10.1108/HFF-08-2022-0488
Publisher
:Emerald Publishing Limited
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