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

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

Grabe, Cornelia (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR))
Jäckel, Florian (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR))
Khurana, Parv (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR))
Dwight, R.P. (TU Delft Aerodynamics)

2023

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.

Data-driven turbulence modeling
Feature selection
Flow separation
Machine learning
RANS
Transonic flows

http://resolver.tudelft.nl/uuid:8db07b55-4656-443d-b08f-695affda1cd0

https://doi.org/10.1108/HFF-08-2022-0488

2023-07-10

0961-5539

International Journal of Numerical Methods for Heat and Fluid Flow, 33 (4), 1544-1561

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Cornelia Grabe, Florian Jäckel, Parv Khurana, R.P. Dwight