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

Journal article (2023)

Authors

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 Aerodynamics - Aerospace Engineering
Research Group
Aerodynamics (Aerospace Engineering) (TU Delft)
Copyright: © 2023 Cornelia Grabe, Florian Jäckel, Parv Khurana, R.P. Dwight
DOI: https://doi.org/10.1108/HFF-08-2022-0488
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Published Date

2023

Language

English

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Faculty

Aerospace Engineering

Department

Flow Physics and Technology

Research Group

Aerodynamics

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.

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