Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients
International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Article publication date: 13 June 2024
Issue publication date: 2 September 2024
Abstract
Purpose
Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.
Design/methodology/approach
The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.
Findings
The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.
Originality/value
To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.
Keywords
Acknowledgements
This work was funded by the National Sciences and Engineering Research Council of Canada (NSERC) and the Tyler Lewis Clean Energy Research Foundation (TLCERF). The authors thank S. Rezaeiravesh (University of Manchester) for useful discussion regarding the presently proposed techniques, and W. Melek (University of Waterloo) for his ongoing feedback on this work. Authors also thank the four anonymous peer reviews for their careful review and insightful comments.
Citation
McConkey, R., Kalia, N., Yee, E. and Lien, F.-S. (2024), "Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 34 No. 8, pp. 2986-3016. https://doi.org/10.1108/HFF-12-2023-0726
Publisher
:Emerald Publishing Limited
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