Journal article

RANS turbulence model development using CFD-driven machine learning

Y Zhao, HD Akolekar, J Weatheritt, V Michelassi, RD Sandberg

Journal of Computational Physics | Elsevier | Published : 2020

Abstract

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method Weatheritt and Sandberg (2016) [8], but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated way, rather than using an algebraic function. Unlike other data-driven methods that fit the Reynolds stresses of trained models to high-fidelity data, the cost function for the CFD-driven training can be defined based on any flow feature from the CFD results. This extends the applicability of the method especially when the training data is limited..

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Grants

Awarded by DOE Office of Science User Facility


Awarded by Swiss National Supercomputing Centre (CSCS)


Funding Acknowledgements

This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID s884. This work was also supported by the resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.