Conference Proceedings
IMPROVING A TWO-EQUATION EDDY-VISCOSITY TURBULENCE MODEL FOR HIGH-RAYLEIGH-NUMBER NATURAL-CONVECTION FLOWS USING MACHINE LEARNING
A Haghiri, X Xu, RD Sandberg, K Tanimoto, T Oda
Proceedings of the ASME Turbo Expo | AMER SOC MECHANICAL ENGINEERS | Published : 2024
Abstract
This study presents data-driven modelling of the Reynolds stress tensor and turbulent heat flux vector for improving unsteady RANS (Reynolds-averaged Navier Stokes) predictions of natural convection problems. While RANS-based calculations are cost-effective, conventional models fail to deliver the requisite predictive precision for high-Rayleigh-number practical engineering flows. To rectify this limitation, a gene-expression programming (GEP)based machine-learning technique was employed to train novel models using a high-fidelity dataset from a vertical cylinder case with Ra=O(1013), which was generated using LES and validated against experimental data from Mitsubishi Heavy Industries (MHI)..
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Awarded by Mitsubishi Heavy Industries
Funding Acknowledgements
The University of Melbourne authors wish to acknowledge Mitsubishi Heavy Industries, Ltd. for their financial support and permission to publish. Funding from the Australian Research Council (ARC) is acknowledged, through the linkage project LP180100712 and the future fellowship FT190100072. The simulations conducted were enabled by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia under the National Computational Merit Allocation Scheme, under project bq2 (Highfidelity simulations of turbulent flows in power generation and transport).