Conference Proceedings
DEVELOPMENT AND USE OF MACHINE-LEARNT ALGEBRAIC REYNOLDS STRESS MODELS FOR ENHANCED PREDICTION OF WAKE MIXING IN LPTS
Harshal D Akolekar, Jack Weatheritt, Nicholas Hutchins, Richard D Sandberg, Gregory Laskowski, Vittorio Michelassi
ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition | AMER SOC MECHANICAL ENGINEERS | Published : 2018
DOI: 10.1115/GT2018-75447
Abstract
Non-linear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, Reynolds-averaged Navier-Stokes (RANS) calculations using five linear turbulence closures were performed for the T106A LPT profile at exit Mach number 0.4 and isentropic exit Reynolds numbers 60,000 and 100,000. None of these RANS models were able to accurately reproduce wake loss profiles, a crucial parameter in LPT design, from direct numerical simulation (DNS) reference data. However, the recently proposed kv2w transition model was found to produce the best agreement with DNS data in terms of blade loading and boundary layer behavior and thus was se..
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Awarded by Australian Research Council
Funding Acknowledgements
This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. The support by the Australian Government Research Training Program Scholarship is acknowledged. The University of Melbourne authors also acknowledge the financial support and the permission to publish by General Electric.