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Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation

[+] Author Affiliations
Jack Weatheritt, Richard Pichler, Richard D. Sandberg

University of Melbourne, Parkville, Australia

Gregory Laskowski

GE Aviation, Lynn, MA

Vittorio Michelassi

General Electric Oil & Gas, Florence, Italy

Paper No. GT2017-63497, pp. V02BT41A015; 12 pages
  • ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition
  • Volume 2B: Turbomachinery
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5079-4
  • Copyright © 2017 by ASME


The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By increasing the coefficient of the linear term, the farwake prediction shows minor improvement. By using additional non-linear terms in the stress-strain coupling relationship, created by the evolutionary algorithm, the near-wake can also be improved upon. Terms created by the algorithm are scrutinized and the discussion is closed by suggesting a tentative non-linear expression for the Reynolds stress, suitable for the wake behind a high-pressure turbine blade.

Copyright © 2017 by ASME



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