Conference Proceedings

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

Jack Weatheritt, Richard Pichler, Richard D Sandberg, Gregory Laskowski, Vittorio Michelassi

Proceedings of the ASME Turbo Expo: Turbine Technical Conference and Exposition, 2017, VOL 2B | American Society of Mechanical Engineers | Published : 2017

Abstract

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 ..

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University of Melbourne Researchers

Grants

Awarded by Office of Science of the U.S. Department of Energy


Funding Acknowledgements

We acknowledge the funding provided by veski. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work was also supported by resources provided by The Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.