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.
Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation
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Weatheritt, J, Pichler, R, Sandberg, RD, Laskowski, G, & Michelassi, V. "Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation." Proceedings of the ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. Volume 2B: Turbomachinery. Charlotte, North Carolina, USA. June 26–30, 2017. V02BT41A015. ASME. https://doi.org/10.1115/GT2017-63497
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