Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot
RD Sandberg, R Tan, J Weatheritt, A Ooi, A Haghiri, V Michelassi, G Laskowski
Proceedings of the ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition (GT2018) | American Society of Mechanical Engineers | Published : 2018
A form of supervised machine learning was applied to highly resolved large-eddy simulation (LES) data to develop non linear turbulence stress and heat flux closures with increased prediction accuracy for trailing-edge cooling slot cases. The LES data were generated for a thick and a thin trailing-edge slot and shown to agree well with experimental data, thus providing suitable training data for model development. A Gene Expression Programming (GEP) based algorithm was used to symbolically regress novel nonlinear Explicit Algebraic Stress Models (EASM) and heat-flux closures based on either the gradient diffusion or the generalized gradient diffusion approaches. Following a-priori assessment,..View full abstract
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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 University of Melbourne authors also acknowledge the financial support and the permission to publish by General Electric.