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research-article

Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a fundamental Trailing Edge Slot

[+] Author and Article Information
Richard Sandberg

Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
richard.sandberg@unimelb.edu.au

Raynold Tan

Department of Mechanical Engineering, University of Melbourne Parkville, Victoria 3010 Australia
yiyunt@student.unimelb.edu.au

Jack Weatheritt

Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
jack.weatheritt@unimelb.edu.au

Andrew Ooi

Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
a.ooi@unimelb.edu.au

Ali Haghiri

Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
haghiri.a@unimelb.edu.au

Vittorio Michelassi

Baker Hughes, a GE Company, Florence 50127, Italy
vittorio.michelassi@bhge.com

Gregory M. Laskowski

General Electric Aviation, Lynn, MA 01905, USA
laskowsk@ge.com

1Corresponding author.

ASME doi:10.1115/1.4041268 History: Received August 15, 2018; Revised August 21, 2018

Abstract

Machine learning was applied to LES data to develop nonlinear 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 non-linear Explicit Algebraic Stress Models (EASM) and heat-flux closures based on either the gradient diffusion or the generalized gradient diffusion approaches. Steady RANS calculations were then conducted with the new EASM models. The best overall agreement with LES data was found when selecting the near wall region, where high levels of anisotropy exist, as training region, and using the mean squared error of the anisotropy tensor as cost function. For the thin lip geometry, the adiabatic wall effectiveness was predicted in good agreement with LES and experimental data when combining the GEP-trained model with the standard eddy-diffusivity model. Crucially, the same model combination also produced significant improvement in the predictive accuracy of adiabatic wall effectiveness for different blowing ratios, despite not having seen those in the training process. For the thick lip case, the match with reference values deteriorated due to the presence of large-scale, relative to slot height, vortex shedding. A GEP-trained scalar flux model, in conjunction with a trained RANS model, was found to significantly improve the prediction of the adiabatic wall effectiveness.

Copyright (c) 2018 by ASME
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