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research-article

Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in LPTs

[+] Author and Article Information
Harshal D Akolekar

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

Jack Weatheritt

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

Nick Hutchins

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

Richard Sandberg

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

Gregory M. Laskowski

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

Vittorio Michelassi

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

1Corresponding author.

ASME doi:10.1115/1.4041753 History: Received October 03, 2018; Revised October 14, 2018

Abstract

Non-linear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, RANS calculations using five linear turbulence closures were performed for the T106A LPT profile at isentropic exit Reynolds numbers 60,000 and 100,000. None of these RANS models were able to accurately reproduce wake loss profiles, a crucial parameter in LPT design, from direct numerical simulation (DNS) reference data. However, the recently proposed kv2? transition model was found to produce the best agreement with DNS data in terms of blade loading and boundary layer behavior and thus was selected as baseline model for turbulence closure development. Analysis of the DNS data revealed that the linear stress-strain coupling constitutes one of the main model form errors. Hence, a gene-expression programming (GEP) based machine-learning technique was applied to the high-fidelity DNS data to train non-linear explicit algebraic Reynolds stress models (EARSM), using different training regions. The trained models were first assessed in an a priori sense (without running any CFD) and showed much improved alignment of the trained models in the region of training. Additional RANS calculations were then performed using the trained models. Importantly, to assess their robustness, the trained models were tested both on the cases they were trained for and on testing, i.e. previously not seen, cases with different flow features. The developed models improved prediction of the Reynolds stress, TKE production, wake-loss profiles and wake maturity, across all cases.

Copyright (c) 2018 by ASME
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