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Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning
Article

Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows

1
Department of Industrial Engineering, University of Florence, Via Santa Marta, 3, 50139 Florence, Italy
2
GE Avio Aero, Via I maggio 99, 10040 Rivalta di Torino, Italy
3
Center for Applied Physics and Technology, HEDPS, College of Engineering, Peking University, Beijing 100871, China
4
Department of Mechanical Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Davide Astolfi
Energies 2021, 14(24), 8327; https://doi.org/10.3390/en14248327
Received: 31 October 2021 / Revised: 2 December 2021 / Accepted: 3 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Transition/Turbulence Models for Turbomachinery Applications)
This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladings, was analyzed, using a state-of-the-art RANS approach, over a wide range of Reynolds numbers. To ensure that the wake originates from correctly reproduced blade boundary-layers, preliminary analyses were carried out to check for the impact of transition closures, and the best-performing numerical setup was identified. Two different machine-learned closures were considered. They were applied in a prescribed region downstream of the blade trailing edge, excluding the endwall boundary layers. A sensitivity analysis to the distance from the trailing edge at which they are activated is presented in order to assess their applicability to the whole wake affected portion of the computational domain and outside the training region. It is shown how the best-performing closure can provide results in very good agreement with the experimental data in terms of wake loss profiles, with substantial improvements relative to traditional turbulence models. The discussed analysis also provides guidelines for defining an automated zonal application of turbulence closures trained for wake-mixing predictions. View Full-Text
Keywords: low-pressure turbine; wake mixing; transition; machine learning; explicit algebraic Reynolds stress model; laminar kinetic energy low-pressure turbine; wake mixing; transition; machine learning; explicit algebraic Reynolds stress model; laminar kinetic energy
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MDPI and ACS Style

Pacciani, R.; Marconcini, M.; Bertini, F.; Rosa Taddei, S.; Spano, E.; Zhao, Y.; Akolekar, H.D.; Sandberg, R.D.; Arnone, A. Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows. Energies 2021, 14, 8327. https://doi.org/10.3390/en14248327

AMA Style

Pacciani R, Marconcini M, Bertini F, Rosa Taddei S, Spano E, Zhao Y, Akolekar HD, Sandberg RD, Arnone A. Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows. Energies. 2021; 14(24):8327. https://doi.org/10.3390/en14248327

Chicago/Turabian Style

Pacciani, Roberto, Michele Marconcini, Francesco Bertini, Simone Rosa Taddei, Ennio Spano, Yaomin Zhao, Harshal D. Akolekar, Richard D. Sandberg, and Andrea Arnone. 2021. "Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows" Energies 14, no. 24: 8327. https://doi.org/10.3390/en14248327

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