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Involving non-equilibrium training dataset in data-driven turbulence modeling for turbomachinery

Published online by Cambridge University Press:  28 November 2024

J.L. Du Open the ORCID record for J.L. Du [Opens in a new window] ,
W.S. Liu Open the ORCID record for W.S. Liu [Opens in a new window] ,
L. Fang Open the ORCID record for L. Fang [Opens in a new window]  and
T.W. Bao
J.L. Du
Affiliation:
Laboratory of Complex Systems, Ecole Centrale de Pékin, Beihang University, Beijing, China
W.S. Liu
Affiliation:
School of Energy and Power Engineering, Beihang University, Beijing, China College of Engineering, Peking University, Beijing, China
L. Fang*
Affiliation:
Laboratory of Complex Systems, Ecole Centrale de Pékin, Beihang University, Beijing, China
T.W. Bao*
Affiliation:
BSS TurboTech, Beijing, China
*
Corresponding authors: L. Fang; Email: le.fang@buaa.edu.cn, T.W. Bao; Email: tianwei.bao@bssturbotechltd.cn
Corresponding authors: L. Fang; Email: le.fang@buaa.edu.cn, T.W. Bao; Email: tianwei.bao@bssturbotechltd.cn
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Abstract

In order to improve the performance of $k - \omega $ SST model in turbomachinery, previous studies have used the machine-learning (ML) technique to obtain turbulence models (for example, the ML-RANS EQ model). However, these models do not lead to satisfactory results in complex flows in turbomachinery. In this study, we use non-equilibrium training dataset to obtain a new turbulence model (i.e., the ML-RANS TR-NE-EQ model). Calculations in various cases of turbine cascade flows show that ML-RANS TR-NE-EQ model performs obviously better than ML-RANS EQ model as well as $k - \omega $ SST model.

Keywords

k − ω SST modelmachine learningnon-equilibrium turbulenceturbine cascade

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Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1335 , May 2025 , pp. 1215 - 1236
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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