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International Journal of Rotating Machinery
Volume 2017, Article ID 3718671, 11 pages
https://doi.org/10.1155/2017/3718671
Research Article

Numerical Investigation of Fluid Flow and Performance Prediction in a Fluid Coupling Using Large Eddy Simulation

1School of Mechanical Science and Engineering, Jilin University, Changchun 130022, China
2State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, China

Correspondence should be addressed to Chunbao Liu; moc.621@cbnauil

Received 27 December 2016; Revised 22 May 2017; Accepted 9 July 2017; Published 21 August 2017

Academic Editor: Funazaki Ken-ichi

Copyright © 2017 Wei Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Large eddy simulation (LES) with various subgrid-scale (SGS) models was introduced to numerically calculate the transient flow of the hydraulic coupling. By using LES, the study aimed to advance description ability of internal flow and performance prediction. The CFD results were verified by experimental data. For the purpose of the description of the flow field, six subgrid-scale models for LES were employed to depict the flow field; the distribution structure of flow field was legible. Moreover, the flow mechanism was analyzed using 3D vortex structures, and those showed that DSL and KET captured abundant vortex structures and provided a relatively moderate eddy viscosity in the chamber. The predicted values of the braking torque for hydraulic coupling were compared with experimental data. The comparison results were compared with several simulation models, such as SAS and RKE, and SSTKW models. Those comparison results showed that the SGS models, especially DSL and KET, were applicable to obtain the more accurate predicted results than SAS and RKE, and SSTKW models. Clearly, the predicted results of LES with DSL and KET were far more accurate than the previous studies. The performance prediction was significantly improved.