Wind and Structures

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Comparison of RANS, URANS, SAS and IDDES for the prediction of train crosswind characteristics

  • Xiao-Shuai Huo (Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University) ;
  • Tang-Hong Liu (Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University) ;
  • Zheng-Wei Chen (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wen-Hui Li (School of Rail Transportation, Soochow University) ;
  • Hong-Rui Gao (Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University) ;
  • Bin Xu (Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University)
  • Received : 2021.12.26
  • Accepted : 2023.09.22
  • Published : 2023.10.25
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Abstract

In this study, two steady RANS turbulence models (SST k-ω and Realizable k-ε) and four unsteady turbulence models (URANS SST k-ω and Realizable k-ε, SST-SAS, and SST-IDDES) are evaluated with respect to their capacity to predict crosswind characteristics on high-speed trains (HSTs). All of the numerical simulations are compared with the wind tunnel values and LES results to ensure the accuracy of each turbulence model. Specifically, the surface pressure distributions, time-averaged aerodynamic coefficients, flow fields, and computational cost are studied to determine the suitability of different models. Results suggest that the predictions of the pressure distributions and aerodynamic forces obtained from the steady and transient RANS models are almost the same. In particular, both SAS and IDDES exhibits similar predictions with wind tunnel test and LES, therefore, the SAS model is considered an attractive alternative for IDDES or LES in the crosswind study of trains. In addition, if the computational cost needs to be significantly reduced, the RANS SST k-ω model is shown to provide relatively reasonable results for the surface pressures and aerodynamic forces. As a result, the RANS SST k-ω model might be the most appropriate option for the expensive aerodynamic optimizations of trains using machine learning (ML) techniques because it balances solution accuracy and resource consumption.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 52202426), the National Key R&D Program of China (Grant No. 2020YFA0710903), the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2021zzts0171), Hong Kong and Macau Joint Research and Development Fund of Wuyi University (Grant No. 2019WGALH15, 2019WGALH17, 2021WGALH15), and Guangdong-Hong Kong-Macao Research Team Project - Guangdong Basic and Applied Basic Research Fund (Grant No. 2021B1515130006).

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