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

Xiao Shuai Huo, Tang Hong Liu, Zheng Wei Chen, Wen Hui Li, Hong Rui Gao, Bin Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In this study, two steady RANS turbulence models (SST k-ωand Realizable k-ϵ) and four unsteady turbulence models (URANS SST k-ωand 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-ωmodel is shown to provide relatively reasonable results for the surface pressures and aerodynamicforces. As a result, the RANS SST k-ωmodel 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.

Original languageEnglish
Pages (from-to)303-314
Number of pages12
JournalWind and Structures, An International Journal
Issue number4
Publication statusPublished - Oct 2023


  • crosswind
  • high-speed train
  • RANS
  • SAS

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Modelling and Simulation
  • Building and Construction


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