The performance of different turbulence models (URANS, SAS and DES) for predicting high-speed train slipstream

Shibo Wang, James R. Bell, David Burton, Astrid H Herbst, John Sheridan, Mark C. Thompson

Research output: Contribution to journalArticleResearchpeer-review

127 Citations (Scopus)


The air movement induced by a high-speed train (HST) as it passes, the slipstream, is a safety hazard to commuters and trackside workers, and can cause damage to infrastructure along track lines. Because of its importance, many numerical studies have been undertaken to investigate this phenomenon. However, to the authors' knowledge, a systematic comparison of the accuracy of different turbulence models applied to the prediction of slipstream has not yet been conducted. This study investigates and evaluates the performance of three widely used turbulence models: URANS, SAS and DES, to predict the slipstream of a full-featured generic train model, and the results are compared with wind-tunnel experimental data to determine the fidelity of the models. Specifically, this research aims to determine the suitability of different turbulence modelling approaches, involving significantly different computational resources, for modelling different aspects of slipstream.

Original languageEnglish
Pages (from-to)46-57
Number of pages12
JournalJournal of Wind Engineering and Industrial Aerodynamics
Publication statusPublished - 1 Jun 2017


  • Computational Fluid Dynamics (CFD)
  • Detached Eddy Simulation (DES)
  • High-speed trains
  • Scale-Adaptive Simulation (SAS)
  • Slipstream
  • Train aerodynamics
  • Unsteady Reynolds-Averaged Navier–Stokes equations (URANS)

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