Aerodynamic effects of the gap spacing between adjacent vehicles on wind tunnel train models

Yutao Xia, Tanghong Liu, Houyu Gu, Zijian Guo, Zhengwei Chen, Wenhui Li, Li Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)

Abstract

A certain gap spacing between adjacent vehicles is usually inevitable in wind tunnel force tests of high-speed trains under no crosswind, which may affect the wind tunnel test results. Thus, to understand the influence of gap spacings on the train aerodynamics, the aerodynamic drag, pressure distributions and airflow structures of 1/8th-scale high-speed train models with gap spacings of 0, 5, 8, 10, 20, and 30 mm were studied using RANS based on SST k-ω turbulence model. The simulation method was verified by the wind tunnel experiment data. The results show that the gap spacing significantly affects the airflow structure around inter-car gap and aerodynamic resistances of train models. For the high-speed train model scaled at 1/8th at zero yaw, compared with gap spacing of 0 mm, the gap spacings lead to a significant reduction in the aerodynamic drag of the head car and an increase in that of the tail car, whereas which of the middle car is not significant. The maximum difference of the drag coefficient of the entire train model is smaller than 2.0%. When the gap spacing does not exceed 8 mm, the discrepancies of the drag coefficients of three cars are within 6.15%.

Original languageEnglish
Pages (from-to)835-852
Number of pages18
JournalEngineering Applications of Computational Fluid Mechanics
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes

Keywords

  • aerodynamic drag
  • boundary layer
  • gap spacing
  • High-speed train
  • pressure distribution
  • RANS

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation

Fingerprint

Dive into the research topics of 'Aerodynamic effects of the gap spacing between adjacent vehicles on wind tunnel train models'. Together they form a unique fingerprint.

Cite this