Reducing the contaminant dispersion and infection risks in the train cabins by adjusting the inlet turbulence intensity: A study based on turbulence simulation

Tiantian Wang; Yaxin Zheng; Yibin Lu; Fangcheng Shi; Peng Ji; Bosen Qian; Lei Zhang; Dongrun Liu; Jiabin Wang; Buyao Yang

doi:10.1016/j.scitotenv.2024.172735

Reducing the contaminant dispersion and infection risks in the train cabins by adjusting the inlet turbulence intensity: A study based on turbulence simulation

Sci Total Environ. 2024 Apr 24:930:172735. doi: 10.1016/j.scitotenv.2024.172735. Online ahead of print.

Authors

Tiantian Wang¹, Yaxin Zheng², Yibin Lu³, Fangcheng Shi², Peng Ji⁴, Bosen Qian⁵, Lei Zhang⁵, Dongrun Liu⁵, Jiabin Wang⁵, Buyao Yang²

Affiliations

¹ College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, Hunan, China; Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan, China.
² College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, Hunan, China.
³ Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan, China; Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China. Electronic address: luyibin@csu.edu.cn.
⁴ School of Design, Hunan University, Changsha 410082, Hunan, China.
⁵ Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan, China.

PMID: 38663624
DOI: 10.1016/j.scitotenv.2024.172735

Abstract

Existing studies on ventilation in closed spaces mainly considered the average inlet velocity and ignored the influence of inlet turbulent fluctuation. However, the variation in inlet turbulence intensity (TI) is considerable and significantly affects the dispersion of contaminants. This study conducts numerical simulations verified by experiments to investigate the effect of the inlet TI on train contaminants dispersion and analyze infection probability variation. Firstly, the unsteady Reynolds-averaged Navier-Stokes (URANS) method and improved delayed detached eddy simulation (IDDES) method are compared in simulating the internal airflow characteristics based on the on-site measurement. The results indicate that the latter dominates in capturing airflow pulsations more than the former, although the mean airflow results obtained from both methods agree well with experimental results. Furthermore, the IDDES method is employed to investigate the effect of the inlet TI on contaminant dispersion, and the infection risks are also assessed using the improved probability model. The results show that, with the increase of TI from 5 % to 30 %, the contaminant removal grows considerably, with the removal index rising from 0.23 to 1.86. The increased TI leads to the overall and local infection risks of occupants descending significantly, wherein the former decreases from 1.53 % to 0.88 % with a reduction rate of 42 %, and the latter drops from 3.30 % to 2.16 % with a mitigation rate of 35 %. The findings can serve as solid guidelines for numerical method selection in accurately capturing the indoor dynamic airflow distribution and for the ventilation parameters design regarding TI inside trains to mitigate the airborne infection risk.

Keywords: Indoor ventilation; Infection risk; Numerical simulation; On-site measurements; Train cabins; Turbulence intensity.