Abstract
Nowadays the use of public transportation (PT) has been identified as high risk as due to the transfer of particles carrying the coronavirus from an infected passenger to others. This study puts forward a new computational framework for predicting the spread of droplets produced while the infected passenger talking inside the cabin of a train during various scenarios, including the changes in the outflows' location and the infected passenger's position. CFD was used to conduct the study, using the Euler-Lagrange approach to capture the transmission of particles, and Reynolds-averaged Navier-Stokes equations (RANS) to compute the airflow field. The results revealed that opening the window reduces the duration of particles inside the domain. So that when the window is open, the particle's shelf time can decrease to 25 percent comparing with closed mode. It was found that the passenger sitting next to the infected passenger encountered the highest infection risk. The conclusions made in this work show that the most desirable situation is obtained when the infected passenger is sitting next to the exits, whether the window is closed or open. The results of this paper offer comprehensive insights into how to keep indoor environments safe against infection aerosols.