During an airborne infectious disease outbreak, bus passengers can be easily infected by the dispersion of exhaled droplets from an infected passenger. Therefore, measures to control the transport of droplets are necessary, such as a mask or purifier. The current research examined aerosol transport in a bus with air-conditioning. To determine the dispersion path, deposition distribution, and droplet escape time, the computational fluid dynamics were used to predict the flow field and the dispersion of droplets considering the effects of droplet size, location of the infected person, and purifier type. In addition, based on the viability and the number of virus particles in a droplet, the total number of virus particles inhaled by passengers over a 4-h journey was obtained by the superposition method. The Wells–Riley equation was then used to assess the infection risk of the passengers in the bus cabin. The results showed that droplets with a size of 1–20 μ m have essentially the same deposition characteristics, and the location of the infected passenger affects the distribution of droplets' transport and the effectiveness of a purifier in removing droplets. A purifier can effectively remove droplets from passengers' coughs and reduce the infection risk of passengers. The performance of the smaller purifiers is not as stable as that of the larger purifiers, and the performance is influenced by the airflow structure where the infected passenger is located.
COVID-19 can be easily transmitted to passengers by inhaling exhaled droplets from the infected person in a bus. Therefore, studying droplet dispersion would provide further insight into the mechanism of virus transmission and predict the risk of infection among passengers on a bus. In this research, a bus equipped with air-conditioning was employed as the research object. To determine the dispersion path, concentration distribution, and escape time of the droplets, computational fluid dynamic (CFD) was applied to simulate the flow field and the droplets’ dispersion. The effect of the air supply rate, the location of vents, and the location of infected persons on the dispersion were discussed. Based on the distribution of droplets in the cabin calculated by CFD, a superposition method was used to determine the number of virus particles inhaled by every individual passenger over a four-hour journey. Then, infection risk was assessed by the Wells-Riley equation for all the passengers in the cabin after the whole journey. The results show that the distribution of droplets in the cabin is greatly influenced by the location of the infected person, and the airflow pattern is highly associated with the air supply rate and the location of vents. The infection risk of passengers located at the droplet dispersion path and the distance from the infected persons less than 2.2 m is over 10%. The increase in the air supply rate could speed up the spread of the droplets but at the same time, it could reduce the infection risk.
In order to improve the thermal comfort of passengers, the thermoelectric cooling climate-controlled seats were set in the passenger cabin. In this study, thermoelectric cooler units’ cooling performance is studied by experiment method. The heat and flow field of the bus cabin are analyzed in four different cases (air conditioner open/closed, thermoelectric cooler open/closed) using computational fluid dynamics method. Moreover, an experiment has been conducted to verify the accuracy of the simulation method. The results show as below: the ideal working current of the thermoelectric cooler is in range of 3-4 A and the coefficient of performance value range 0.5-0.7. The climate-controlled seats can enhance the local airflow perturbation, which can increase the passengers’ overall thermal comfort by 18.96%. The thermal comfort of passengers closer to air conditioner inlet is better than passengers in other areas, and the area with the best thermal comfort for passengers are the front and rear seats of the bus cabin. As expected, the thermoelectric cooling climate-controlled seat can improve the thermal comfort of passengers, and has a broad application prospect.
In an intercity bus, respiratory infectious diseases put passengers at high risk of getting infected by the droplets exhaled by an infected person, and the risk increases when exposed to more droplets. Here, to quickly determine the concentration distribution of droplets and predict the infection risks in a closed space, and to enhance the reliability of the conventional steady-state particle tracking method for predicting the trajectory of droplets released by coughing or sneezing, an improved steady-state particle tracking method is proposed. In it, the momentum of released droplets previously ignored in the conventional steady-state particle tracking method was specifically incorporated using experimental data. Then, the improved method was combined with a random walk model and applied to investigate all possible trajectories of droplets released by different passengers inside a bus. Consequently, the concentration distribution of droplets was obtained from the trajectory information. Finally, the Wells-Riley equation was used to predict the infection risk of every passenger, this based on the evaluated number of droplets inhaled per passenger. The results show that the improved steady-state tracking method performs more accurately at predicting the concentration field of droplets and associated infection risk than the conventional steady-state particle tracking method. Furthermore, the relative cost of the improved steady-state tracking method is just one percent of the transient calculation method currently considered the most accurate.
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