# Study on the Impact of Ventilation Methods on Droplet Nuclei Transmission in Subway Carriages
## 2 Materials and Methods
### Boundary Conditions  
This research investigates the effects of different ventilation methods, air supply velocities, and masks on droplet distribution within the subway carriage. In accordance with the Chinese national standard \"Subway Design Code\" [33], which stipulates that individuals must be able to tolerate an appropriate level of heat, three air change rates and a specific air source temperature were selected as the primary focus of the study. ASHRAE 55-2020 may specify the maximum air velocity allowed in summer air-conditioned environments to avoid cold draft sensations. In indoor environments, the air supply velocity needs to be set to avoid cold air sensations (draft) and localized discomfort. ASHRAE 55-2020 recommends the following general comfort range: The air supply velocity should be controlled between 0.15 and 0.25 m/s to maintain thermal comfort. In order to better remove contaminants from the cabin, we increased the air velocity to 0.4 m/s. The subsequent ADPI values show that this air velocity meets the thermal comfort requirements. The air supply speed of the SFRC air supply method is one quarter of that of the MV air supply method, because the area of the lower air outlet of the SFRC air supply method is four times the area of the air supply outlet of the MV air supply method, so there are three kinds of air supply speeds used in the MV system, namely, 0.8 m/s, 1.2 m/s, and 1.6 m/s, and the SFRC system is used in the air supply speeds of 0.2 m/s, 0.3 m/s, and 0.4 m/s. air supply speeds. The air supply velocities for the MV system were established at 0.8 m/s, 1.2 m/s, and 1.6 m/s, in accordance with the ventilation specifications of the subway carriage. The SFRC system's speeds were set at 0.2 m/s, 0.3 m/s, and 0.4 m/s. The inlet was configured as a uniform velocity inlet. The air supply opening was covered by a very fine mesh, with the difference between the effective area and the total area being sufficiently small. The turbulence intensity was set at 5%. The carriage's enclosure, which includes the ceiling, side walls, and floor, was defined as a non-slip wall. To reflect real conditions  
Figure 4: Results of mesh gird independence. more accurately, the ceiling was modeled as two flat panels. The incompressible ideal gas assumption was used to approximate density variations. The convergence criterion was set to 10 \(\times\) 10\({}^{-5}\). A second-order upwind scheme was used to solve for momentum, turbulent kinetic energy, and the turbulence dissipation rate, while pressure was solved using the SIMPLE algorithm. The surface temperature of the body was set to 310 K, based on measurements taken during the trial [34; 35], with specific details shown in Table 2. The other solid surfaces in the subway car were assumed to be adiabatic. The droplets were released due to coughing (0-0.4 s) and continuous breathing (0.4-120 s) was simulated, with all other passengers maintaining exhalation. A user-defined function (UDF) was used to control the infected passenger's breathing after the cough. This UDF was used to infect the passenger's mouth, because the cough only lasted 0.2 s, but the whole simulation continued for 120 s, so the UDF was used to make them stop coughing automatically. In the 0-0.4 s time range, a coughing event was simulated in order to establish a connection between the continuous and discrete phases in both directions. The speed of coughing was set to 13 m/s [36]. Multiple time steps were tested to ensure accurate capture of the transient airflow field. The study found that time steps of 0.01 s for coughing and 0.1 s for breathing were sufficient to capture airflow changes during these processes. Therefore, a time step of 0.01 s was used for coughing and 0.1 s for breathing. The infected patient's mouth diameter was 0.028 m, and the breathing temperature was around 310 K. Given the wide initial size range of cough droplets, this study used a size range of 0.1 \(\upmu\)m to 100 \(\upmu\)m, and particles within this range were classified using a double-R distribution.