Virus transport and infection evaluation in a passenger elevator with a COVID-19 patient

Contaminant transport and flow distribution are very important during an elevator ride, as the reduced social distancing may increase the infection rate of airborne diseases such as COVID-19. Studying the airflow and contaminant concentration in an elevator is not straightforward because the flow pattern inside an elevator changes dramatically with passenger movement and frequent door opening. Since very little experimental data were available for elevators, this investigation validated the use of computational fluid dynamics (CFD) based on the RNG k- $$ turbulence model to predict airflow and contaminant transport in a scaled, empty airliner cabin with a moving passenger. The movement of the passenger in the cabin created a dynamic airflow and transient contaminant dispersion that were similar to those in an elevator. The computed results agreed reasonably well with the experimental data for the cabin. The validated CFD program was then used to calculate the distributions of air velocity, air temperature, and particle concentration during an elevator ride with an index patient. The CFD results showed that the airflow pattern in the elevator was very complex due to the downward air supply from the ceiling and upward thermal plumes generated by passengers. This investigation studied different respiratory activities of the index patient, that is, breathing only, breathing, and coughing with and without a mask, and talking. The results indicated that the risk of infection was generally low because of the short duration of the elevator ride. If the index patient talked in the elevator, two passengers in the closest proximity to distance would be infected.