Numerical study of when and who will get infected by coronavirus in passenger car

Abstract

In light of the COVID-19 pandemic, it is becoming extremely necessary to assess respiratory disease transmission in passenger cars. This study numerically investigated the human respiration activities’ effects, such as breathing and speaking, on the transport characteristics of respiratory-induced contaminants in passenger car. The main objective of the present study is to accurately predict when and who will get infected by coronavirus while sharing a passenger car with a patient of COVID-19 or similar viruses. To achieve this goal, transient simulations were conducted in passenger car. We conducted a 3D computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a passenger car. The Eulerian-Eulerian flow model coupled with k-ε turbulence approach was used to track respiratory contaminants with diameter ≥ 1 μm that were released by different passengers within the passenger car. The results showed that around 6.38 min, this is all that you need to get infected with COVID-19 when sharing a poorly ventilated car with a driver who got coronavirus. It also has been found that enhancing the ventilation system of the passenger car will reduce the risk of contracting Coronavirus. The predicted results could be useful for future engineering studies aimed at designing public transport and passenger cars to face the spread of droplets that may be contaminated with pathogens.