ABSTRACT
In this study, the flow field around face masks was visualized and evaluated using computational fluid dynamics. The protective efficiency of face masks suppressing droplet infection owing to differences in the shape, medium, and doubling usage is predicted. Under the ongoing COVID-19 pandemic condition, many studies have been conducted to highlight that airborne transmission is the possible transmission route. However, the virus infection prevention effect of face masks has not been sufficiently discussed and, thus, remains as a controversial issue. Therefore, we aimed to provide a beneficial index for the society. The topology-free immersed boundary method, which is advantageous for complex shapes, was used to model the flow in the constriction area, including the contact surface between the face and mask. The jet formed from the oral cavity flow out through the surface of the mask and leaks from the gap between the face and mask. A Darcy-type model of porous media was used to model the flow resistance of masks. A random variable stochastic model was used to measure particle transmittance. We evaluated the differences in the amount of leakage and deposition of the droplets during exhalation and inhalation, depending on the differences in the conditions between the surgical and cloth masks owing to coughing and breathing. The obtained results could be useful for epidemiological measures by numerically showing the particle suppression effect of the face mask. This includes both exhalation and inhalation. © 2022 Author(s).
ABSTRACT
Transmission of infectious respiratory diseases through airborne dispersion of viruses poses a great risk to public health. In several major diseases, one of the main modes of transmission is through respiratory droplets. Virus laden respiratory droplets and aerosols can be generated during coughing, sneezing and speaking. These droplets and aerosols can remain suspended in air and be transported by airflow posing a risk of infection in individuals who might come in contact with them. With this background, in this work, we present a numerical framework for simulation of dispersion of respiratory sputum droplets using implicit large-eddy simulations. A combination of discrete Lagrangian droplet model and fully compressible Navier-Stokes flow solver is employed in this study. The method is applied to analyze cases such as droplet dispersion during speech and cough under different environmental settings. Furthermore, the performance of the numerical framework is evaluated through strong and weak scaling analysis. © 2021 ACM.