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Concerns have been ramping up with regard to the propagation of infectious droplets due to the recent COVID-19 pandemic. The effects of filter microstructures and ambient air flows on droplet dispersion by sneezing are investigated by a fully coupled Eulerian–Lagrangian computational modeling with a micro-to-macroscale bridging approach. Materials that are commonly applied to face masks are modeled to generate two different virtual masks with various levels of filtration efficiency, and the leakage percentages through the unsealed nose and cheek areas were set to 11% and 25%, respectively. The droplet propagation distance was simulated with and without mask wearing in still and windy conditions involving head wind, tail wind, and side wind. The results demonstrate that wearing a face mask reduces the transmittance distance of droplets by about 90%–95% depending on the mask type; nonetheless, the droplets can be transmitted to distances of 20–25 cm in the forward direction even with mask-wearing. Thus, a social distance of at least 20 cm between people would help to prevent them from becoming exposed to ejected droplets. This study is significant in that important aspects of mask materials, in this case the porous microstructure-dependent filtration efficiency and permeability under varied ambient flow conditions, were considered for the first time in an evaluation of the barrier performance against droplet transmittance through a multiphase computational fluid dynamics simulation of air-droplet interaction and turbulence flow dynamics.

physics of fluids

Effects of the filter microstructure and ambient air condition on the aerodynamic dispersion of sneezing droplets: A multiscale and multiphysics simulation study