Numerical analysis of the efficiency of face masks for preventing droplet airborne infections
Keiji Onishi, Akiyoshi Iida, Masashi Yamakawa, Makoto Tsubokura

TL;DR
This study uses computational fluid dynamics to analyze how different face mask designs and materials affect droplet leakage and deposition, providing insights into their effectiveness in preventing airborne infections like COVID-19.
Contribution
It introduces a novel CFD-based approach to evaluate face mask efficiency considering shape, material, and usage, filling a gap in infection prevention research.
Findings
Mask shape and material significantly influence droplet leakage.
Double masking reduces droplet transmission more effectively.
Flow dynamics differ between coughing and breathing scenarios.
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 main 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 is guided to the outside through the surface of…
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