Large eddy simulation of droplet transport and deposition in the human respiratory tract to evaluate inhalation risk

Abstract

As evidenced by the worldwide pandemic, respiratory infectious diseases and their airborne transmission must be studied to safeguard public health. This study focuses on the emission and transport of speech-generated droplets, which can pose risk of infection depending on the loudness of the speech, its duration and the initial angle of exhalation. We have numerically investigated the transport of these droplets into the human respiratory tract by way of a natural breathing cycle in order to predict the infection probability of three strains of SARS-CoV-2 on a person who is listening at a one-meter distance. Numerical methods were used to set the boundary conditions of the speaking and breathing models and large eddy simulation (LES) was used for the unsteady simulation of approximately 10 breathing cycles. Four different mouth angles when speaking were contrasted to evaluate real conditions of human communication and the possibility of infection. Breathed virions were counted using two different approaches: the breathing zone of influence and direction deposition on the tissue. Our results show that infection probability drastically changes based on the mouth angle and the breathing zone of influence overpredicts the inhalation risk in all cases. We conclude that to portray real conditions, the probability of infection should be based on direct tissue deposition results to avoid overprediction and that several mouth angles must be considered in future analyses.

Coupled Eulerian Wall Film–Discrete Phase model for predicting respiratory droplet generation during a coughing event

“…The realistic trachea-bronchus model was created from CT scans, and details on the process are available in the previous study by Ito (Ito, 2016). This trachea-bronchus model, in combination with the nasal cavity, has been validated by our research group, providing reliable results in diverse research themes (C. Yoo & Ito, 2022;Kuga et al, 2021Kuga et al, , 2022Kuga et al, , 2023Murga et al, 2023;Khoa, Li, et al, 2023).…”