A Quantitative Framework for Numerically Estimating the COVID19 Infection Risk in a Crowded Indoor Environment

Airborne transmission has been considered as one of the main infection routes for COVID19 [(Wang et al., Science 373, 981 (2021)]. To estimate the infection risk, the current framework is based on a fluid solver considering the influence of the temperature field, which simulates the indoor airflow affecting airborne transmission, and a Lagrangian droplet model to describe the path of the viral particle moving under the indoor airflow. For the fluid solver, fully compressible Navier–Stokes is adopted to accurately capture the thermal-fluid interactions with significant heat transfer such as the airflow near the LED lights. Additionally, the immersed boundary developed by authors is used to construct the objects and assign the boundary condition in indoor environments such as the ventilation system, the human body heat and, the oral airflow. The fluid solver is validated by comparing the result of the mean age of air with the experimental result. With the information of ambient air’s velocity, humidity, and temperature obtained from the fluid solver, the path of the viral particle moving can be predicted using the Lagrangian droplet model considering the effect of the drag, natural convection, and evaporation. The droplet model is coupled with the Eulerian fluid solver with a weak-two-way coupling. Finally, the infection risk is quantitatively estimated by using Wells-Riley Model. The whole framework is based on a hierarchical structure named CUBE to perform the calculation in supercomputer Fugaku. With the framework developed in this work, the sputum droplet dynamics interacted with the indoor airflow to influence the infection risk can be qualitatively and quantitatively estimated.