CFD modeling of airborne pathogen transmission of COVID-19 in confined spaces under different ventilation strategies

Hamid Motamedi; Mohammadreza Shirzadi; Yoshihide Tominaga; Parham A Mirzaei

doi:10.1016/j.scs.2021.103397

CFD modeling of airborne pathogen transmission of COVID-19 in confined spaces under different ventilation strategies

Sustain Cities Soc. 2022 Jan:76:103397. doi: 10.1016/j.scs.2021.103397. Epub 2021 Oct 3.

Authors

Hamid Motamedi¹, Mohammadreza Shirzadi^{2

3}, Yoshihide Tominaga², Parham A Mirzaei⁴

Affiliations

¹ Department of Mechanical Engineering, Tarbiat Modares University, Iran.
² Wind and Fluid Engineering Research Center, Niigata Institute of Technology, Kashiwazaki, Japan.
³ Fine Particle Technology Laboratory, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, 739-8527, Hiroshima, Japan.
⁴ Architecture & Built Environment Department, University of Nottingham, University Park, Nottingham NG2RD, United Kingdom.

Abstract

Airborne transmission is an important route of spread of viral diseases (e.g., COVID-19) inside the confined spaces. In this respect, computational fluid dynamics (CFD) emerged as a reliable and fast tool to understand the complex flow patterns in such spaces. Most of the recent studies, nonetheless, focused on the spatial distribution of airborne pathogens to identify the infection probability without considering the exposure time. This research proposes a framework to evaluate the infection probability related to both spatial and temporal parameters. A validated Eulerian-Lagrangian CFD model of exhaled droplets is first developed and then evaluated with an office case study impacted by different ventilation strategies (i.e., cross- (CV), single- (SV), mechanical- (MV) and no-ventilation (NV)). CFD results were analyzed in a bespoke code to calculate the tempo-spatial distribution of accumulated airborne pathogens. Furthermore, two indices of local and general infection risks were used to evaluate the infection probability of the ventilation scenarios. The results suggest that SV has the highest infection probability while SV and NO result in higher dispersions of airborne pathogens inside the room. Eventually, the time history of indices reveals that the efficiency of CV and MV can be poor in certain regions of the room.

Keywords: Airborne pathogen transmission; Buildings; COVID-19; Eulerian-Lagrangian CFD; Infection risk; Ventilation.