Simulation-based study of COVID-19 outbreak associated with air-conditioning in a restaurant

Han Liu, Sida He, Lian Shen, Jiarong Hong

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7 Scopus citations

Abstract

COVID-19 has shown a high potential of transmission via virus-carrying aerosols as supported by growing evidence. However, detailed investigations that draw direct links between aerosol transport and virus infection are still lacking. To fill in the gap, we conducted a systematic computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a restaurant setting, where likely cases of airflow-induced infection of COVID-19 caused by asymptomatic individuals were widely reported by the media. We employed an advanced in-house large eddy simulation solver and other cutting-edge numerical methods to resolve complex indoor processes simultaneously, including turbulence, flow-aerosol interplay, thermal effect, and the filtration effect by air conditioners. Using the aerosol exposure index derived from the simulation, we are able to provide a spatial map of the airborne infection risk under different settings. Our results have shown a remarkable direct linkage between regions of high aerosol exposure index and the reported infection patterns in the restaurant, providing strong support to the airborne transmission occurring in this widely reported incident. Using flow structure analysis and reverse-time tracing of aerosol trajectories, we are able to further pinpoint the influence of environmental parameters on the infection risks and highlight the need for more effective preventive measures, e.g., placement of shielding according to the local flow patterns. Our research, thus, has demonstrated the capability and value of high-fidelity CFD tools for airborne infection risk assessment and the development of effective preventive measures.

Original languageEnglish (US)
Article number023301
JournalPhysics of Fluids
Volume33
Issue number2
DOIs
StatePublished - Feb 1 2021

Bibliographical note

Funding Information:
J.H. would like to acknowledge the support of the University of Minnesota Rapid Response Grant from the Office for Vice President of Research (OVPR). The authors would also like to acknowledge the Minnesota Supercomputing Institute (MSI, http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper.

Publisher Copyright:
© 2021 Author(s).

PubMed: MeSH publication types

  • Journal Article

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