期刊
JOURNAL OF CLEANER PRODUCTION
卷 279, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.123431
关键词
Coronavirus; Air distribution; Cross infection; Computational fluid flow; Discrete phase model
资金
- National Natural Science Foundation of China [51725602]
- China Postdoctoral Science Foundation [2020M671618]
This study investigated the air distribution optimization of a general three-bed hospital ward in China, and suggested that a bottom-in and top-out air distribution proposal is recommended to minimize cross-infections.
Currently, the 2019-CoV-2 has been raging across the world for months, causing massive death, huge panic, chaos, and immeasurable economic loss. Such emerging epidemic viruses come again and again over years, leading to similar destructive consequences. Air-borne transmission among humans is the main reason for the rapid spreading of the virus. Blocking the air-borne transmission should be a significant measure to suppress the spreading of the pandemic. Considering the hospital is the most probable place to occur massive cross-infection among patients as emerging virus usually comes in a disguised way, an air distribution optimization of a general three-bed hospital ward in China is carried out in this paper. Using the Eulerian-Lagrangian method, sneeze process from patients who are assumed to be the virus carrier, which is responsible for a common event to trigger cross-infection, is simulated. The trajectory of the released toxic particle and the probability of approaching others in the same ward are calculated. Two evaluation parameter, total maximum time (TMT) and overall particle concentration (OPC) to reflect the particle mobility and probability to cause cross-infection respectively, are developed to evaluate the proposed ten air distributions in this paper. A relatively optimized air distribution proposal with the lowest TMT and OPC is distinguished through a three-stage analysis. Results show that a bottom-in and top-out air distribution proposal is recommended to minimize cross-infections. (c) 2020 Elsevier Ltd. All rights reserved.
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