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Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – A machine learning approach
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Metadata
Document Title
Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – A machine learning approach
Author
Mesgarpour M.,Abad J.M.N.,Alizadeh R.,Wongwises S.,Doranehgard M.H.,Jowkar S.,Karimi N.
Name from Authors Collection
Affiliations
Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi (KMUTT), Bangmod, Bangkok, 10140, Thailand; Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran; Department of Mechanical Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran; National Science and Technology Development Agency (NSTDA), Pathum Thani, 12120, Thailand; Department of Civil and Environmental Engineering, School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; Aerospace Engineering Department, Sharif University of Technology14588-89694, Iran; School of Engineering and Materials Science, Queen Mary University of London, London, E1 4NS, United Kingdom; James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
Type
Article
Source Title
Chemical Engineering Journal
ISSN
13858947
Year
2022
Volume
430
Open Access
All Open Access, Hybrid Gold, Green
Publisher
Elsevier B.V.
DOI
10.1016/j.cej.2021.132761
Abstract
Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250μm are investigated inside a bus. It is shown that humidity can profoundly affect the droplets distribution, such that 10% increase in relative humidity results in 30% increase in the droplets density at the farthest point from a sneezing passenger. Further, ventilation process is found to feature dual effects on the droplets distribution. Simple increases in the ventilation rate may accelerate the droplets transmission. However, carefully tailored injection of fresh air enhances deposition of droplets on the surfaces and thus reduces their concentration in the bus. Finally, the analysis identifies an optimal range of temperature, humidity and ventilation rate to maintain human comfort while minimising the transmission of droplets. © 2021 The Author(s)
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
Funding Sponsor
Engineering and Physical Sciences Research Council
License
CC BY
Rights
Author
Publication Source
Scopus