-
Prediction of the spread of Corona-virus carrying droplets in a bus – A computational based artificial intelligence approach
- Back
Metadata
Document Title
Prediction of the spread of Corona-virus carrying droplets in a bus - A computational based artificial intelligence approach
Author
Mesgarpour M.,Abad J.M.N.,Alizadeh R.,Wongwises S.,Doranehgard M.H.,Ghaderi 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; Department of General Surgery, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, 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
Journal of Hazardous Materials
ISSN
03043894
Year
2021
Volume
413
Open Access
All Open Access, Bronze, Green
Publisher
Elsevier B.V.
DOI
10.1016/j.jhazmat.2021.125358
Abstract
Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus. © 2021
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
Copyright
Rights
Publisher
Publication Source
Scopus