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Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network

Hamid Tamaddon-Jahromi, Igor Sazonov Orcid Logo, Jason Jones Orcid Logo, Alberto Coccarelli Orcid Logo, Sam Rolland Orcid Logo, Neeraj Kavan Chakshu, Hywel Thomas Orcid Logo, Perumal Nithiarasu Orcid Logo

International Journal of Numerical Methods for Heat & Fluid Flow, Volume: 32, Issue: 9, Pages: 2964 - 2981

Swansea University Authors: Hamid Tamaddon-Jahromi, Igor Sazonov Orcid Logo, Jason Jones Orcid Logo, Alberto Coccarelli Orcid Logo, Sam Rolland Orcid Logo, Neeraj Kavan Chakshu, Hywel Thomas Orcid Logo, Perumal Nithiarasu Orcid Logo

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DOI (Published version): 10.1108/hff-07-2021-0498

Abstract

PurposeThe main purpose of this paper is to devise a tool, based on Computational Fluid Dynamics (CFD) and Machine Learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A Gated Recurrent Units Neural Network (GRU-NN) is presented to learn and predict the...

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Published in: International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Published: Emerald 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa58941
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A Gated Recurrent Units Neural Network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking datasets.Design/methodology/approachA computational methodology is used for investigating how infectious particles originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor air flow is obtained by means of an in-house parallel CFD solver which employs a one equation Spalrat&#x2013;Allmaras (SA) turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted human breath. The numerical results are used to the ML training.FindingIn this work, it is shown that the developed ML model, based on the Gated Recurrent Units Neural Network (GRU-NN), can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results inthe paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.Originality/valueThis study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environments, potentially leading to new design. A parametric study is carried out to evaluate the impact of system settings on the time variation particles emitted human breath within the space considered.</abstract><type>Journal Article</type><journal>International Journal of Numerical Methods for Heat &amp;amp; Fluid Flow</journal><volume>32</volume><journalNumber>9</journalNumber><paginationStart>2964</paginationStart><paginationEnd>2981</paginationEnd><publisher>Emerald</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0961-5539</issnPrint><issnElectronic/><keywords>COVID-19 infection, CFD modelling, Spalrat&#x2013;Allmaras (SA) model, Particle tracking, Inhalation airflow, Recurrent Neural Network, Gated Recurrent Units (GRU)</keywords><publishedDay>20</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-07-20</publishedDate><doi>10.1108/hff-07-2021-0498</doi><url/><notes/><college>COLLEGE NANME</college><department>Civil Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>CIVL</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2022-07-25T16:37:35.6019745</lastEdited><Created>2021-12-07T09:51:54.1161588</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering</level></path><authors><author><firstname>Hamid</firstname><surname>Tamaddon-Jahromi</surname><order>1</order></author><author><firstname>Igor</firstname><surname>Sazonov</surname><orcid>0000-0001-6685-2351</orcid><order>2</order></author><author><firstname>Jason</firstname><surname>Jones</surname><orcid>0000-0002-7715-1857</orcid><order>3</order></author><author><firstname>Alberto</firstname><surname>Coccarelli</surname><orcid>0000-0003-1511-9015</orcid><order>4</order></author><author><firstname>Sam</firstname><surname>Rolland</surname><orcid>0000-0003-0455-5620</orcid><order>5</order></author><author><firstname>Neeraj Kavan</firstname><surname>Chakshu</surname><order>6</order></author><author><firstname>Hywel</firstname><surname>Thomas</surname><orcid>0000-0002-3951-0409</orcid><order>7</order></author><author><firstname>Perumal</firstname><surname>Nithiarasu</surname><orcid>0000-0002-4901-2980</orcid><order>8</order></author></authors><documents><document><filename>58941__21826__ba6c0708fb2846db9104fe7d66fbce29.pdf</filename><originalFilename>58941.pdf</originalFilename><uploaded>2021-12-07T09:59:36.9530928</uploaded><type>Output</type><contentLength>3696230</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><documentNotes>Released under the terms of a Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0)</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2022-07-25T16:37:35.6019745 v2 58941 2021-12-07 Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network b3a1417ca93758b719acf764c7ced1c5 Hamid Tamaddon-Jahromi Hamid Tamaddon-Jahromi true false 05a507952e26462561085fb6f62c8897 0000-0001-6685-2351 Igor Sazonov Igor Sazonov true false aa4865d48c53a0df1c1547171826eab9 0000-0002-7715-1857 Jason Jones Jason Jones true false 06fd3332e5eb3cf4bb4e75a24f49149d 0000-0003-1511-9015 Alberto Coccarelli Alberto Coccarelli true false c14ac34a71e9c058d1d2a353b44a24cd 0000-0003-0455-5620 Sam Rolland Sam Rolland true false e21c85ee9062e9be0fff8ab9d77b14d7 Neeraj Kavan Chakshu Neeraj Kavan Chakshu true false 08ebc76b093f3e17fed29281f5cb637e 0000-0002-3951-0409 Hywel Thomas Hywel Thomas true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2021-12-07 CIVL PurposeThe main purpose of this paper is to devise a tool, based on Computational Fluid Dynamics (CFD) and Machine Learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A Gated Recurrent Units Neural Network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking datasets.Design/methodology/approachA computational methodology is used for investigating how infectious particles originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor air flow is obtained by means of an in-house parallel CFD solver which employs a one equation Spalrat–Allmaras (SA) turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted human breath. The numerical results are used to the ML training.FindingIn this work, it is shown that the developed ML model, based on the Gated Recurrent Units Neural Network (GRU-NN), can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results inthe paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.Originality/valueThis study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environments, potentially leading to new design. A parametric study is carried out to evaluate the impact of system settings on the time variation particles emitted human breath within the space considered. Journal Article International Journal of Numerical Methods for Heat &amp; Fluid Flow 32 9 2964 2981 Emerald 0961-5539 COVID-19 infection, CFD modelling, Spalrat–Allmaras (SA) model, Particle tracking, Inhalation airflow, Recurrent Neural Network, Gated Recurrent Units (GRU) 20 7 2022 2022-07-20 10.1108/hff-07-2021-0498 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2022-07-25T16:37:35.6019745 2021-12-07T09:51:54.1161588 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Hamid Tamaddon-Jahromi 1 Igor Sazonov 0000-0001-6685-2351 2 Jason Jones 0000-0002-7715-1857 3 Alberto Coccarelli 0000-0003-1511-9015 4 Sam Rolland 0000-0003-0455-5620 5 Neeraj Kavan Chakshu 6 Hywel Thomas 0000-0002-3951-0409 7 Perumal Nithiarasu 0000-0002-4901-2980 8 58941__21826__ba6c0708fb2846db9104fe7d66fbce29.pdf 58941.pdf 2021-12-07T09:59:36.9530928 Output 3696230 application/pdf Accepted Manuscript true Released under the terms of a Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0) true eng
title Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
spellingShingle Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
Hamid Tamaddon-Jahromi
Igor Sazonov
Jason Jones
Alberto Coccarelli
Sam Rolland
Neeraj Kavan Chakshu
Hywel Thomas
Perumal Nithiarasu
title_short Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
title_full Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
title_fullStr Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
title_full_unstemmed Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
title_sort Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
author_id_str_mv b3a1417ca93758b719acf764c7ced1c5
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c14ac34a71e9c058d1d2a353b44a24cd
e21c85ee9062e9be0fff8ab9d77b14d7
08ebc76b093f3e17fed29281f5cb637e
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author_id_fullname_str_mv b3a1417ca93758b719acf764c7ced1c5_***_Hamid Tamaddon-Jahromi
05a507952e26462561085fb6f62c8897_***_Igor Sazonov
aa4865d48c53a0df1c1547171826eab9_***_Jason Jones
06fd3332e5eb3cf4bb4e75a24f49149d_***_Alberto Coccarelli
c14ac34a71e9c058d1d2a353b44a24cd_***_Sam Rolland
e21c85ee9062e9be0fff8ab9d77b14d7_***_Neeraj Kavan Chakshu
08ebc76b093f3e17fed29281f5cb637e_***_Hywel Thomas
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
author Hamid Tamaddon-Jahromi
Igor Sazonov
Jason Jones
Alberto Coccarelli
Sam Rolland
Neeraj Kavan Chakshu
Hywel Thomas
Perumal Nithiarasu
author2 Hamid Tamaddon-Jahromi
Igor Sazonov
Jason Jones
Alberto Coccarelli
Sam Rolland
Neeraj Kavan Chakshu
Hywel Thomas
Perumal Nithiarasu
format Journal article
container_title International Journal of Numerical Methods for Heat &amp; Fluid Flow
container_volume 32
container_issue 9
container_start_page 2964
publishDate 2022
institution Swansea University
issn 0961-5539
doi_str_mv 10.1108/hff-07-2021-0498
publisher Emerald
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
document_store_str 1
active_str 0
description PurposeThe main purpose of this paper is to devise a tool, based on Computational Fluid Dynamics (CFD) and Machine Learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A Gated Recurrent Units Neural Network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking datasets.Design/methodology/approachA computational methodology is used for investigating how infectious particles originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor air flow is obtained by means of an in-house parallel CFD solver which employs a one equation Spalrat–Allmaras (SA) turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted human breath. The numerical results are used to the ML training.FindingIn this work, it is shown that the developed ML model, based on the Gated Recurrent Units Neural Network (GRU-NN), can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results inthe paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.Originality/valueThis study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environments, potentially leading to new design. A parametric study is carried out to evaluate the impact of system settings on the time variation particles emitted human breath within the space considered.
published_date 2022-07-20T04:15:52Z
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score 11.013037

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