No Cover Image

Journal article 167 views 95 downloads

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

  • 58941.pdf

    PDF | Accepted Manuscript

    Released under the terms of a Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0)

    Download (3.52MB)

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...

Full description

Published in: International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Published: Emerald 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58941
Tags: Add Tag
No Tags, Be the first to tag this record!
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 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.
Keywords: COVID-19 infection, CFD modelling, Spalrat–Allmaras (SA) model, Particle tracking, Inhalation airflow, Recurrent Neural Network, Gated Recurrent Units (GRU)
College: College of Engineering
Issue: 9
Start Page: 2964
End Page: 2981