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Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network
International Journal of Numerical Methods for Heat & Fluid Flow, Volume: 32, Issue: 9, Pages: 2964 - 2981
Swansea University Authors: Hamid Tamaddon-Jahromi, Igor Sazonov , Jason Jones , Alberto Coccarelli , Sam Rolland , Neeraj Kavan Chakshu, Hywel Thomas , Perumal Nithiarasu
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DOI (Published version): 10.1108/hff-07-2021-0498
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...
|Published in:||International Journal of Numerical Methods for Heat & Fluid Flow|
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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.
COVID-19 infection, CFD modelling, Spalrat–Allmaras (SA) model, Particle tracking, Inhalation airflow, Recurrent Neural Network, Gated Recurrent Units (GRU)
Faculty of Science and Engineering