A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMirzaei, Parham A.en_US
dc.contributor.authorMoshfeghi, Mohammaden_US
dc.contributor.authorMotamedi, Hamiden_US
dc.contributor.authorSheikhnejad, Yahyaen_US
dc.contributor.authorBordbar, Hadien_US
dc.contributor.departmentDepartment of Civil Engineeringen
dc.contributor.groupauthorStructures – Structural Engineering, Mechanics and Computationen
dc.contributor.organizationUniversity of Nottinghamen_US
dc.contributor.organizationTarbiat Modares Universityen_US
dc.contributor.organizationUniversity of Aveiroen_US
dc.contributor.organizationSogang Universityen_US
dc.date.accessioned2022-02-09T06:52:37Z
dc.date.available2022-02-09T06:52:37Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2024-01-01en_US
dc.date.issued2022-01en_US
dc.description.abstractCOVID19 pathogens are primarily transmitted via airborne respiratory droplets expelled from infected bio-sources. However, there is a lack of simplified accurate source models that can represent the airborne release to be utilized in the safe-social distancing measures and ventilation design of buildings. Although computational fluid dynamics (CFD) can provide accurate models of airborne disease transmissions, they are computationally expensive. Thus, this study proposes an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model. The dataset has been generated based on key clinical parameters (i.e., the velocity of droplet release) and environmental factors (i.e., room temperature and relative humidity) in the droplet release modes. An Eulerian CFD model is first validated against experimental data and then interlinked with a Lagrangian CFD model to simulate trajectory and evaporation of numerous droplets in various sizes (0.1 μm–700 μm). A risk assessment model previously developed by the authors is then applied to the simulation cases to identify the horizontal and vertical spread lengths (risk cloud) of viruses in each case within an exposure time. Eventually, an artificial neural network-based model is fitted to the spread lengths to develop the simplified predictive source model. The results identify three main regimes of risk clouds, which can be fairly predicted by the ANN model.en
dc.description.versionPeer revieweden
dc.format.extent15
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMirzaei, P A, Moshfeghi, M, Motamedi, H, Sheikhnejad, Y & Bordbar, H 2022, ' A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach ', Building and Environment, vol. 207 A, 108428 . https://doi.org/10.1016/j.buildenv.2021.108428en
dc.identifier.doi10.1016/j.buildenv.2021.108428en_US
dc.identifier.issn0360-1323
dc.identifier.issn1873-684X
dc.identifier.otherPURE UUID: f2e03f92-d98f-4ad1-bcc3-b211a477a07ben_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/f2e03f92-d98f-4ad1-bcc3-b211a477a07ben_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85118736375&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/79135358/ENG_Mirzaei_et_al_A_simplified_tempo_spatial_model_to_predict_Building_and_Environment.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/112973
dc.identifier.urnURN:NBN:fi:aalto-202202091866
dc.language.isoenen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofseriesBuilding and Environmenten
dc.relation.ispartofseriesVolume 207 Aen
dc.rightsopenAccessen
dc.subject.keywordtempo-spatial risk modelen_US
dc.subject.keywordCOVID19en_US
dc.subject.keywordairborne pathogen transmissionen_US
dc.subject.keywordEulerian-Lagrangian-CFDen_US
dc.subject.keywordrespiratory diseaseen_US
dc.subject.keywordartificial neural networken_US
dc.titleA simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approachen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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