Data-Driven Prediction of Indoor Airflow Distribution in Naturally Ventilated Residential Buildings Using Combined CFD Simulation and Machine Learning (ML) Approach

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aut.relation.endpage34
aut.relation.journalJournal of Building Physics
aut.relation.startpage1
dc.contributor.authorQuang, Tran Van
dc.contributor.authorDoan, Dat
dc.contributor.authorPhuong, Nguyen Lu
dc.contributor.authorYun, Geun Young
dc.date.accessioned2024-10-07T20:52:13Z
dc.date.available2024-10-07T20:52:13Z
dc.date.issued2024-01-10
dc.description.abstractPredicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.
dc.identifier.citationJournal of Building Physics, ISSN: 1744-2583 (Print); 1744-2583 (Online), SAGE Publications, 1-34.
dc.identifier.doi10.1177/17442591231219025
dc.identifier.issn1744-2583
dc.identifier.issn1744-2583
dc.identifier.urihttp://hdl.handle.net/10292/18110
dc.publisherSAGE Publications
dc.relation.urihttps://journals.sagepub.com/doi/10.1177/17442591231219025
dc.rightsUnder Sage's Green Open Access policy, the Accepted Version of the article may be posted in the author's institutional repository and reuse is restricted to non-commercial and no derivative uses.
dc.rights.accessrightsOpenAccess
dc.subject0204 Condensed Matter Physics
dc.subject1202 Building
dc.subjectBuilding & Construction
dc.subject3302 Building
dc.titleData-Driven Prediction of Indoor Airflow Distribution in Naturally Ventilated Residential Buildings Using Combined CFD Simulation and Machine Learning (ML) Approach
dc.typeJournal Article
pubs.elements-id534435
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