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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| Title: | Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach. |
|---|---|
| Authors: | Quang, Tran Van1 (AUTHOR) tranvanquang2912@khu.ac.kr, Doan, Dat Tien2 (AUTHOR), Phuong, Nguyen Lu3 (AUTHOR), Yun, Geun Young1 (AUTHOR) |
| Source: | Journal of Building Physics. Jan2024, Vol. 47 Issue 4, p439-471. 33p. |
| Subjects: | Natural ventilation, Machine learning, Air flow, Computational fluid dynamics, Indoor air quality, Dwellings, Temperature distribution |
| Abstract: | Predicting 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Building Physics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174972659 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Quang%2C+Tran+Van%22">Quang, Tran Van</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tranvanquang2912@khu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Doan%2C+Dat+Tien%22">Doan, Dat Tien</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Phuong%2C+Nguyen+Lu%22">Phuong, Nguyen Lu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yun%2C+Geun+Young%22">Yun, Geun Young</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Building+Physics%22">Journal of Building Physics</searchLink>. Jan2024, Vol. 47 Issue 4, p439-471. 33p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Natural+ventilation%22">Natural ventilation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Air+flow%22">Air flow</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Indoor+air+quality%22">Indoor air quality</searchLink><br /><searchLink fieldCode="DE" term="%22Dwellings%22">Dwellings</searchLink><br /><searchLink fieldCode="DE" term="%22Temperature+distribution%22">Temperature distribution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Predicting 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Building Physics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/17442591231219025 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 439 Subjects: – SubjectFull: Natural ventilation Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Air flow Type: general – SubjectFull: Computational fluid dynamics Type: general – SubjectFull: Indoor air quality Type: general – SubjectFull: Dwellings Type: general – SubjectFull: Temperature distribution Type: general Titles: – TitleFull: Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Quang, Tran Van – PersonEntity: Name: NameFull: Doan, Dat Tien – PersonEntity: Name: NameFull: Phuong, Nguyen Lu – PersonEntity: Name: NameFull: Yun, Geun Young IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 17442591 Numbering: – Type: volume Value: 47 – Type: issue Value: 4 Titles: – TitleFull: Journal of Building Physics Type: main |
| ResultId | 1 |
