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Ventilation Rate Prediction in Naturally Ventilated Greenhouses Using a CFD-Driven Machine Learning Model.

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Title: Ventilation Rate Prediction in Naturally Ventilated Greenhouses Using a CFD-Driven Machine Learning Model.
Authors: Park, Sejun1 (AUTHOR), Lee, In-Bok2,3,4 (AUTHOR) iblee@snu.ac.kr, Seo, Jeongwook5 (AUTHOR), Yeo, Uk-Hyeon6 (AUTHOR), Cho, Jeong-Hwa7 (AUTHOR), Decano-Valentin, Cristina8 (AUTHOR)
Source: Journal of the ASABE. 2025, Vol. 68 Issue 4, p573-589. 17p.
Subjects: Machine learning, Computational fluid dynamics, Air flow, Greenhouses, Plant health, Ventilation, Prediction models
Geographic Terms: South Korea
Abstract: The article focuses on the development of a machine learning model, termed the Prediction Local Ventilation Rate CFD-driven Machine Learning model (PLV-CFD-driven ML), designed to predict the local ventilation rate in naturally ventilated greenhouses using Computational Fluid Dynamics (CFD) simulations. The study highlights the importance of effective greenhouse ventilation for crop health and productivity, particularly in Korea, where single-span greenhouses dominate. It discusses the limitations of traditional airflow measurement methods and the advantages of using CFD simulations to generate training data for machine learning models. The research demonstrates that the PLV-CFD-driven ML model can enhance prediction accuracy while reducing the computational burden associated with extensive CFD simulations, making it a valuable tool for optimizing greenhouse ventilation systems. [Extracted from the article]
Copyright of Journal of the ASABE is the property of American Society of Agricultural & Biological Engineers 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
Header DbId: egs
DbLabel: Engineering Source
An: 187503851
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Ventilation Rate Prediction in Naturally Ventilated Greenhouses Using a CFD-Driven Machine Learning Model.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Park%2C+Sejun%22">Park, Sejun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+In-Bok%22">Lee, In-Bok</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<i> iblee@snu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Seo%2C+Jeongwook%22">Seo, Jeongwook</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yeo%2C+Uk-Hyeon%22">Yeo, Uk-Hyeon</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cho%2C+Jeong-Hwa%22">Cho, Jeong-Hwa</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Decano-Valentin%2C+Cristina%22">Decano-Valentin, Cristina</searchLink><relatesTo>8</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+ASABE%22">Journal of the ASABE</searchLink>. 2025, Vol. 68 Issue 4, p573-589. 17p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Air+flow%22">Air flow</searchLink><br /><searchLink fieldCode="DE" term="%22Greenhouses%22">Greenhouses</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+health%22">Plant health</searchLink><br /><searchLink fieldCode="DE" term="%22Ventilation%22">Ventilation</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22South+Korea%22">South Korea</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The article focuses on the development of a machine learning model, termed the Prediction Local Ventilation Rate CFD-driven Machine Learning model (PLV-CFD-driven ML), designed to predict the local ventilation rate in naturally ventilated greenhouses using Computational Fluid Dynamics (CFD) simulations. The study highlights the importance of effective greenhouse ventilation for crop health and productivity, particularly in Korea, where single-span greenhouses dominate. It discusses the limitations of traditional airflow measurement methods and the advantages of using CFD simulations to generate training data for machine learning models. The research demonstrates that the PLV-CFD-driven ML model can enhance prediction accuracy while reducing the computational burden associated with extensive CFD simulations, making it a valuable tool for optimizing greenhouse ventilation systems. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the ASABE is the property of American Society of Agricultural & Biological Engineers 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187503851
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.13031/ja.16019
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 573
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Computational fluid dynamics
        Type: general
      – SubjectFull: Air flow
        Type: general
      – SubjectFull: Greenhouses
        Type: general
      – SubjectFull: Plant health
        Type: general
      – SubjectFull: Ventilation
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: South Korea
        Type: general
    Titles:
      – TitleFull: Ventilation Rate Prediction in Naturally Ventilated Greenhouses Using a CFD-Driven Machine Learning Model.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Park, Sejun
      – PersonEntity:
          Name:
            NameFull: Lee, In-Bok
      – PersonEntity:
          Name:
            NameFull: Seo, Jeongwook
      – PersonEntity:
          Name:
            NameFull: Yeo, Uk-Hyeon
      – PersonEntity:
          Name:
            NameFull: Cho, Jeong-Hwa
      – PersonEntity:
          Name:
            NameFull: Decano-Valentin, Cristina
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          Dates:
            – D: 01
              M: 07
              Text: 2025
              Type: published
              Y: 2025
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              Value: 27693295
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            – Type: volume
              Value: 68
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of the ASABE
              Type: main
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