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

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:27693295
DOI:10.13031/ja.16019