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Optimization of Greenhouse Structure Parameters Based on Temperature and Velocity Distribution Characteristics by CFD—A Case Study in South China

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  • Xinyu Wei

    (Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Yizhi Ou

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Ziwei Li

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Jiaming Guo

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Enli Lü

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Fengxi Yang

    (Environmental Horticulture Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China)

  • Yanhua Liu

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Bin Li

    (Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China)

Abstract

Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China.

Suggested Citation

  • Xinyu Wei & Yizhi Ou & Ziwei Li & Jiaming Guo & Enli Lü & Fengxi Yang & Yanhua Liu & Bin Li, 2025. "Optimization of Greenhouse Structure Parameters Based on Temperature and Velocity Distribution Characteristics by CFD—A Case Study in South China," Agriculture, MDPI, vol. 15(15), pages 1-19, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1660-:d:1715119
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    References listed on IDEAS

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    1. Tsafaras, I. & Campen, J.B. & Stanghellini, C. & de Zwart, H.F. & Voogt, W. & Scheffers, K. & Harbi, A. Al & Assaf, K. Al, 2021. "Intelligent greenhouse design decreases water use for evaporative cooling in arid regions," Agricultural Water Management, Elsevier, vol. 250(C).
    2. Kangji Li & Wenping Xue & Hanping Mao & Xu Chen & Hui Jiang & Gang Tan, 2019. "Optimizing the 3D Distributed Climate inside Greenhouses Using Multi-Objective Optimization Algorithms and Computer Fluid Dynamics," Energies, MDPI, vol. 12(15), pages 1-19, July.
    3. Singh, Gurpreet & Singh, Parm Pal & Lubana, Prit Pal Singh & Singh, K.G., 2006. "Formulation and validation of a mathematical model of the microclimate of a greenhouse," Renewable Energy, Elsevier, vol. 31(10), pages 1541-1560.
    4. Lin, Dong & Zhang, Lijun & Xia, Xiaohua, 2021. "Model predictive control of a Venlo-type greenhouse system considering electrical energy, water and carbon dioxide consumption," Applied Energy, Elsevier, vol. 298(C).
    5. Bonachela, Santiago & Fernández, María Dolores & Hernández, Joaquín & Karaca, Cihan, 2024. "Computing air temperature and humidity for reference crop evapotranspiration calculation in passive Mediterranean greenhouses," Agricultural Water Management, Elsevier, vol. 302(C).
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