with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article

Authors

1 MSc Student, Department of Agricultural Machinery and Mechanization, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

2 Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

3 Department of Horticultural Science, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

Abstract

Introduction
Greenhouse cultivation is the popular intensive kind of crop production with a yield per cultivated unit area more than 10 times higher compared to field crops. Greenhouse production requires the use of large amounts of energy, water, and pesticides and it usually generates huge quantities of wastes to be disposed of it. Investment, labor, and energy costs per unit area are much higher in the greenhouse industry than in any other agricultural sectors. Sustainable greenhouse systems, socially supportive, commercially competitive, and environmentally sound, depend on cultivation techniques, equipment management, and constructive materials that aim to reduce agrochemicals, energy and water consumption as well as waste generation. The management of the greenhouse environment is depending on temperature manipulation. Temperature manipulation is critical to influencing plant growth, quality, and morphology and so is a major strategy in the environmental modification of crops. Heterogeneous indoor microclimate of a greenhouse has long become a matter of concern in many studies. It is believed to be unfavorable for crop growth, which damages crop activity, particularly transpiration and photosynthesis, one of the major causes of non-uniform production and quality. Since early and conventional methods are not sufficient to evaluate microclimate variables inside a greenhouse, Computational Fluid Dynamics (CFD) approach was applied for better and more accurate results. CFD is an effective numerical analysis technique to predict the distribution of the climatic variables inside cultivation facilities. Numerous studies have focused on the internal temperature, humidity, solar radiation, and airflow inside multiple cultivation facilities. For example, the CFD method was used to simulate natural ventilation for agricultural buildings and improve crop production systems. The CFD simulation and evaluation models could be applied for evaluation of the inside situation and temperature in greenhouses. Thermal and water vapor transfer is influenced by the openings of greenhouses in the CFD simulation. The CFD model was developed to predict the distribution of temperature, water vapor, and CO2 occurring in a Venlo-type semi-closed glass greenhouse equipped with air conditioners. Based on the above literature, this research aims to evaluate the energy flow and modeling of an un-even semi-buried greenhouse using external and internal variables and numerical solutions by the CFD method.
Materials and Methods
In this study, Computational Fluid Dynamic (CFD) solution was applied to evaluate the inside environment of a semi-double glass greenhouse with an east-west location. This greenhouse has a special structure that is used in very hot or very cold areas due to its depth of more than one meter below the ground. The greenhouse has an area of 38m2 and an air volume of 78.8m3. The temperature and humidity data were collected from inside and outside the greenhouse by temperature sensors (SHT 11 model made by CMOS USA). Irradiation data were collected inside the greenhouse, on level ground, by the TES132 radiometer.
Results and Discussion
In this study, the CFD method was used for a model solution with ANSYS Fluent version 2020R2 software. To evaluate the predictive capability of the model and its optimization, the comparison between actual (ya) and predicted values (yp) was used. Three criteria of RMSE, MAPE, and R2 were also used to evaluate the accuracy of the final model. The results showed that the dynamic model can accurately estimate the temperature of the air inside the greenhouse at a height of 1 m (R2 = 0.987, MAPE = 2.17%) and 2 m (R2 = 0.987, MAPE = 2.28%) from the floor. The results of energy flow showed that this greenhouse transfers 6779.4.4 kJ of accumulated thermal energy to the ground during the experiment.
Conclusion
In the present study, the computational fluid dynamics method was used to simulate the internal conditions of an un-even semi-buried greenhouse with external and internal variables including temperature and solar radiation. The results showed that this greenhouse structure is able to transfer part of the increase in temperature caused by sunlight to the soil depth (104.214 kJm-2 heat through the floor, 178.443 kJm-2 through the north wall and 113.757 kJm-2 through the south wall). By increasing the thermal conductivity of the inner surface of the greenhouse, the heat flux to the depth of the soil can be increased.

Keywords

Main Subjects

Open Access

©2021 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

  1. Abdel Ghany, A. M., & Helal, I. M. (2011). Solar energy utilization by a greenhouse: general relations. Renewable Energy, 36, 189-196. https://doi.org/10.1016/j.renene.2010.06.020.
  2. Abu-Hamdeh, N. H., & Reeder, R. C. (2000). Soil thermal conductivity effects of density, moisture, salt concentration, and organic matter. Soil Science Society of America Journal, 64(4), 1285-1290. https://doi.org/10.2136/sssaj2000.6441285x.
  3. Ahamed, S., Guo, H., & Tanino, K. 2019. Energy saving techniques for reducing the heating cost of conventional greenhouses. Biosystems Engineering, 178, 9-33. https://doi.org/10.1016/j.biosystemseng.2018.10.017.
  4. Bergman, T. L., Incropera, F. P., Lavine, A. S., & Dewitt, D. P. (2011). Introduction to heat transfer. John Wiley & Sons. Amsterdam.
  5. Bolandnazar, E., Sadrnia, H., Rohani, A., & Taki, M. (2019). Prediction of Temperature in a Greenhouse Covered with Polyethylene Plastic Using Artificial Neural Networks, Case Study: Jiroft Region. Iranian Journal of Biosystem Engineering, 51(1), 125-137. https://doi.org/10.22059/ijbse.2019.291077.665235.
  6. Dayioğlu, M. A., & Silleli, H. H. (2015). Performance analysis of a greenhouse fan-pad cooling system: gradients of horizontal temperature and relative humidity. Journal of Agricultural Science, 21, 132-143. https://doi.org/10.15832/TBD.25721.
  7. Fidaros, D. K., Baxevanou, C. A., Bartzanas, T., & Kittas, C. (2010). Numerical simulation of thermal behavior of a ventilated arc greenhouse during a solar day. Renewable Energy, 35, 1380-1386. https://doi.org/10.1016/j.renene.2009.11.013.
  8. Ghani, S., Bakochristou, F., ElBialy, E. M., Gamaledin, A. A., Rashwan, S. M. A., Abdelhalim, M. M., & Ismail, S. M. 2019. Design challenges of agricultural greenhouses in hot and arid environments– A review. Engineering in Agriculture, Environment and Food, 12, 48-70. https://doi.org/10.1016/j.eaef.2018.09.004.
  9. Ghasemi Mobtaker, H., Ajabshirchi, Y., Ranjbar, S. F., & Matloobi, M. (2019). Simulation of thermal performance of solar greenhouse in north-west of Iran: An experimental validation. Renewable Energy, 135, 88-97. https://doi.org/10.1016/j.renene.2018.10.003.
  10. Hamdani, M., Taki, M., Rahnama, M., Rohani, A., & Rahmati-joneidabad, M. (2020). Prediction the inside Variables of Even-span Glass Greenhouse with Special Structure by Artificial Neural Network (MLP-RBF) Models. Journal of Agricultural Machinery, 10(2), 213-227. (in Persian with English abstract). https://doi.org/10.22067/jam.v10i2.72346
  11. Holman, J. P. (2010). Heat Transfer. Eighth ed. McGraw-Hill Science, New York.
  12. Jiao, W., Qi, L., Lijun, G., Kunyu, L., Shi, R., & Ta, N. (2020). Computational Fluid Dynamics-Based Simulation of Crop CanopyTemperature and Humidity in Double-Film Solar Greenhouse. Journal of Sensors, 1-15. https://doi.org/10.1155/2020/8874468.
  13. Joudi, K., & Farhan, A. (2015). A dynamic model and an experimental study for the internal air and soil temperatures in an innovative greenhouse. Energy Conversion and Management, 91, 76-82. https://doi.org/10.1016/j.enconman.2014.11.052.
  14. Moghaddam, J. J., Ozlati, S., Zarei, Gh., Momeni, D., & Azadshahraki, F. (2021). Ventilation and Cooling Modeling and Lyout of Fans, Pads and Vents of an Octagonal Greenhouse. Journal of Agricultural Machinery, 11(2), 247-262. (in Persian with English abstract). https://doi.org/10.22067/jam.v11i2.82130.
  15. Munar, E., & Aldana, C. (2019). CFD Simulation of the Increase of the Roof Ventilation Area in a Traditional Colombian Greenhouse: Effect on Air Flow Patterns and Thermal Behavior. International Journal of Heat and Technology, 7(3), 881-892. http://doi.org/10.18280/ijht.370326
  16. Nadi, F., Abdanan Mehdizadeh, S., & Nourani Zonouz, O. (2016). Comparing between predicted output temperature of flat-plate solar collector and experimental results: computational fluid dynamics and artificial neural network. Journal of Agricultural Machinery, 7(1), 298-311. (in Persian with English abstract). https://doi.org/10.22067/jam.v7i1.59698.
  17. Pakari, A., & Ghani, S. (2019). Evaluation of a novel greenhouse design for reduced cooling loads during the hot season in subtropical regions. Solar Energy, 181, 234-242. https://doi.org/10.1016/j.solener.2019.02.006.
  18. Roy, J. C., Boulard, T., Kittas, C., & Wang, S. 2002. Convective and ventilation transfers in greenhouses, Part 1: the greenhouse considered as a perfectly stirred tank. Biosystems Engineering, 83, 1-20. https://doi.org/10.1006/bioe.2002.0107.
  19. Saberian, A., & Sajadiye, S. M. (2019). The effect of dynamic solar heat load on the greenhouse microclimate using CFD simulation. Renewable Energy, 138, 722-737. https://doi.org/10.1016/j.renene.2019.01.108.
  20. Santolini, E., Pulvirenti, B., Benni, S., Barbaresi, L., Torreggiani, D., & Tassinari, P. (2018). Numerical study of wind-driven natural ventilation in a greenhouse with screens. Computers and Electronics in Agriculture, 149, 41-53. https://doi.org/10.1016/j.compag.2017.09.027.
  21. Taki, M., Ajabshirchi, Y., Ranjbar, S. F., Rohani, A., & Matloobi, M. (2016a). Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse. Energy and Buildings, 110, 314-29. https://doi.org/10.1016/j.enbuild.2015.11.010.
  22. Taki, M., Ajabshirchi, Y., Ranjbar, S. F., Rohani, A., & Matloobi, M. (2016b). Modeling and experimental validation of heat transfer and energy consumption in an innovative greenhouse structure. Information Processing in Agriculture, 3, 157-174. https://doi.org/10.1016/j.inpa.2016.06.002.
  23. Taki, M., Rohani, A., & Rahmati-Joneidabad, M. (2018). Solar thermal simulation and applications in greenhouse. Information Processing in Agriculture, 5, 83-113. https://doi.org/10.1016/j.inpa.2017.10.003.
  24. Wang, J., Li, S., Guo, S., Ma, C., Wang, J., & Sun, J. (2017). Analysis of heat transfer properties of hollow block wall filled by different materials in solar greenhouse. Engineering in Agriculture, Environment and Food, 10, 31-38. https://doi.org/10.1016/j.eaef.2016.07.003.
  25. Zhang, X., You, S., Tian, Y., & Li, J. (2019). Comparison of plastic film, biodegradable paper and bio-based film mulching for summer tomato production: Soil properties, plant growth, fruit yield and fruit quality. Sciatica Horticulture, 249, 38-48. https://doi.org/10.1016/j.scienta.2019.01.037.
CAPTCHA Image