Evaluating the Efficiency of Different Cover Forms of the Large Spans in Flowers and Plant Exhibitions Based on the Natural Ventilation Vystem in a Moderate and Humid Climate

Document Type : Original Article

Authors

1 M.Arch., Department of Architecture, College of Fine Arts, University of Tehran, Tehran, Iran.

2 Associate Professor, Department of Architecture, College of Fine Arts, University of Tehran, Tehran, Iran.

3 Member of The Industrial Design Department Scientific Board, College of Fine Arts, University of Tehran, Tehran, Iran.

Abstract

Deciding the roof type with a large ventilation spans for uses in the flower and plant exhibitions that can operate beyond the exhibition space functions as it can provide a desirable climate for the growth of its plants, it can be designed and enhanced according to the geographical site of it. Deciding and designing the roof form can prevent dissipations in energy and assets and develops a construction with high efficiency together with low costs of maintenance, only if it is done in an intelligent way. The independent variables in this research are the climate conditions, and form of the structure is considered as the intervening variables together with factors like the internal air current and sub climates and the levels of thermal comfort for individual occupants as the dependent variables. The aim of conducting this master thesis which is considered as an interdisciplinary research, is to reach for proper patterns in covering the ventilators in greenhouses with large spans by using the climate information of the north-Iran region. The main question of this research is the most efficient roof form in regard to natural ventilation in mild and humid climate condition? Research method the study is modeling and computer simulation in a way that they are evaluated with the prevalent forms of exhibitions and greenhouses with large vents in the terms of the external wind flow impacts and natural ventilation in their interior and analyzed by employing Computational Fluid Dynamics (CFD) and moving particle semi-implicit (MPS) simulation. Results indicate that form of a building has an obvious impact on the internal airflow and the curved forms have a better impact on the internal circulation of air. As an instance, in the convex geometries, the airflow speed rate drops in the center of the construction while in the concave geometries it is quite the opposite as the speed is reduced around the sidewalls of the construction and the thermal comfort becomes a different point along with natural ventilation.

Keywords

References

  1. Baeza, E. J., Perez-Parra, J. J., Lopez, J. C., & Montero, J. I. (2007, October). CFD simulation of natural ventilation of a parral greenhouse with a baffle device below the greenhouse vents. In International Symposium on High Technology for Greenhouse System Management: Greensys 2007 801 (pp. 885-892). doi: 10.17660/Acta Hortic.2008.801.104.
  2. Bartzanas, T., Boulard, T. and Kittas, C. (2004). Effect of vent arrangement on windward ventilation of a tunnel greenhouse. Biosystems Engineering, 88(4):479- 490. doi:10.1016/j.biosystems eng.2003.10.006.
  3. Perén, J. I., van Hooff, T., Leite, B. C. C., &Blocken, B. (2016). CFD simulation of wind-driven upward cross ventilation and its enhancement in long buildings: Impact of single-span versus double-span leeward sawtooth roof and opening ratio. Building and Environment, 96, 142-156. doi: 10.1016/j.buildenv.2015.11.021.
  4. Kottek, M., Grieser, J., Beck, C., Rudolf, B., &Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. MeteorologischeZeitschrift, 15(3), 259-263. DOI: 10.1127/0941-2948/2006/0130.
  5. Anderson, J. D., & Wendt, J. (1995). Computational fluid dynamics (Vol. 206). New York: McGraw-Hill.
  6. Ameer, S. A., Chaudhry, H. N., & Agha, A. (2016). Influence of roof topology on the air distribution and ventilation effectiveness of wind towers. Energy and Buildings, 130, 733-746. https://doi.org/10.1016/j.enbuild.2016.09.005.
  7. Khaoua, S. O., Bournet, P. E., Migeon, C., Boulard, T., &Chassériaux, G. (2006). Analysis of greenhouse ventilation efficiency based on computational fluid dynamics. Biosystems Engineering, 95(1), 83-98. https://doi.org/10.1016/j.biosystemseng.2006.05.004.
  8. Bournet, P. E., Khaoua, S. O., Boulard, T., Migeon, C., &Chassériaux, G. (2004). EFFECT OF ROOF AND SIDE OPENING COMBINATIONS.
  9. Kim, T., Kim, K., & Kim, B. S. (2010). A wind tunnel experiment and CFD analysis on airflow performance of enclosed-arcade markets in Korea. Building and Environment, 45(5), 1329-1338. https://doi.org/10.1016/j.buildenv.2009.11.016.
  10. Rico-García, E., Lopez-Cruz, I. L., Herrera-Ruiz, G., Soto-Zarazua, G. M., & Castaneda-Miranda, R. (2008). Effect of temperature on greenhouse natural ventilation under hot conditions: Computational Fluid Dynamics simulations. J. Appl. Sci, 8, 4543-4551.
  11. Roy, J. C., Vidal, C., Fargues, J., &Boulard, T. (2008). CFD based determination of temperature and humidity at leaf surface. Computers and Electronics in Agriculture, 61(2), 201-212. https://doi.org/10.1016/j.compag.2007.11.007.
  12. Ramponi, R., &Blocken, B. (2012). CFD simulation of cross-ventilation for a generic isolated building: impact of computational parameters. Building and Environment, 53, 34-48. doi:10.1016/j.buildenv.2012.01.004.
  13. Endalew, A. M., Hertog, M., Delele, M. A., Baetens, K., Persoons, T., Baelmans, M., ...&Verboven, P. (2009). CFD modelling and wind tunnel validation of airflow through plant canopies using 3D canopy architecture. International Journal of Heat and Fluid Flow, 30(2), 356-368. doi:10.1016/j.ij heat fluid flow.2008.12.007.
  14. Zhai, Z., & Chen, Q. Y. (2004). Numerical determination and treatment of convective heat transfer coefficient in the coupled building energy and CFD simulation. Building and Environment, 39(8), 1001-1009. doi:10.1016/j.buildenv.2004.01.023.
  15. Kaijima, S., Bouffanais, R., Willcox, K., & Naidu, S. (2013). Computational fluid dynamics for architectural design. Architectural Design, 83(2), 118-123. doi: 10.1002/ad.1566.
  16. Schmid, F., Burrell, G. (2004). CFD Analysis challenges in building simulation for SIMBUILD 2004 Conference.
  17. Couto, N., Rouboa, A., Monteiro, E., &Viera, J. (2012). Computational Fluid Dynamics Analysis of Greenhouses with Artificial Heat Tube. doi:10.4236/wjm.2012.24022.
  18. Pontikakos, C., Ferentinos, K. P., Tsiligiridis, T. A., &Sideridis, A. B. (2006, September). Natural ventilation efficiency in a twin-span greenhouse using 3D computational fluid dynamics. In Of the 3rd International Conference on Information and Communication Technologies in Agriculture, September (pp. 20-23).
  19. De la Torre-Gea, G., Soto-Zarazúa, G. M., López-Cruz, I., Torres-Pacheco, I., & Rico-García, E. (2011). Computational fluid dynamics in greenhouses: A review. African Journal of Biotechnology, 10(77), 17651-17662. doi: 10.5897/AJB10.2488.
  20. Molina-Aiz, F. D., Valera, D. L., &Álvarez, A. J. (2004). Measurement and simulation of climate inside Almerıa-type greenhouses using computational fluid dynamics. Agricultural and Forest Meteorology, 125(1), 33-51. doi:10.1016/j.agrformet.2004.03.009.
  21. Abdeen, M. O. (2009). The effect of air pollution and thermal comfort in greenhouses.
  22. Bartok Jr, J. W., & Aldrich, R. A. (1983, August). Low cost solar collectors for greenhouse water heating. In III International Symposium on Energy in Protected Cultivation 148 (pp. 771-774).
  23. Pedlosky, Joseph (1987). Geophysical fluid dynamics. Springer. pp. 10–13. ISBN 978-0-387-96387-7.
  24. Standard, A. S. H. R. A. E. (2004). Standard 55-2004. Thermal environmental conditions for human occupancy, 9-11.
  25. Niktash, Amirreza. Huynh, Phuoc. (2014). CFD Simulation and Analysis of a Two-sided Windcatcher’s Inlet/Outlet Geometric Shape Effect in Ventilation Flow Through a Three Dimensional Room, 19th Australasian Fluid Mechanics Conference, Melbourne, Australia, 8-11 December 2014.
  26. Shane, F. (2011). Pedestrian Level Wind Study. Toronto, Ontario, Canada.
  27. Yao, R., Li, B., & Liu, J. (2009). A theoretical adaptive model of thermal comfort–Adaptive Predicted Mean Vote (aPMV). Building and environment, 44(10), 2089-2096.
  28. Thorsson, S., Lindberg, F., Eliasson, I., &Holmer, B. (2007). Different methods for estimating the mean radiant temperature in an outdoor urban setting. International journal of climatology, 27(14), 1983-1993.
  29. Bartak, M., Cermak, M., Clarke, J. A., Denev, J., Drkal, F., Lain, M., ...&Stankov, P. (2001). Experimental and numerical study of local mean age of air.
  30. Olesen, B. W., &Brager, G. S. (2004). A better way to predict comfort. ASHRAE Journal, 46(8), 20.

Files

Share

How to cite

Statistics