The roof as an environmental assessment tool The computational equation of thermal comfort based on Fanger scale | ||
| JES. Journal of Engineering Sciences | ||
| Article 7, Volume 51, Issue 6, November and December 2023, Pages 501-521 PDF (2.74 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.21608/jesaun.2023.221057.1242 | ||
| Author | ||
| Mohammed El-essawy* | ||
| Associate professor, Dept. of Arch. Eng., Fayoum University, Fayoum, Egypt. | ||
| Abstract | ||
| Purpose –Is to identify the effect of the design variables of roofs on the thermal comfort field according to the Fanger Scale, in order to determine the computational mathematical design relationship of the effect of the design variable on the amount of change in the ratio of the thermal comfort field according to the Fanger Scale. Design/methodology/approach – A simulation study model was designed according to administrative buildings standards. Then, five roof variables were determined with its values to be simulated using design builder software as an approach to study its effect on thermal comfort according to Fanger scale and its computational mathematical relationship. Findings - The results indicated that the design of pergolas is one of the biggest elements that have a positive impact on thermal comfort, followed by the roof cantilever, the plantings, the inclination of the roof, and then the thermal insulation. Based on the simulation model and the mathematical base of Simple Linear Regression, computational equations were found out for the most influential design variables based on the computational form “Change in PPD Fanger = Constant 1 + Constant 2 x Roof Variable”, as the research determined the values of the constants in typical equation for the top three design variables. Originality/value – It is a base for calculating the roof variables and its impact on thermal comfort as a roof base design standard. | ||
| Keywords | ||
| Fanger scale; thermal comfort; roof environmental tool; environmental roofs variables; roof environmental impact | ||
| References | ||
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[1] CEIC. (n.d.). Global Academic Data. https://www.ceicdata.com/en/indicator/egypt/oil-consumption. Retrieved 5th Jan. 2023.
[2] U.S. Energy information administration EIA. (2018). https://www.eia.gov/energyexplained/oil-and-petroleum-products/imports-and-exports.php. Retrieved 16th Nov. 2022.
[3] ASHREA. (n.d.). American Society of Heating, Refrigerating and Air Conditioning Engineers. https://www.ashrae.org/. Retrieved 12th Dec. 2022.
[4] J. V. Hoof. (2008). Forty years of Fanger's model of thermal comfort: comfort for all. https://onlinelibrary.wiley.com/doi/10.1111/j.1600-0668.2007.00516.x. Retrieved 10th Jan. 2023.
[5] P.O. Fanger, (2001). Human requirements in future air-conditioned environments, International Journal of Refrigeration.
[6] H. Rainer. (2002). Office Buildings: A Design Manual Hardcover. Birkhäuser Architecture. Switzerland.
[7] ScienceDirect. (2001). Energy & buildings, Special Issue on Thermal Comfort Standards. https://www.sciencedirect.com/journal/energy-and-buildings/vol/34/issue/6. Retrieved 16th Nov. 2022.
[8] S. Joe. (2021). Statistical learning with math and python, 100 excercises for building logic. Springer.
[9] Y. Xin. G. Xiao. (2009). Linear regression analysis, theory, and computing. World Scientific Publishing Co Pte Ltd. Singapore
[10] Y. Peter. M. Paula. (2002). Correlation, parametric and nonparametric measures. SAGE Publications, Inc. New York. USA
[11] P. Karl. (2012). On Further Methods, of Determining Correlation. Forgotten Books, London. | ||
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