id |
ascaad2021_153 |
authors |
Valitabar, Mahdi; Mohammadjavad Mahdavinejad, Henry Skates, Peiman Pilechiha |
year |
2021 |
title |
Data-Driven Design of Adaptive Façades: View, Glare, Daylighting and Energy Efficiency |
source |
Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 699-711 |
summary |
This paper attempts to increase occupants’ view to outside through Adaptive facades by employing a parametric design method. Reaching a balance between occupants’ requirements and the building energy criteria is the main objective of this research. To this end, a multi-objective optimization is done to generate some optimum models. The method, indeed, was used to optimize the shading size of a dynamic vertical shading system utilized on the south façade of a single office room located in Tehran. The shading system was defined by five parameters and a combination of Cut-off and a glare protection strategy is used to control dynamic shadings. The size-optimisation objectives are minimum DGP, cooling load and maximum illuminance, which were analysed by Ladybug Tools. Then, Octopus was used for multi-objective optimistion to find new optimum forms. Along with the openness factor, a new index is presented to evaluate the outside view in multiple louver shading systems, named “Openness Curvature Factor” (OCF). Thanks to this method, the size and shape of some optimum generated models were modified to increase the amount of OCF. Following that the Honeybee Plus is used to simulate the visual performance of modified models which shows a significant improvement. The modified models could provide about 4 times more outside view than generated models whilst keeping the DGP value in imperceptible range. Geometric or even complex non-geometric shading forms can be studied by this method to find optimum adaptive facades. |
series |
ASCAAD |
email |
mahdi.valitabar@gmail.com |
full text |
file.pdf (951,903 bytes) |
references |
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last changed |
2021/08/09 13:14 |
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