| id |
ecaadesigradi2019_183 |
| authors |
Mughal, Humera and Beirao, Jose |
| year |
2019 |
| title |
A Workflow for the Performance Based Design of Naturally Ventilated Tall Buildings Using a Genetic Algorithm (GA) |
| source |
Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 645-654 |
| doi |
https://doi.org/10.52842/conf.ecaade.2019.2.645
|
| summary |
Optimization of Natural Ventilation process in highrise buildings is one of the most complex and least addressed phenomenon in the field of sustainable architecture. This issue requires urgent consideration to reduce the computation time due to fast growing demand of vertical construction in metropolitan cities. Until recently most highrise buildings have been operated with mechanical systems, causing high energy loads in hot climates and have high carbon footprints. Highrise buildings with natural ventilation and sky gardens can address these problems. This study involves the development of a Genetic Algorithm (GA) addressing the multi objective optimization of natural ventilation in tall buildings incorporated with Sky-Gardens at different levels all connected through a central ventilation shaft. The fitness function for this GA is composed of three scales; temperature reduction due to evapotranspiration of plants of sky-gardens, optimum wind velocity for channelizing air inside the corridors and ventilation shaft, and optimum building configuration. The aim is to find the best solutions for tall buildings constructed in hot climate through the provision of optimized airflow paths suitable for the effectiveness of natural ventilation, within a reasonably short computation time for supporting design processes at early stage. |
| keywords |
Optimization; Natural Ventilation; Tall buildings; Genetic Algorithms |
| series |
eCAADeSIGraDi |
| email |
|
| full text |
 file.pdf (16,509,349 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:59 |
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