| id |
caadria2023_192 |
| authors |
Lin, Zhichao, Yin, Shi, Liao, Wei and Xiao, Yiqiang |
| year |
2023 |
| title |
Genetic Algorithm-Based Building Geometric Opening Configurations Optimization for Enhancing Ventilation Performance in the High-Density Urban District |
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 251–260 |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.251
|
| summary |
The quality of the outdoor environment relates to urban ventilation performance. Poor wind conditions in high-density urban districts may lead to severe air pollution and deteriorate outdoor thermal comfort. The increase of openings in building geometry is one of the effective passive design strategies for enhancing the porosity of urban morphology and benefitting urban ventilation. However, the outdoor wind environment correlates with the opening configurations of building geometry complicatedly. For seeking the optimal opening configurations, a decision support tool is urgently needed. Our study proposes a genetic algorithm-based optimization workflow for the opening configurations of building geometry design by integrating Computational Fluid Dynamics simulation and parametric design. A residential block in Shenzhen, China is chosen as an example to show this workflow. The results demonstrate that when the porosity is 15%, the pedestrian-level mean wind speed, the wind speed dispersion, and the pressure difference between the front and rear of the downstream building can be optimized by 20.00%, 19.35%, and 183.33% on maximum. When the porosity is increased to 30%, these values are 42.22%, 16.13%, and 483.33%. The resultant opening distribution probability maps can support building design at an early stage to achieve a comfortable urban environment. |
| keywords |
Urban Ventilation, Building Openings, Building Porosity, Genetic Algorithm Optimization, Computational Fluid Dynamics |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (4,905,877 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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