Towards CFD-based optimization of urban wind conditions: Comparison of Genetic algorithm, Particle Swarm Optimization, and a hybrid algorithm
•Genetic algorithm, particle swarm optimization and a hybrid algorithm are compared.•CFD simulation and evolutionary algorithms are coupled to optimize wind conditions.•Wind speed is improved at street scale for a generic urban area.•The use of different evolutionary algorithms can impact wind speed...
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| Published in | Sustainable cities and society Vol. 77; p. 103565 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2210-6707 |
| DOI | 10.1016/j.scs.2021.103565 |
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| Abstract | •Genetic algorithm, particle swarm optimization and a hybrid algorithm are compared.•CFD simulation and evolutionary algorithms are coupled to optimize wind conditions.•Wind speed is improved at street scale for a generic urban area.•The use of different evolutionary algorithms can impact wind speed by up to 425%.•A hybrid algorithm of PSO and GA is superior over PSO and GA.
Urban morphology can significantly impact urban wind conditions. Finding an optimum morphology to improve the wind conditions for a given urban area can be very challenging as it depends on a wide range of parameters. In this perspective, meta-heuristic algorithms can be useful to reach/approximate optimum solutions. While the satisfactory performance of meta-heuristic algorithms has been shown for different complex engineering problems, a detailed evaluation of these algorithms has not yet been performed for urban wind conditions. Therefore, this study aims to systematically evaluate the performance of meta-heuristic algorithms for CFD-based optimization of urban wind conditions at street scale. Three algorithms are considered: (i) Genetic algorithm (GA), (ii) Particle Swarm Optimization (PSO), and (iii) a hybrid algorithm of PSO and GA. The focus is on a compact generic urban area, while the height of the involved buildings is considered as the optimization variable. In total, 714 high-resolution 3D steady Reynolds-averaged Navier-Stokes (RANS) CFD simulations are performed in combination with the standard k-ε turbulence model. The results show that the hybrid algorithm is superior as it can improve the wind conditions by about 425% and 100%, compared with GA and PSO, respectively. |
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| AbstractList | •Genetic algorithm, particle swarm optimization and a hybrid algorithm are compared.•CFD simulation and evolutionary algorithms are coupled to optimize wind conditions.•Wind speed is improved at street scale for a generic urban area.•The use of different evolutionary algorithms can impact wind speed by up to 425%.•A hybrid algorithm of PSO and GA is superior over PSO and GA.
Urban morphology can significantly impact urban wind conditions. Finding an optimum morphology to improve the wind conditions for a given urban area can be very challenging as it depends on a wide range of parameters. In this perspective, meta-heuristic algorithms can be useful to reach/approximate optimum solutions. While the satisfactory performance of meta-heuristic algorithms has been shown for different complex engineering problems, a detailed evaluation of these algorithms has not yet been performed for urban wind conditions. Therefore, this study aims to systematically evaluate the performance of meta-heuristic algorithms for CFD-based optimization of urban wind conditions at street scale. Three algorithms are considered: (i) Genetic algorithm (GA), (ii) Particle Swarm Optimization (PSO), and (iii) a hybrid algorithm of PSO and GA. The focus is on a compact generic urban area, while the height of the involved buildings is considered as the optimization variable. In total, 714 high-resolution 3D steady Reynolds-averaged Navier-Stokes (RANS) CFD simulations are performed in combination with the standard k-ε turbulence model. The results show that the hybrid algorithm is superior as it can improve the wind conditions by about 425% and 100%, compared with GA and PSO, respectively. |
| ArticleNumber | 103565 |
| Author | Kaseb, Z. Rahbar, M. |
| Author_xml | – sequence: 1 givenname: Z. surname: Kaseb fullname: Kaseb, Z. email: zeynab.kaseb@yahoo.com organization: Shahid Beheshti University, Tehran, Iran – sequence: 2 givenname: M. surname: Rahbar fullname: Rahbar, M. email: rahbarm@iust.ac.ir organization: Iran University of Science and Technology, Tehran, Iran |
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| Keywords | Wind comfort Evolutionary algorithms Built environment Simulation-based optimization Direct optimization Urban ventilation |
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