Towards CFD-based optimization of urban wind conditions

Comparison of Genetic algorithm, Particle Swarm Optimization, and a hybrid algorithm

Journal article (2022)

Authors

Z. Kaseb Shahid Beheshti University
Morteza Rahbar Iran University of Science and Technology
Affiliation
External organisation
More Info
expand_more

Published Date

2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Affiliation

External organisation

Abstract

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.