Towards CFD-based optimization of urban wind conditions: Comparison of Genetic algorithm, Particle Swarm Optimization, and a hybrid algorithm

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.