CFD Simulation of the Wind Field in Jinjiang City Using a Building Data Generalization Method
AbstractThe urban wind environment is an important element of urban microclimates and plays an important role in the quality of the urban environment. The computational fluid dynamics (CFD) simulation method is an important means for urban wind field research. However, CFD simulation has high requirements for computer hardware and software. In this paper, based on geographic information system (GIS) technology, a new building data generalization method was developed to solve the problems of a huge amount of data and calculations in urban-scale CFD wind field simulations. Using Fluent software and high-precision urban building geographic information data with elevation attributes, the method was applied to Jinjiang City, Fujian Province, China. A CFD simulation of the wind field of Jinjiang City was implemented, and detailed, intuitive wind field information was obtained, which were compared with the measured data. The results show that the building data generalization method could effectively improve the efficiency of the city's overall wind field CFD simulation. The simulated wind speed was significantly correlated with the measured data, but it was overestimated. The simulated wind direction was consistent with the measured data of most stations. The simulation results were reasonable and could provide reference for application and subsequent research. View Full-Text
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Li, M.; Qiu, X.; Shen, J.; Xu, J.; Feng, B.; He, Y.; Shi, G.; Zhu, X. CFD Simulation of the Wind Field in Jinjiang City Using a Building Data Generalization Method. Atmosphere 2019, 10, 326.
Li M, Qiu X, Shen J, Xu J, Feng B, He Y, Shi G, Zhu X. CFD Simulation of the Wind Field in Jinjiang City Using a Building Data Generalization Method. Atmosphere. 2019; 10(6):326.Chicago/Turabian Style
Li, Mengxi; Qiu, Xinfa; Shen, Juanjun; Xu, Jinqin; Feng, Bo; He, Yongjian; Shi, Guoping; Zhu, Xiaochen. 2019. "CFD Simulation of the Wind Field in Jinjiang City Using a Building Data Generalization Method." Atmosphere 10, no. 6: 326.
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