1. Automated Simulation Framework for Urban Wind Environments Based on Aerial Point Clouds and Deep Learning.
- Author
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Sun, Chujin, Zhang, Fan, Zhao, Pengju, Zhao, Xinyi, Huang, Yuli, and Lu, Xinzheng
- Subjects
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POINT cloud , *COMPUTATIONAL fluid dynamics , *GEOGRAPHIC information systems , *AERODYNAMICS of buildings , *URBAN planning , *DEEP learning ,URBAN ecology (Sociology) - Abstract
Computational fluid dynamics (CFD) simulation is a core component of wind engineering assessment for urban planning and architecture. CFD simulations require clean and low-complexity models. Existing modeling methods rely on static data from geographic information systems along with manual efforts. They are extraordinarily time-consuming and have difficulties accurately incorporating the up-to-date information of a target area into the flow model. This paper proposes an automated simulation framework with superior modeling efficiency and accuracy. The framework adopts aerial point clouds and an integrated two-dimensional and three-dimensional (3D) deep learning technique, with four operational modules: data acquisition and preprocessing, point cloud segmentation based on deep learning, geometric 3D reconstruction, and CFD simulation. The advantages of the framework are demonstrated through a case study of a local area in Shenzhen, China. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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