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arXiv 2607.04495physics.flu-dynphysics.geo-ph
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U3DWind:用于城市空中交通的低空风场数据集及基准

U3DWind: A Low Altitude Wind Field Dataset and Benchmark for Urban Air Mobility

The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州));University of Manchester(曼彻斯特大学);Chalmers University of Technology(查尔姆斯理工大学);Tongji University(同济大学);The Hong Kong Polytechnic University(香港理工大学);The Hong Kong University of Science and Technology(香港科技大学)

Shixiong Zhou, Huanxia Wei, Chao Xia, Yingying Xing, Changmin Jiang, Hai Yang, Shuai Jia

AI总结:

研究针对城市空中交通低空风害评估数据资源不足问题,用GPU加速框架生成U3DWind数据集,涵盖中国五座大城市,定义五项基线任务,为低空交通风影响评估及城市空域管理等提供基准。

摘要:

城市空中交通(UAM)需要可靠评估低空风害。虽现有方法推动了风感知分析,但面向数据驱动、城市规模UAM规划的公共资源有限。为此引入U3DWind,这是用GPU加速的格子玻尔兹曼方法-大涡模拟(LBM-LES)框架生成的建筑解析低空风场数据集,涵盖中国五座大城市,含720次模拟,还定义了五项基线任务,可实现系统评估并提供开放基准。

英文摘要:

Urban Air Mobility (UAM) requires reliable assessment of low-altitude wind hazards, because winds, gusts, and building-induced turbulence have been recognized as critical factors affecting vehicle stability, route feasibility, vertiport siting, and airspace management. While wind-tunnel experiments, computational fluid dynamics (CFD), multiscale downscaling, reduced-order models, and UAV planning datasets have advanced wind-aware analysis, public resources for data-driven, city-scale UAM planning remain limited in geographic coverage, scenario diversity, vertical extent, building realism, and task-oriented benchmarking. To address this gap, we introduce U3DWind, a building-resolved low-altitude wind-field dataset generated using our GPU-accelerated Lattice Boltzmann Method--Large-Eddy Simulation (LBM-LES) framework for rapid urban flow simulation. U3DWind covers five megacities in China: Beijing, Shanghai, Guangzhou, Shenzhen, and Hong Kong. It contains 720 simulations, with 16 inflow directions, three reference wind speeds, and three seasonal atmospheric scenarios (annual, summer, and winter) for each city. At a 10 m grid resolution, the dataset provides three-dimensional three-component (3D3C) velocity, turbulent kinetic energy (TKE), flow density, and fluid--solid masks. To support operationally relevant evaluation, we further define five baseline tasks: wind-field prediction, sparse-sensor wind-field reconstruction, site wind-exposure ranking, airworthiness wind-compliance risk scoring, and noise propagation modeling. As a multi-city, building-resolved 3D urban wind-field dataset, U3DWind enables systematic evaluation of wind-induced impacts in low-altitude traffic scenarios and provides an open benchmark for urban airspace management and data-driven high-fidelity urban flow simulation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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