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<p>The accuracy and efficiency of urban microscale wind field prediction are critical for applications in wind environment assessment and urban planning. However, conventional numerical weather prediction, which relies on data from mesoscale simulations, is often hindered by insufficient spatial and temporal resolution. To address this limitation, this study introduces a novel framework for rapid wind field prediction by establishing an urban microscale wind field database using block-based computational fluid dynamics (CFD). The framework involves partitioning a GIS-based building and terrain model into 1 km × 1 km blocks, with the CFD simulation results for each block composing a precomputed database. A comprehensive analysis was conducted to determine the optimal transition zone length required to account for the aerodynamic effects of surrounding buildings. The minimal discrepancies observed in wind profiles at the interfaces of adjacent blocks validate the feasibility of this independent, block-wise simulation approach. Furthermore, the framework’s predictive capability was demonstrated by comparing its statistically-derived mean wind speeds against a year of field data from multiple meteorological stations. Finally, the precomputed CFD database, containing wind speed ratios and wind pressure coefficients, has been integrated into a WebGIS platform, enabling practical applications.</p>

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A fast prediction framework for urban microscale wind environment based on precomputed CFD database

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Peisheng Zhao,
  • Chao Li,
  • Chao Yang,
  • Zhichen Han,
  • Lingwei Chen,
  • Gang Hu,
  • Lixiao Li,
  • Xiaolu Wang

摘要

The accuracy and efficiency of urban microscale wind field prediction are critical for applications in wind environment assessment and urban planning. However, conventional numerical weather prediction, which relies on data from mesoscale simulations, is often hindered by insufficient spatial and temporal resolution. To address this limitation, this study introduces a novel framework for rapid wind field prediction by establishing an urban microscale wind field database using block-based computational fluid dynamics (CFD). The framework involves partitioning a GIS-based building and terrain model into 1 km × 1 km blocks, with the CFD simulation results for each block composing a precomputed database. A comprehensive analysis was conducted to determine the optimal transition zone length required to account for the aerodynamic effects of surrounding buildings. The minimal discrepancies observed in wind profiles at the interfaces of adjacent blocks validate the feasibility of this independent, block-wise simulation approach. Furthermore, the framework’s predictive capability was demonstrated by comparing its statistically-derived mean wind speeds against a year of field data from multiple meteorological stations. Finally, the precomputed CFD database, containing wind speed ratios and wind pressure coefficients, has been integrated into a WebGIS platform, enabling practical applications.