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Multi-objective Optimization of Pedestrian Wind Comfort and Natural Ventilation in a Residential Area

  • H.Y. Peng (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • S.F. Dai (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • D. Hu (School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • H.J. Liu (School of Civil and Environmental Engineering, Harbin Institute of Technology)
  • Published : 2022.12.01

Abstract

With the rapid development of urbanization the problems of pedestrian-level wind comfort and natural ventilation of tall buildings are becoming increasingly prominent. The velocity at the pedestrian level ($\overline{MVR}$) and variation of wind pressure coefficients $\overline{{\Delta}C_p}$ between windward and leeward surfaces of tall buildings were investigated systematically through numerical simulations. The examined parameters included building density ρ, height ratio of building αH, width ratio of building αB, and wind direction θ. The linear and quadratic regression analyses of $\overline{MVR}$ and $\overline{{\Delta}C_p}$ were conducted. The quadratic regression had better performance in predicting $\overline{MVR}$ and $\overline{{\Delta}C_p}$ than the linear regression. $\overline{MVR}$ and $\overline{{\Delta}C_p}$ were optimized by the NSGA-II algorithm. The LINMAP and TOPSIS decision-making methods demonstrated better capability than the Shannon's entropy approach. The final optimal design parameters of buildings were ρ = 20%, αH = 4.5, and αB = 1, and the wind direction was θ = 10°. The proposed method could be used for the optimization of pedestrian-level wind comfort and natural ventilation in a residential area.

Keywords

Acknowledgement

This work was supported by National Natural Science Foundation of China (Grant No.: 51978221), Shenzhen Science and Technology Innovation Committee (Grant No.: GXWD20201230155427003-20200824100128002) and Characteristic and Innovation Projects of Universities in Guangdong Province (Grant No.: 2021KTSCX367). The authors would like to express their gratitude for the support.

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