Wind and Structures

Techno-Press (테크노프레스)
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Probabilistic analysis of gust factors and turbulence intensities of measured tropical cyclones

  • Tianyou Tao (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University) ;
  • Zao Jin (School of Civil Engineering, Southeast University) ;
  • Hao Wang (Key Laboratory of C&PC Structures of Ministry of Education, Southeast University)
  • Received : 2023.08.15
  • Accepted : 2024.01.23
  • Published : 2024.04.25
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Abstract

The gust factor and turbulence intensity are two crucial parameters that characterize the properties of turbulence. In tropical cyclones (TCs), these parameters exhibit significant variability, yet there is a lack of established formulas to account for their probabilistic characteristics with consideration of their inherent connection. On this condition, a probabilistic analysis of gust factors and turbulence intensities of TCs is conducted based on fourteen sets of wind data collected at the Sutong Cable-stayed Bridge site. Initially, the turbulence intensities and gust factors of recorded data are computed, followed by an analysis of their probability densities across different ranges categorized by mean wind speed. The Gaussian, lognormal, and generalized extreme value (GEV) distributions are employed to fit the measured probability densities, with subsequent evaluation of their effectiveness. The Gumbel distribution, which is a specific instance of the GEV distribution, has been identified as an optimal choice for probabilistic characterizations of turbulence intensity and gust factor in TCs. The corresponding empirical models are then established through curve fitting. By utilizing the Gumbel distribution as a template, the nexus between the probability density functions of turbulence intensity and gust factor is built, leading to the development of a generalized probabilistic model that statistically describe turbulence intensity and gust factor in TCs. Finally, these empirical models are validated using measured data and compared with suggestions recommended by specifications.

Keywords

Acknowledgement

The authors would like to acknowledge the financial supports from the National Natural Science Foundation of China (Grant No. 52278486, 52338011), the Young Elite Scientists Sponsorship Program by CAST (Grant No. YESS20220647), and the Zhishan Young Scholar Program of Southeast University (Grant No. 2242024RCB0007).

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