Wind and Structures

  • 제31권4호
  • /
  • Pages.363-371
  • /
  • 2020
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  • 1226-6116(pISSN)
  • /
  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Quantitative assessment of offshore wind speed variability using fractal analysis

  • Shu, Z.R. (Department of Civil Engineering, University of Birmingham) ;
  • Chan, P.W. (Hong Kong Observatory) ;
  • Li, Q.S. (Department of Architecture and Civil Engineering, City University of Hong Kong) ;
  • He, Y.C. (Joint Research Center for Engineering Structure Disaster Prevention and Control, Guangzhou University) ;
  • Yan, B.W. (Chongqing University, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, School of Civil Engineering)
  • 투고 : 2019.07.26
  • 심사 : 2020.08.05
  • 발행 : 2020.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Proper understanding of offshore wind speed variability is of essential importance in practice, which provides useful information to a wide range of coastal and marine activities. In this paper, long-term wind speed data recorded at various offshore stations are analyzed in the framework of fractal dimension analysis. Fractal analysis is a well-established data analysis tool, which is particularly suitable to determine the complexity in time series from a quantitative point of view. The fractal dimension is estimated using the conventional box-counting method. The results suggest that the wind speed data are generally fractals, which are likely to exhibit a persistent nature. The mean fractal dimension varies from 1.31 at an offshore weather station to 1.43 at an urban station, which is mainly associated with surface roughness condition. Monthly variability of fractal dimension at offshore stations is well-defined, which often possess larger values during hotter months and lower values during winter. This is partly attributed to the effect of thermal instability. In addition, with an increase in measurement interval, the mean and minimum fractal dimension decrease, whereas the maximum and coefficient of variation increase in parallel.

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