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Seasons Effects of Field Measurement of Near-Ground Wind Characteristics in a Complex Terrain Forested Region

Author

Listed:
  • Hao Yue

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Yagebai Zhao

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Dabo Xin

    (School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China)

  • Gaowa Xu

    (School of Architecture and Civil Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

Abstract

The wind characteristics of the atmospheric boundary layer in forested regions exhibit a significant complexity due to rugged terrain, seasonal climate variability, and seasonal growth of vegetation, which play a key role not only in designing optimal blades to gain better performance but also in assessing the structural response, and there is a paucity of research on such wind fields. Therefore, this paper investigates wind characteristics via on-site wind field measurement. The mean and fluctuating wind characteristics of the forested region in different seasons were analyzed based on the field measurement data. The results show that for the mean wind characteristics, the seasonally fitted exponents play a decisive role in characterizing the mean wind profile, while the season and temperature are the key factors affecting the mean wind direction in forested regions. For fluctuating wind characteristics, the seasonal power-law function can accurately characterize the turbulence intensity profile. Moreover, the ratio of the three turbulence intensity components is significantly affected by temperature and season, and the Von Kármán spectrum has better applicability in the cold and less canopy-disturbed winter than in the other three seasons. The proposed seasonally fitted parameters show better applicability in terms of vertical coherence.

Suggested Citation

  • Hao Yue & Yagebai Zhao & Dabo Xin & Gaowa Xu, 2023. "Seasons Effects of Field Measurement of Near-Ground Wind Characteristics in a Complex Terrain Forested Region," Sustainability, MDPI, vol. 15(14), pages 1-33, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10806-:d:1190664
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    References listed on IDEAS

    as
    1. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    2. Yu-Ling Hsiao, Cody & Sheng, Ni & Fu, Shenze & Wei, Xinyang, 2022. "Evaluation of contagious effects of China's wind power industrial policies," Energy, Elsevier, vol. 238(PB).
    3. Giannaros, Theodore M. & Melas, Dimitrios & Ziomas, Ioannis, 2017. "Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece," Renewable Energy, Elsevier, vol. 102(PA), pages 190-198.
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