JOURNAL ARTICLE

Improved tropical cyclone boundary layer model and its application in floating offshore wind turbine structural response analysis.

  • Published In: Journal of Renewable & Sustainable Energy, 2024, v. 16, n. 6. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: He, Lun; An, Liqiang; Zhang, Ruixing; Yang, Xinmeng; Huang, Zenghao 3 of 3

Abstract

The article focuses on the development and validation of an improved tropical cyclone (TC) boundary layer wind field model, enhancing the Kepert model by incorporating a two-dimensional pressure field varying with height and radius, a surface turbulent diffusivity dependent on wind speed, and a sea surface roughness length influenced by waves and spray. Validation against observational data demonstrates that the improved model more accurately simulates vertical wind profiles and near-surface wind speeds, addressing the Kepert model’s tendency to overestimate tangential wind speeds. The improved model is applied to analyze structural responses of a DTU 10MW floating offshore wind turbine (FOWT) under TC eyewall conditions, revealing that the Kepert model overestimates blade tip displacements, tower top displacements, and platform motions (translation and rotation). These findings suggest that the improved model provides more precise wind field inputs for assessing FOWT structural reliability in TC environments, with implications for enhancing offshore wind turbine design and safety.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2024/11, Vol. 16, Issue 6, p1
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2024
  • ISSN:1941-7012
  • DOI:10.1063/5.0229795
  • Accession Number:181974444
  • Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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