JOURNAL ARTICLE

Experimental study on the near wake recovery of horizontal axis wind turbines with blades of different trailing edge thickness.

  • Published In: Journal of Renewable & Sustainable Energy, 2025, v. 17, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Niu, Ruiqi; Dong, Xueqing; Zhang, Liru; Gao, Zhiying; Han, Yuxia; Deng, Peitian; Wang, Jianwen; Lu, Haojie 3 of 3

Abstract

This article investigates the effects of varying trailing edge thickness on the wake recovery and power performance of small horizontal axis wind turbine blades using the NACA4415 airfoil. Through wind tunnel experiments employing a constant temperature hot wire anemometer, blades with trailing edge thicknesses from 0% to 4% chord length (c) were tested, revealing that a 3%c thickened trailing edge optimizes wake expansion, reduces turbulence intensity by up to 3%, and decreases velocity deficits in the near wake region (x/R = 2–4 and x/R = 4). Power measurements under different tip speed ratios (TSR) showed that the 3%c thickened trailing edge blade increased power output by up to 7.01% at TSR = 5 compared to the original sharp trailing edge blade. The study concludes that symmetric thickening of the trailing edge can enhance wake recovery and power generation efficiency in small wind turbines, with implications for wind farm layout and capacity planning, while noting the need for further field validation for full-scale applications.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2025/01, Vol. 17, Issue 1, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2025
  • ISSN:1941-7012
  • DOI:10.1063/5.0237330
  • Accession Number:183417685
  • 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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