JOURNAL ARTICLE

An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer.

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

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Narasimhan, Ghanesh; Gayme, Dennice F.; Meneveau, Charles 3 of 3

Abstract

This article presents the development and validation of a new analytical wake model for wind turbines operating in the atmospheric boundary layer (ABL), specifically addressing conventionally neutral boundary layer (CNBL) and stably stratified boundary layer (SBL) conditions. The model integrates a vortex sheet-based wake framework for yawed turbines with a coupled Ekman-surface layer ABL velocity profile model, incorporating effects of wind veer (height-dependent changes in wind direction due to Coriolis forces) and thermal stratification. Validation against large-eddy simulation (LES) data demonstrates that the model accurately predicts wake velocity deficits, wake deflection, and curled wake structures for both yawed and unyawed turbines across various atmospheric stabilities, improving power loss estimates from wake interactions compared to existing models that neglect veer and stratification. The study highlights the model’s self-consistent, fully predictive capability without reliance on external inputs and suggests future extensions to convective boundary layers and entire wind farm modeling.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2025/05, Vol. 17, Issue 3, p1
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2025
  • ISSN:1941-7012
  • DOI:10.1063/5.0251305
  • Accession Number:185593784
  • 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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