Performance of a Wind Turbine Blade in Sandstorms Using a CFD-BEM Based Neural Network

Iham Zidane*, Greg Swadener, Xianghong Ma, Mohamed F. Shehadeh, Mahmoud Salem, Khalid M. Saqr

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In arid regions, such as the North African desert, sandstorms impose considerable restrictions on horizontal axis wind turbines (HAWTs), which have not been thoroughly investigated. This paper examines the effects of debris flow on the power generation of the HAWT. Computational Fluid Dynamics (CFD) models were established and validated to provide novel insight into the effects of debris on the aerodynamic characteristics of NACA 63415. To account for the change in the chord length and Reynolds number along the span of the blade and the 3D flow patterns, the power curves for a wind turbine were obtained using the Blade Element Momentum (BEM) method. We present a novel coupled application of the neural network, CFD, and BEM to investigate the erosion rates of the blade due to different sandstorm conditions. The proposed model can be scaled and developed to assist in monitoring and prediction of HAWT blade conditions. This work shows that HAWT performance can be significantly diminished due to the aerodynamic losses under sandstorm conditions. The power generated under debris flow conditions can decrease from 10 to 30% compared to clean conditions.

Original languageEnglish
Article number053310
JournalJournal of Renewable and Sustainable Energy
Issue number5
Publication statusPublished - 27 Oct 2020

Bibliographical note

Copyright © 2020 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Journal of Renewable and Sustainable Energy 12, 053310 (2020) and may be found at


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