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.

中文翻译：

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使用基于 CFD-BEM 的神经网络的风轮机叶片在沙尘暴中的性能

在干旱地区，如北非沙漠，沙尘暴对水平轴风力涡轮机 (HAWT) 施加了相当大的限制，但尚未得到彻底调查。本文研究了泥石流对HAWT发电的影响。建立并验证了计算流体动力学 (CFD) 模型，以提供有关碎片对 NACA 63415 空气动力学特性影响的新见解。 考虑沿叶片跨度和 3D 流动的弦长和雷诺数的变化模式，风力涡轮机的功率曲线是使用叶片元素动量 (BEM) 方法获得的。我们提出了神经网络、CFD 和 BEM 的新型耦合应用，以研究叶片在不同沙尘暴条件下的侵蚀率。所提出的模型可以进行扩展和开发，以帮助监测和预测 HAWT 叶片状况。这项工作表明，由于沙尘暴条件下的空气动力损失，HAWT 性能会显着降低。与清洁条件相比，泥石流条件下产生的功率可降低 10% 至 30%。