Novel Machine-Learning-Based Stall Delay Correction Model for Improving Blade Element Momentum Analysis in Wind Turbine Performance Prediction

IF 1.3 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY Wind and Structures Pub Date : 2022-10-06 DOI:10.3390/wind2040034
Ijaz Fazil Syed Ahmed Kabir, M. Gajendran, E. Ng, A. Mehdizadeh, A. Berrouk
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引用次数: 4

Abstract

Wind turbine blades experience excessive load due to inaccuracies in the prediction of aerodynamic loads by conventional methods during design, leading to structural failure. The blade element momentum (BEM) method is possibly the oldest and best-known design tool for evaluating the aerodynamic performance of wind turbine blades due to its simplicity and short processing time. As the turbine rotates, the aerofoil lift coefficient enhances, notably in the rotor’s inboard section, relative to the value predicted by 2D experimentation or computational fluid dynamics (CFD) for the identical angle of attack; this is induced by centrifugal pumping action and the Coriolis force, thus delaying the occurrence of stall. This rotational effect is regarded as having a significant influence on the rotor blade’s aerodynamic performance, which the BEM method does not capture, as it depends on 2D aerofoil characteristics. Correction models derived from the traditional hard computing mathematical method are used in the BEM predictions to take into account stall delay. Unfortunately, it has been observed from the earlier literature that these models either utterly fail or inaccurately predict the enhancement in lift coefficient due to stall delay. Consequently, this paper proposes a novel stall delay correction model based on the soft computing technique known as symbolic regression for high-level precise aerodynamic performance prediction by the BEM process. In complement to the correction model for the lift coefficient, a preliminary correction model for the drag coefficient is also suggested. The model is engendered from the disparity in 3D and 2D aerofoil coefficients over the blade length for different wind speeds for the NREL Phase VI turbine. The proposed model’s accuracy is evaluated by validating the 3D aerofoil coefficients computed from the experimental results of a second wind turbine known as the MEXICO rotor.
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基于机器学习的失速延迟修正模型改进风力机性能预测中叶片单元动量分析
风力发电机叶片在设计过程中,由于传统的气动载荷预测方法不准确,导致叶片承受过大的载荷,导致结构失效。叶片单元动量法(BEM)由于其简单和处理时间短,可能是最古老和最著名的评估风力涡轮机叶片气动性能的设计工具。当涡轮旋转时,相对于相同迎角的二维实验或计算流体力学(CFD)预测值,翼型升力系数增加,特别是在转子内侧截面;这是由离心泵送作用和科里奥利力引起的,从而延缓了失速的发生。这种旋转效应被认为对动叶的气动性能有重大影响,而边界元法没有捕捉到这一点,因为它取决于二维翼型的特性。在边界元模型的预测中,采用了传统的硬计算数学方法推导出的校正模型来考虑失速延迟。不幸的是,从早期文献中观察到,这些模型要么完全失败,要么不准确地预测由于失速延迟而导致的升力系数的增加。因此,本文提出了一种基于符号回归软计算技术的失速延迟修正模型,用于边界元过程的高精度气动性能预测。在升力系数修正模型的基础上,提出了阻力系数的初步修正模型。该模型是由不同风速下NREL第六阶段涡轮机的三维和二维翼型系数在叶片长度上的差异产生的。该模型的精度是通过验证三维翼型系数计算从第二个风力涡轮机被称为墨西哥转子的实验结果进行评估。
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来源期刊
Wind and Structures
Wind and Structures 工程技术-工程:土木
CiteScore
2.70
自引率
18.80%
发文量
0
审稿时长
>12 weeks
期刊介绍: The WIND AND STRUCTURES, An International Journal, aims at: - Major publication channel for research in the general area of wind and structural engineering, - Wider distribution at more affordable subscription rates; - Faster reviewing and publication for manuscripts submitted. The main theme of the Journal is the wind effects on structures. Areas covered by the journal include: Wind loads and structural response, Bluff-body aerodynamics, Computational method, Wind tunnel modeling, Local wind environment, Codes and regulations, Wind effects on large scale structures.
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