基于人工神经网络和CFD方法的风机翼型性能预测

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
M. Moshtaghzadeh, M. Aligoodarz
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引用次数: 1

摘要

为了从风力涡轮机中获得最高的能量水平,对其性能的预测是必不可少的。本文研究了风电场中常用的不同翼型的气动性能。本文采用基于有限体积方法的计算流体力学方法,考虑带过渡型的稳态流动。采用一种先进的人工神经网络来减少计算时间。结果表明,该算法能准确预测翼型效率,训练集误差小于2%,测试集误差小于3%。计算结果与实验结果吻合;这种分析通过选择叶片的翼型准确地预测了风力涡轮机的性能。该研究为在风电场中更广泛地使用这些翼型提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Wind Turbine Airfoil Performance Using Artificial Neural Network and CFD Approaches
To achieve the highest energy level from a wind turbine, the prediction of its performance is essential. This study investigates the aerodynamic performance of different airfoils, which are frequently used in wind farms. The computational fluid dynamics method based on the finite-volume approach is utilized, and a steady-state flow with the transition regime is considered in this study. A developed artificial neural network is used to reduce the computational time. The results indicates that the trained algorithm could accurately predict the airfoil efficiency with less than 2% error on the training set and fewer than 3% error on the test set. The results agree with the experimental results; this analysis accurately predicts wind turbine performance by selecting the blade’s airfoil. This study provides a reference for a broader range of using these airfoils in wind farms.
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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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