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大型风电长叶片气动外形的高效低载三维设计

High efficiency and low load three-dimensional design of the aerodynamic shape of long blades in large wind turbine blade
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摘要 海上风力机等大型风电设备叶片较长,所承受气动载荷较大,易产生变形,影响气动性能和运行稳定性。针对这一问题,以美国NREL实验室的5 MW大型风电叶片为例,对其进行以各截面翼型形线、安装角及额定功率下桨距角为设计变量的高效低载三维优化。优化基于动量叶素理论和多岛遗传算法,以叶根弯矩最低和风能利用率最大为优化目标,并将优化叶片与原始叶片于变桨、变风况下的气动性能进行对比。结果表明:在设计工况下,相较于原始叶片,优化叶片在保证高气动效率的同时叶根弯矩降低了5%;变风况条件下,变桨前优化叶片的风能利用率平均提升了1%,叶根弯矩平均降低了5.8%,变桨后优化叶片的叶根弯矩平均降低了4%。 Large-scale wind power equipment such as offshore wind turbines have long blades,which bear a large aerodynamic load and are prone to deformation,which affects aerodynamic performance and operational stability.In response to this problem,the 5 MW large-scale wind turbine blade of the NREL laboratory in the United States of America was taken as an example.A three-dimensional optimization of high efficiency and low load was carried out with the airfoil profile of each section,the installation angle and the pitch angle at rated power as the design variables.The maximum wind energy utilization rate and the minimum blade root bending moment were taken as the optimization goals.The optimization design based on the blade element momentum theory and the multi-island genetic algorithm,and the aerodynamic performance of the optimized blade under variable pitch and variable wind conditions was compared with the original blade.The optimization results showed that,compared with the original blade,under the design conditions,the optimized blade reduced the root bending moment by 5%while ensuring high aerodynamic efficiency.Under variable wind conditions,the wind energy utilization rate of the optimized blades before the pitch change was increased by an average of 1%,and the root bending moment of the blade was reduced by an average of 5.8%.After the pitching,the blade root bending moment of the optimized blade was reduced by an average of 4%.
作者 姚叶宸 黄典贵 YAO Yechen;HUANG Diangui(School of Energy and Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处 《上海理工大学学报》 CAS CSCD 北大核心 2023年第6期584-590,共7页 Journal of University of Shanghai For Science and Technology
基金 国家自然科学基金资助项目(52036005)。
关键词 风电叶片 翼型优化 气动效率 气动载荷 遗传算法 wind turbine blade airfoil optimization aerodynamic efficiency aerodynamic load genetic algorithm
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