针对大尺寸叶片运行状态下受旋转软化效应和动力刚化效应耦合作用从而导致其模态特性改变的现象,提出一种基于摄动模态分析的旋转叶片振动分析方法。建立0.89 m和61.5 m两种型号的叶片模型,通过数值模拟分析叶片在0~50 r/min转速范围内这两种效应对叶片频率的影响。基于计算的频率差值Δf的变化规律,研究叶片3种主要振型与这两种效应之间的关联关系。最后,为验证数值模拟的准确性,通过锤击法进行实验模态分析并与数值模拟结果进行比较。实验结果表明,其一、二阶模态频率误差均在1%以内,计算结果较为准确。
Abstract
In view of the phenomenon that the blade is coupled by rotating softening effect and dynamic stiffening effect, which leads to the change of its modal characteristics under the operating state of large-scale blade, a vibration analysis method of rotating blade based on perturbation modal analysis is proposed. In this paper, two blade models of 0.89 m and 61.5 m are established. Through numerical simulation, the influence of two effects on blade frequency at 0-50 r/min speed is analyzed. Based on the variation rule of calculated frequency difference Δf, the correlation between the three main modes of blade and the two effects is studied. Finally, in order to verify the accuracy of numerical simulation, experimental modal analysis and numerical simulation results are compared by hammering method. The experimental results show that the errors of first and second order mode frequencies are within 1%, and the calculation results in this paper are more accurate.
关键词
有限元法 /
风力机叶片 /
模态分析 /
旋转软化效应 /
动力刚化效应
Key words
finite element method /
wind turbine blades /
modal analysis /
rotation softening effect /
dynamic rigidity effect
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参考文献
[1] 郭迎福, 全伟铭, 王文韫, 等. 基于三维振动信息融合的卷积神经网络风力机叶片裂纹诊断方法[J]. 光学学报, 2020, 40(22): 140-148.
GUO Y F, QUAN W M, WANG W Y, et al.Crack diagnosis method of wind turbine blade based on convolution neural network with 3D vibration information fusion[J]. Acta optica sinica, 2020, 40(22): 140-148.
[2] 王文韫. 基于组网摄影的风力机叶片振动测量与模态辨识方法研究[D]. 湘潭: 湖南科技大学, 2019.
WANG W Y.Research on vibration measurement and modal identification of wind turbine blade based on network photography[D]. Xiangtan: Hunan University of Science and Technology, 2019.
[3] ZUO Y J, MONTESANO J, SINGH C V.Assessing progressive failure in long wind turbine blades under quasi-static and cyclic loads[J]. Renewable energy, 2018, 119: 754-766.
[4] 廖高华, 乌建中. 风电叶片双向电动往复式疲劳加载系统振动特性分析[J]. 太阳能学报, 2021, 42(4): 380-385.
LIAO G H, WU J Z.VIbration characteristics of bidirectional electric reciprocating loading system of wind turbine blade[J]. Acta energiae solaris sinica, 2021, 42(4): 380-385.
[5] CHEN Y C, ESCALERA MENDOZA A S, GRIFFITH D T. Experimental and numerical study of high-order complex curvature mode shape and mode coupling on a three-bladed wind turbine assembly[J]. Mechanical systems and signal processing, 2021, 160: 107873.
[6] 黄俊东, 夏鸿建, 李德源, 等. 风力机叶片后掠结构模态与颤振特性的影响分析[J]. 太阳能学报, 2021, 42(11): 273-279.
HUANG J D, XIA H J, LI D Y, et al.Analysis of effects of modal and flutter characteristics of wind turbine blade[J]. Acta energiae solaris sinica, 2021, 42(11): 273-279.
[7] 关玉铭, 戈新生. 基于非约束模态刚柔耦合系统的动力刚化分析[J]. 应用力学学报, 2021, 38(5): 1953-1959.
GUAN Y M, GE X S.Dynamic stiffening analysis of rigid-flexible coupling systems based on unconstrained modes[J]. Chinese journal of applied mechanics, 2021, 38(5): 1953-1959.
[8] 张俊苹. 基于振动的旋转风力机叶片损伤识别研究[D]. 哈尔滨: 哈尔滨工业大学, 2012.
ZHANG J P.Study on vibration-based damage detection of rotational wind turbine blades[D]. Harbin: Harbin Institute of Technology, 2012.
[9] 樊俊铃. 基于能量耗散的Q235钢高周疲劳性能评估[J]. 机械工程学报, 2018, 54(6): 1-9.
FAN J L.High cycle fatigue behavior evaluation of Q235 steel based on energy dissipation[J]. Journal of mechanical engineering, 2018, 54(6): 1-9.
[10] JONKMAN J M.Dynamics modeling and loads analysis of an offshore floating wind turbine[M]. Colorado: University of Colorado at Boulder, 2007.
[11] 喻涛涛, 张立茹, 王雪丽. 基于流固耦合的风力机叶片变形研究[J]. 可再生能源, 2019, 37(9): 1381-1385.
YU T T, ZHANG L R, WANG X L.Research on the deformation of wind turbine blade using fluid-solid coupling method[J]. Renewable energy resources, 2019, 37(9): 1381-1385.
基金
国家自然科学基金面上项目（51975535）; 湖南省教育厅科学基金优秀青年项目（22B0465）; 中央引导地方科技发展资金项目（2022ZYT012）
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