基于GSA的风电叶片主梁初始损伤的敏感因素分析
SENSITIVITY FACTOR ANALYSIS ON INITIAL DAMAGE OF WIND TURBINE BLADE SPAR BASED ON GSA
孙宁 1周勃 2郑皓成 1李晖3
作者信息
- 1. 沈阳工业大学机械工程学院,沈阳 110870
- 2. 沈阳工业大学机械工程学院,沈阳 110870;沈阳工业大学建筑与土木工程学院,沈阳 110870
- 3. 东北大学机械工程与自动化学院,沈阳 110819
- 折叠
摘要
针对风电叶片主梁褶皱缺陷演化为初始损伤模式的不确定性问题,采用高斯概率分布函数确定7个因素的分布信息,根据褶皱缺陷在拉伸加载时的应力数据,改进Sobol'算法采用拉丁超立方法获取样本点训练BP神经网络,采用Kullback-Leibler散度计算最大和非最大损伤处的应变余能密度相对熵,分别作为基体开裂和纤维断裂的敏感度响应指标.结果表明,基体损伤和纤维断裂的高敏感因素均为载荷幅值、纤维含量、基纤模量比和褶皱高宽比,但基纤模量比与褶皱高宽比的排序有所不同,说明叶片主梁材料性能和缺陷特征形貌对初始损伤模式的作用程度不同.最后建立含褶皱的GFPR层合板有限元模型,模拟结果验证了全局敏感度分析方法的准确性.
Abstract
Aiming at the issue of uncertainty in the initial damage evolution of wind turbine blade spars with wrinkle defects,Gaussian probability distribution function is used to determine the distribution information of seven factors.The stress data of wrinkle defects under tensile loading is used to improve Sobol's algorithm through Latin hypercube sampling,which enables the acquisition of sample points and the training of a BP neural network.The relative entropy of strain residual energy density at maximum and non-maximum damage sites is calculated using Kullback-Leibler divergence.These values serve as sensitivity response indexes for matrix cracking and fiber fracture,respectively.The results show that loading amplitude is the most sensitive factor for both matrix damage and fiber fracture,followed by fiber content,matrix-to-fiber modulus ratio,and wrinkle height-to-width ratio.However,the order of the matrix-to-fiber modulus ratio and wrinkle height-to-width ratio is different.The related result shows that the blade spar's material properties and the defect feature's appearance have different effects on the initial damage mode.Finally,the finite element model of GFPR laminate with wrinkle is established.The simulation results verify the accuracy of the global sensitivity analysis method.
关键词
风力机叶片/疲劳损伤/敏感度分析/KL散度/神经网络Key words
wind turbine blades/fatigue damage/sensitivity analysis/KL divergence/neural network引用本文复制引用
基金项目
国家自然科学基金(52175105)
国家自然科学基金(52175079)
辽宁省教育厅高等学校基本科研项目(LJKMZ20220486)
出版年
2024
NETL
NSTL
万方数据