叶片疲劳寿命神经网络近似计算模型数值实验

doi:10.12066/j.issn.1007-2861.2016

Abstract

Abstract:

By means of structural approximate analysis based on neural network, approximate calculation model of neural network for fatigue life of wind turbine blade is established. And, the numerical experiments of different parameters for constructing the model of neural network for fatigue life approximation computation of wind turbine are made. The results show the influence of the number of learning samples, the number of hidden layers elements of the neural network, and the learning accuracy on the approximate calculation results of the fatigue life of wind turbine blades. It is helpful for improving the accuracy in calculating the fatigue life value of wind turbine blade based on the approximate model of the wind turbine blade fatigue life neural network. The approximate calculation method of wind turbine blade fatigue life based on neural network provides a new calculation method for calculating the fatigue life performance of wind turbine blades.

Key words: blade fatigue life, structural approximation analysis, neural network model

CLC Number:

TK83

Lei WANG, Jingui LU, Lewei LI. Neural network for fatigue life of wind turbine blade numerical calculation of approximate model[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(6): 870-878.

Figures/Tables 16

Fig.1

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Fig.2

Table 7

Table 8

Table 9

Table 10

Table 11

Table 12

Table 13

Table 14

References 15

[1]	朱杰, 蔡新, 潘盼 , 等. 风力机叶片结构参数敏感性分析及优化设计[J]. 河海大学学报(自然科学版), 2015,43(2):156-162.
[2]	石可重, 赵晓路, 徐建中 . 大型风电机组叶片疲劳试验研究[J]. 太阳能学报, 2011,32(8):1264-1268.
[3]	原红红, 赵振宙, 郑源 , 等. 导叶对涡轮型垂直轴风力机气动性能的影响[J]. 河海大学学报(自然科学版), 2013,41(6):560-564.
[4]	Søensen J D, Frandsen S, Tarp-Johansen N J . Effective turbulence models and fatigue reliability in wind farms[J]. Probabilistic Engineering Mechanics, 2008,23(4):531-538.
[5]	Mc Cartney L N . Energy methods for fatigue damage modelling of laminates[J]. Composites Science & Technology, 2008,68(13):2601-2615.
[6]	Tserpes K I, Papanikos P, Labeas G , et al. Fatigue damage accumulation and residual strength assessment of CFRP laminates[J]. Composite Structures, 2004,63(2):219-230.
[7]	齐红宇, 温卫东 . 现代纤维增强复合材料疲劳理论进展[J]. 材料导报, 2001,15(1):36-38.
[8]	史慧媛, 刘伟庆, 方海 . 复合材料夹层结构的疲劳损伤性能[J]. 南京工业大学学报(自然科学版), 2017,39(5):1-6.
[9]	李德源, 叶枝全, 陈严 , 等. 风力机玻璃钢叶片疲劳寿命分析[J]. 太阳能学报, 2004,25(5):592-598.
[10]	米良, 程珩, 权龙 . 基于泊松随机过程的风力发电机叶片疲劳寿命估算[J]. 机械工程学报, 2016,52(18):134-139
[11]	黄旭进, 曹飞龙 . 矩阵输入的多层前向神经网络学习算法[J]. 中国计量大学学报, 2017(4):485-491.
[12]	Wang L, Hu P, Lu J G , et al. Neural network and PSO-based structural approximation analysis for blade of wind turbine[J]. International Journal of Modelling Identification & Control, 2013,18(1):16-20.
[13]	李文勇, 李泉永 . 利用神经网络进行近似分析的结构优化设计[J]. 广西科学, 2001,8(2):86-89.
[14]	陆金桂, 余俊, 王浩 , 等. 基于人工神经网络的结构近似分析方法的研究[J]. 中国科学: A 辑, 1994,24(6):653-658.
[15]	张云波 . 神经网络近似结构分析的最佳样本集[J]. 华侨大学学报(自然科学版), 2001,22(4):397-400.

Neural network for fatigue life of wind turbine blade numerical calculation of approximate model

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 15

Related Articles 0

Recommended Articles

Metrics

Comments