Fatigue strength prediction in composite materials of wind turbine blades under dry-wet conditions: An artificial neural network approach

被引：5

作者：

Ziane, Khaled ^{[1
]}

Zebirate, Soraya ^{[1
]}

Zaitri, Adel ^{[2
]}

机构：

[1] Univ Oran 2 MB, IMSI, Lab Ingn Secur Ind & Dev Durable, BP 170 El Mnaouer, Oran 31000, Algeria

[2] Univ Djelfa, Fac Sci & Technol, Dept Sci & Tech, Djelfa, Algeria

来源：

WIND ENGINEERING | 2016年 / 40卷 / 03期

关键词：

Prediction; fatigue strength; environmental effects; wind turbine blade; composite materials;

D O I：

10.1177/0309524X16641849

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this article, the fatigue strength in composite materials of wind turbine blades under dry-wet conditions is predicted using artificial neural networks. Compression-compression constant amplitude fatigue tests were performed on thermoset polymer resins including polyesters and vinyl esters. Coupons were tested under an air temperature of 20 degrees C and 50 degrees C in both dry and wet conditions. The results show that artificial neural network can provide accurate fatigue strength prediction for different resin matrices under different values of temperature.

引用

页码：189 / 198

页数：10

共 27 条

[1] Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks [J].