复合材料科学与工程Issue(12)：69-74,95,7.DOI:10.19936/j.cnki.2096-8000.20241228.010
基于神经网络的风电叶片极限载荷预测及玻碳混合铺层结构优化
Prediction of the ultimate loads and structural optimization design for the wind turbine blades with glass-carbon laminate based on neural network
摘要
Abstract
In order to optimize the layup structure of wind turbine blades with the practical ultimate loads,the study was conducted on the DTU10MW wind turbine blade with a length of 89 m.A neural network model was de-veloped through a Latin hypercube experiment,by using the root triaxial ply thickness,trailing edge uniaxial ply thickness,spar cap uniaxial ply thickness,pre-bend value of blade-tip,pre-bend index as input variables,and the blade tip deformation and ultimate loads of blade root as output variables.The layup structure of the wind turbine blades was optimized by using the particle swarm algorithm.For the optimized design of the glass-carbon hybrid blades,a newly proposed method for calculating blade mass and cost was used to analyze their load characteristics and economic feasibility.This research provides practical reference value for the optimization design and cost evalua-tion analysis of large-scale wind turbine blades with glass-carbon hybrid structures,and holds significant importance for the lightweight design of wind turbine units.关键词
碳纤维复合材料/铺层结构/神经网络/极限载荷预测/优化设计Key words
carbon fiber composites/layer structure/neural network/prediction of ultimate load/optimization design分类
通用工业技术引用本文复制引用
徐权威,郭小锋,乔书杰,李思卿,车江宁..基于神经网络的风电叶片极限载荷预测及玻碳混合铺层结构优化[J].复合材料科学与工程,2024,(12):69-74,95,7.基金项目
国家自然科学基金(51705545) （51705545）
河南省科技攻关项目(222102220058,222102220091) （222102220058,222102220091）
