Prediction of the ultimate loads and structural optimization design for the wind turbine blades with glass-carbon laminate based on neural network

doi:10.19936/j.cnki.2096-8000.20241228.010

Abstract

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 developed 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 evaluation analysis of large-scale wind turbine blades with glass-carbon hybrid structures, and holds significant importance for the lightweight design of wind turbine units.

Key words: carbon fiber composites, layer structure, neural network, prediction of ultimate load, optimization design

CLC Number:

TB332

XU Quanwei, GUO Xiaofeng, QIAO Shujie, LI Siqing, CHE Jiangning. Prediction of the ultimate loads and structural optimization design for the wind turbine blades with glass-carbon laminate based on neural network[J]. COMPOSITES SCIENCE AND ENGINEERING, 2024, 0(12): 69-74.

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