基于机器学习的风电叶片关键部位设计和优化方法OA

An analysis of the structural performance of the spar cap of wind turbine blades was conducted,and a reverse structural optimization method for key components of wind turbine blades based on a machine learning model was developed,combining the use of FOCUS and the Python programming language.Taking a 1.5 MW wind turbine blade design as an example,the finite element analysis(FEA)model of the blade was established.The thickness of the spar cap was selected as the design variable,and the peak strain of the spar cap served as the opti-mization objective.A machine learning model was developed to reflect the underlying mapping relationship between the spar cap layup parameters,strain,and mass.Based on this machine learning model,a self-learning cyclic opti-mization method was developed.This method enables the rapid iteration of key parts of the same blade type under different wind fields and load conditions.The optimized spar cap improves performance by about 11.44％while maintaining the same cost.Due to its high portability,this method is expected to become an effective tool for the de-sign and optimization of key parts of wind turbine blades.