Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model
作者机构：Department of Engineering MechanicsHohai UniversityNanjing 211100China
出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿(英文版))
年 卷 期：2023年第17卷第7期
页 面：1086-1099页
核心收录：
学科分类：0820[工学-石油与天然气工程] 0711[理学-系统科学] 07[理学] 0818[工学-地质资源与地质工程] 0903[农学-农业资源与环境] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(Grant No.12072104) the National Key R&D Program of China(No.2018YFC0406703)。
主 题：reliability-based design optimization offshore wind turbine parametric finite element analysis RBF surrogate model uncertain soil parameter
摘 要：An efficient reliability-based design optimization method for the support structures of monopile offshore wind turbines is proposed herein.First,parametric finite element analysis(FEA)models of the support structure are established by considering stochastic variables.Subsequently,a surrogate model is constructed using a radial basis function(RBF)neural network to replace the time-consuming FEA.The uncertainties of loads,material properties,key sizes of structural components,and soil properties are considered.The uncertainty of soil properties is characterized by the variabilities of the unit weight,friction angle,and elastic modulus of soil.Structure reliability is determined via Monte Carlo simulation,and five limit states are considered,i.e.,structural stresses,tower top displacements,mudline rotation,buckling,and natural frequency.Based on the RBF surrogate model and particle swarm optimization algorithm,an optimal design is established to minimize the volume.Results show that the proposed method can yield an optimal design that satisfies the target reliability and that the constructed RBF surrogate model significantly improves the optimization efficiency.Furthermore,the uncertainty of soil parameters significantly affects the optimization results,and increasing the monopile diameter is a cost-effective approach to cope with the uncertainty of soil parameters.
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