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Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model

An ef cient 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 mo...

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Published in:Frontiers of Structural and Civil Engineering 2023-07, Vol.17 (7), p.1086-1099
Main Authors: YU, Changhai, LV, Xiaolong, HUANG, Dan, JIANG, Dongju
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description An ef cient 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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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. 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Struct. Civ. Eng</addtitle><description>An ef cient 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. 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Struct. Civ. Eng</stitle><date>2023-07-01</date><risdate>2023</risdate><volume>17</volume><issue>7</issue><spage>1086</spage><epage>1099</epage><pages>1086-1099</pages><issn>2095-2430</issn><eissn>2095-2449</eissn><abstract>An ef cient 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.</abstract><cop>Beijing</cop><pub>Higher Education Press</pub><doi>10.1007/s11709-023-0976-8</doi><tpages>14</tpages></addata></record>
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subjects Algorithms
Cities
Civil Engineering
Countries
Design
Design optimization
Engineering
Finite element method
Limit states
Material properties
Mathematical models
Mechanical properties
Modulus of elasticity
Monte Carlo simulation
Neural networks
Offshore
Offshore structures
offshore wind turbine
Parameters
parametric finite element analysis
Particle swarm optimization
Radial basis function
RBF surrogate model
Regions
reliability-based design optimization
Research Article
Resonant frequencies
Soil properties
Soil structure
Soils
Stochasticity
Structural reliability
Turbines
uncertain soil parameter
Uncertainty
Wind power
Wind turbines
title Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model
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