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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 |
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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. |
doi_str_mv | 10.1007/s11709-023-0976-8 |
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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.</description><identifier>ISSN: 2095-2430</identifier><identifier>EISSN: 2095-2449</identifier><identifier>DOI: 10.1007/s11709-023-0976-8</identifier><language>eng</language><publisher>Beijing: Higher Education Press</publisher><subject>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</subject><ispartof>Frontiers of Structural and Civil Engineering, 2023-07, Vol.17 (7), p.1086-1099</ispartof><rights>Copyright reserved, 2023, Higher Education Press</rights><rights>Higher Education Press 2023</rights><rights>Higher Education Press 2023.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c365t-5423d4c3c8462ff899c4817faa8cebc15e9bfe862c0b6dce5a3982007496b9fa3</citedby><cites>FETCH-LOGICAL-c365t-5423d4c3c8462ff899c4817faa8cebc15e9bfe862c0b6dce5a3982007496b9fa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>YU, Changhai</creatorcontrib><creatorcontrib>LV, Xiaolong</creatorcontrib><creatorcontrib>HUANG, Dan</creatorcontrib><creatorcontrib>JIANG, Dongju</creatorcontrib><title>Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model</title><title>Frontiers of Structural and Civil Engineering</title><addtitle>Front. 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. 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.</description><subject>Algorithms</subject><subject>Cities</subject><subject>Civil Engineering</subject><subject>Countries</subject><subject>Design</subject><subject>Design optimization</subject><subject>Engineering</subject><subject>Finite element method</subject><subject>Limit states</subject><subject>Material properties</subject><subject>Mathematical models</subject><subject>Mechanical properties</subject><subject>Modulus of elasticity</subject><subject>Monte Carlo simulation</subject><subject>Neural networks</subject><subject>Offshore</subject><subject>Offshore structures</subject><subject>offshore wind turbine</subject><subject>Parameters</subject><subject>parametric finite element analysis</subject><subject>Particle swarm optimization</subject><subject>Radial basis function</subject><subject>RBF surrogate model</subject><subject>Regions</subject><subject>reliability-based design optimization</subject><subject>Research Article</subject><subject>Resonant frequencies</subject><subject>Soil properties</subject><subject>Soil structure</subject><subject>Soils</subject><subject>Stochasticity</subject><subject>Structural reliability</subject><subject>Turbines</subject><subject>uncertain soil parameter</subject><subject>Uncertainty</subject><subject>Wind power</subject><subject>Wind turbines</subject><issn>2095-2430</issn><issn>2095-2449</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhosoKKs_wFvAczUf_UiOurgqLAii55Cmk26029QkRdZfb5aK3hYCk5m8zwSeLLsk-JpgXN8EQmosckxZjkVd5fwoO6NYlDktCnH8d2f4NLsI4R1jTHDNMGdn2ccL9FY1trdxlzcqQItaCLYbkBuj3dpvFa1LjUnHhI3zgL7s0KI4-cYOgMI0js5HFKKfdBpCQFOwQ4de7lbp0XvXqQho61roz7MTo_oAF791kb2t7l-Xj_n6-eFpebvONavKmJcFZW2hmeZFRY3hQuiCk9ooxTU0mpQgGgO8oho3VauhVExwmjwUomqEUWyRXc17R-8-JwhRvrvJD-lLSXlV1xUXtEwpMqe0dyF4MHL0dqv8ThIs91rlrFUmrXKvVfLE0JkJKTt04P83H4L4DG1stwEP7ZgsBWm8G6IFfwj9AUqJju4</recordid><startdate>20230701</startdate><enddate>20230701</enddate><creator>YU, Changhai</creator><creator>LV, Xiaolong</creator><creator>HUANG, Dan</creator><creator>JIANG, Dongju</creator><general>Higher Education Press</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20230701</creationdate><title>Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model</title><author>YU, Changhai ; LV, Xiaolong ; HUANG, Dan ; JIANG, Dongju</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-5423d4c3c8462ff899c4817faa8cebc15e9bfe862c0b6dce5a3982007496b9fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Cities</topic><topic>Civil Engineering</topic><topic>Countries</topic><topic>Design</topic><topic>Design optimization</topic><topic>Engineering</topic><topic>Finite element method</topic><topic>Limit states</topic><topic>Material properties</topic><topic>Mathematical models</topic><topic>Mechanical properties</topic><topic>Modulus of elasticity</topic><topic>Monte Carlo simulation</topic><topic>Neural networks</topic><topic>Offshore</topic><topic>Offshore structures</topic><topic>offshore wind turbine</topic><topic>Parameters</topic><topic>parametric finite element analysis</topic><topic>Particle swarm optimization</topic><topic>Radial basis function</topic><topic>RBF surrogate model</topic><topic>Regions</topic><topic>reliability-based design optimization</topic><topic>Research Article</topic><topic>Resonant frequencies</topic><topic>Soil properties</topic><topic>Soil structure</topic><topic>Soils</topic><topic>Stochasticity</topic><topic>Structural reliability</topic><topic>Turbines</topic><topic>uncertain soil parameter</topic><topic>Uncertainty</topic><topic>Wind power</topic><topic>Wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>YU, Changhai</creatorcontrib><creatorcontrib>LV, Xiaolong</creatorcontrib><creatorcontrib>HUANG, Dan</creatorcontrib><creatorcontrib>JIANG, Dongju</creatorcontrib><collection>CrossRef</collection><jtitle>Frontiers of Structural and Civil Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>YU, Changhai</au><au>LV, Xiaolong</au><au>HUANG, Dan</au><au>JIANG, Dongju</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model</atitle><jtitle>Frontiers of Structural and Civil Engineering</jtitle><stitle>Front. 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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