A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades

Autores
Albanesi, Alejandro Eduardo; Roman, Nadia Denise; Bre, Facundo; Fachinotti, Victor Daniel
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In wind turbine blades, the complex resultant geometry due to the aerodynamic design cannot be modified in the successive mechanical design stage. Hence, the reduction of the weight and manufacturing costs of the blades while assuring appropriate levels of structural stiffness, integrity and reliability, require a composite material layout that must be optimally defined. The aim of this work is to present a metamodel-based method to optimize the composite laminate of wind turbine blades. This methodology combines a genetic algorithm (GA) with an artificial neural network (ANN) in order to reduce the computational cost of the optimization procedure. Therefore, at first, representative samples were built to train and validate the ANN model, and then, the ANN model is coupled with GA to find the optimal structural blade design. As an actual case study, the method was applied to redesign a medium-power 40-kW wind turbine blade to reduce its mass while structural and manufacturing constrained are fulfilled. The results indicated that is possible to save of up to 20% of laminated mass compared to a reference design. Furthermore, a 40% reduction of the computational cost was achieved in contrast with the typical simulation-based optimization approach.
Fil: Albanesi, Alejandro Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Roman, Nadia Denise. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Materia
ARTIFICIAL NEURAL NETWORK
COMPOSITE MATERIALS
GENETIC ALGORITHM
OPTIMIZATION
WIND TURBINE BLADE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/86223
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/86223
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine bladesAlbanesi, Alejandro EduardoRoman, Nadia DeniseBre, FacundoFachinotti, Victor DanielARTIFICIAL NEURAL NETWORKCOMPOSITE MATERIALSGENETIC ALGORITHMOPTIMIZATIONWIND TURBINE BLADEhttps://purl.org/becyt/ford/2.3https://purl.org/becyt/ford/2https://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2In wind turbine blades, the complex resultant geometry due to the aerodynamic design cannot be modified in the successive mechanical design stage. Hence, the reduction of the weight and manufacturing costs of the blades while assuring appropriate levels of structural stiffness, integrity and reliability, require a composite material layout that must be optimally defined. The aim of this work is to present a metamodel-based method to optimize the composite laminate of wind turbine blades. This methodology combines a genetic algorithm (GA) with an artificial neural network (ANN) in order to reduce the computational cost of the optimization procedure. Therefore, at first, representative samples were built to train and validate the ANN model, and then, the ANN model is coupled with GA to find the optimal structural blade design. As an actual case study, the method was applied to redesign a medium-power 40-kW wind turbine blade to reduce its mass while structural and manufacturing constrained are fulfilled. The results indicated that is possible to save of up to 20% of laminated mass compared to a reference design. Furthermore, a 40% reduction of the computational cost was achieved in contrast with the typical simulation-based optimization approach.Fil: Albanesi, Alejandro Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Roman, Nadia Denise. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaElsevier2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/86223Albanesi, Alejandro Eduardo; Roman, Nadia Denise; Bre, Facundo; Fachinotti, Victor Daniel; A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades; Elsevier; Composite Structures; 194; 6-2018; 345-3560263-8223CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0263822318301879info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compstruct.2018.04.015info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:10:15Zoai:ri.conicet.gov.ar:11336/86223instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:10:16.076CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
title A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
spellingShingle A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
Albanesi, Alejandro Eduardo
ARTIFICIAL NEURAL NETWORK
COMPOSITE MATERIALS
GENETIC ALGORITHM
OPTIMIZATION
WIND TURBINE BLADE
title_short A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
title_full A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
title_fullStr A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
title_full_unstemmed A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
title_sort A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades
dc.creator.none.fl_str_mv Albanesi, Alejandro Eduardo
Roman, Nadia Denise
Bre, Facundo
Fachinotti, Victor Daniel
author Albanesi, Alejandro Eduardo
author_facet Albanesi, Alejandro Eduardo
Roman, Nadia Denise
Bre, Facundo
Fachinotti, Victor Daniel
author_role author
author2 Roman, Nadia Denise
Bre, Facundo
Fachinotti, Victor Daniel
author2_role author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORK
COMPOSITE MATERIALS
GENETIC ALGORITHM
OPTIMIZATION
WIND TURBINE BLADE
topic ARTIFICIAL NEURAL NETWORK
COMPOSITE MATERIALS
GENETIC ALGORITHM
OPTIMIZATION
WIND TURBINE BLADE
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.3
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In wind turbine blades, the complex resultant geometry due to the aerodynamic design cannot be modified in the successive mechanical design stage. Hence, the reduction of the weight and manufacturing costs of the blades while assuring appropriate levels of structural stiffness, integrity and reliability, require a composite material layout that must be optimally defined. The aim of this work is to present a metamodel-based method to optimize the composite laminate of wind turbine blades. This methodology combines a genetic algorithm (GA) with an artificial neural network (ANN) in order to reduce the computational cost of the optimization procedure. Therefore, at first, representative samples were built to train and validate the ANN model, and then, the ANN model is coupled with GA to find the optimal structural blade design. As an actual case study, the method was applied to redesign a medium-power 40-kW wind turbine blade to reduce its mass while structural and manufacturing constrained are fulfilled. The results indicated that is possible to save of up to 20% of laminated mass compared to a reference design. Furthermore, a 40% reduction of the computational cost was achieved in contrast with the typical simulation-based optimization approach.
Fil: Albanesi, Alejandro Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Roman, Nadia Denise. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
description In wind turbine blades, the complex resultant geometry due to the aerodynamic design cannot be modified in the successive mechanical design stage. Hence, the reduction of the weight and manufacturing costs of the blades while assuring appropriate levels of structural stiffness, integrity and reliability, require a composite material layout that must be optimally defined. The aim of this work is to present a metamodel-based method to optimize the composite laminate of wind turbine blades. This methodology combines a genetic algorithm (GA) with an artificial neural network (ANN) in order to reduce the computational cost of the optimization procedure. Therefore, at first, representative samples were built to train and validate the ANN model, and then, the ANN model is coupled with GA to find the optimal structural blade design. As an actual case study, the method was applied to redesign a medium-power 40-kW wind turbine blade to reduce its mass while structural and manufacturing constrained are fulfilled. The results indicated that is possible to save of up to 20% of laminated mass compared to a reference design. Furthermore, a 40% reduction of the computational cost was achieved in contrast with the typical simulation-based optimization approach.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/86223
Albanesi, Alejandro Eduardo; Roman, Nadia Denise; Bre, Facundo; Fachinotti, Victor Daniel; A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades; Elsevier; Composite Structures; 194; 6-2018; 345-356
0263-8223
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86223
identifier_str_mv Albanesi, Alejandro Eduardo; Roman, Nadia Denise; Bre, Facundo; Fachinotti, Victor Daniel; A metamodel-based optimization approach to reduce the weight of composite laminated wind turbine blades; Elsevier; Composite Structures; 194; 6-2018; 345-356
0263-8223
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0263822318301879
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compstruct.2018.04.015
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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