A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams

[EN] Applications of composite materials in industry have increased due to their high stiffness-to-weight ratio. In the particular case of unidirectional fibers or perpendicular fabrics, the materials behavior is orthotropic, so that an extra degree of freedom, related to the orientation of the fibe...

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Autores: Torregrosa, A. J.|||0000-0003-0933-1626, Gil, A.|||0000-0001-7192-6992, Quintero-Igeño, Pedro-Manuel|||0000-0003-4373-2079, Cremades-Botella, Andrés|||0000-0002-7052-4913
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/196137
Acceso en línea:https://riunet.upv.es/handle/10251/196137
Access Level:acceso abierto
Palabra clave:Aeroelasticity
Reduced Order Model
Artificial Neural Networks
Structural coupling
Flutter
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
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network_acronym_str ES
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repository_id_str
spelling A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beamsTorregrosa, A. J.|||0000-0003-0933-1626Gil, A.|||0000-0001-7192-6992Quintero-Igeño, Pedro-Manuel|||0000-0003-4373-2079Cremades-Botella, Andrés|||0000-0002-7052-4913AeroelasticityReduced Order ModelArtificial Neural NetworksStructural couplingFlutterINGENIERIA AEROESPACIALMAQUINAS Y MOTORES TERMICOS[EN] Applications of composite materials in industry have increased due to their high stiffness-to-weight ratio. In the particular case of unidirectional fibers or perpendicular fabrics, the materials behavior is orthotropic, so that an extra degree of freedom, related to the orientation of the fibers, must be included in the structural optimization. Composite material thin walled beam models have been developed for reducing the computational cost of the simulations. Traditionally, these models have been coupled with potential aerodynamics to calculate the aeroelastic response, and thus, the viscous nonlinear effects have been omitted. In order to capture these effects, this manuscript focus on the development of a Reduced Order Model enhanced by an Artificial Neural Network for the analysis of composite structures under aerodynamic loads. The presented methodology shows the training process of the neural network, the comparison with high fidelity simulations and the design optimization of a carbon fiber laminated foam beam. It is demonstrated that the model reduces the computational cost by orders of magnitude, while still capturing structural couplings and being capable of increasing the flutter velocity by more than 10% with respect to the longitudinal orientation.This project have been partially funded by Spanish Ministry of University through the University Faculty Training (FPU) program with reference FPU19/02201.ElsevierDepartamento de Máquinas y Motores TérmicosEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialInstituto Universitario de Investigación CMT - Clean Mobility & ThermofluidsDepartamento de Matemática AplicadaInstituto Universitario de Matemática Pura y AplicadaEscuela Politécnica Superior de AlcoyMinisterio de UniversidadesUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/196137reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Universidades MIU Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i FPU19%2F02201 Interacción fluido estructura con aplicación a fenómenos aeroelásticos no linealesopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1961372026-06-13T07:49:27Z
dc.title.none.fl_str_mv A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
title A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
spellingShingle A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
Torregrosa, A. J.|||0000-0003-0933-1626
Aeroelasticity
Reduced Order Model
Artificial Neural Networks
Structural coupling
Flutter
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
title_short A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
title_full A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
title_fullStr A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
title_full_unstemmed A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
title_sort A Reduced Order Model based on Artificial Neural Networks for nonlinear aeroelastic phenomena and application to composite material beams
dc.creator.none.fl_str_mv Torregrosa, A. J.|||0000-0003-0933-1626
Gil, A.|||0000-0001-7192-6992
Quintero-Igeño, Pedro-Manuel|||0000-0003-4373-2079
Cremades-Botella, Andrés|||0000-0002-7052-4913
author Torregrosa, A. J.|||0000-0003-0933-1626
author_facet Torregrosa, A. J.|||0000-0003-0933-1626
Gil, A.|||0000-0001-7192-6992
Quintero-Igeño, Pedro-Manuel|||0000-0003-4373-2079
Cremades-Botella, Andrés|||0000-0002-7052-4913
author_role author
author2 Gil, A.|||0000-0001-7192-6992
Quintero-Igeño, Pedro-Manuel|||0000-0003-4373-2079
Cremades-Botella, Andrés|||0000-0002-7052-4913
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Máquinas y Motores Térmicos
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Instituto Universitario de Investigación CMT - Clean Mobility & Thermofluids
Departamento de Matemática Aplicada
Instituto Universitario de Matemática Pura y Aplicada
Escuela Politécnica Superior de Alcoy
Ministerio de Universidades
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Aeroelasticity
Reduced Order Model
Artificial Neural Networks
Structural coupling
Flutter
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
topic Aeroelasticity
Reduced Order Model
Artificial Neural Networks
Structural coupling
Flutter
INGENIERIA AEROESPACIAL
MAQUINAS Y MOTORES TERMICOS
description [EN] Applications of composite materials in industry have increased due to their high stiffness-to-weight ratio. In the particular case of unidirectional fibers or perpendicular fabrics, the materials behavior is orthotropic, so that an extra degree of freedom, related to the orientation of the fibers, must be included in the structural optimization. Composite material thin walled beam models have been developed for reducing the computational cost of the simulations. Traditionally, these models have been coupled with potential aerodynamics to calculate the aeroelastic response, and thus, the viscous nonlinear effects have been omitted. In order to capture these effects, this manuscript focus on the development of a Reduced Order Model enhanced by an Artificial Neural Network for the analysis of composite structures under aerodynamic loads. The presented methodology shows the training process of the neural network, the comparison with high fidelity simulations and the design optimization of a carbon fiber laminated foam beam. It is demonstrated that the model reduces the computational cost by orders of magnitude, while still capturing structural couplings and being capable of increasing the flutter velocity by more than 10% with respect to the longitudinal orientation.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/196137
url https://riunet.upv.es/handle/10251/196137
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Universidades MIU Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i FPU19%2F02201 Interacción fluido estructura con aplicación a fenómenos aeroelásticos no lineales
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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