A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media

Autores
Rangel, Rafael; Gimenez, Juan Marcelo; Oñate, Eugenio; Franci, Alessandro
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work presents a data-driven continuum-discrete multiscale methodology to simulate heat transfer through granular materials.The two scales are hierarchically coupled, where the effective thermal conductivity tensor required by the continuous method at the macroscale is obtained from offline microscale analyses.A set of granular media samples is created through the Discrete Element Method (DEM) to relate microstructure properties with thermal conductivity.The protocol for generating these Representative Volume Elements (RVEs) and homogenizing the microscale response is presented and validated by assessing the representativeness of the granular assemblies.The study found that two local properties, the porosity and the fabric of the material, are sufficient to accurately estimate a representative thermal conductivity tensor.The created dimensionless database of microscale results is used for training a surrogate model based on machine learning.In this way, effective thermal conductivity tensors that accurately reflect the local microstructure can be efficiently predicted from the surrogate model by taking the microstructural properties as inputs.The proposed multiscale methodology enables us to solve heat problems in granular media using a continuum approach with accuracy comparable to a pure discrete computational method but at significantly reduced computational cost.
Fil: Rangel, Rafael. Universidad Politécnica de Catalunya; España
Fil: Gimenez, Juan Marcelo. 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: Oñate, Eugenio. Universidad Politécnica de Catalunya; España
Fil: Franci, Alessandro. Universidad Politécnica de Catalunya; España
Materia
Granular materials
Thermal behavior
Hierarchical multiscale
Continuum–discrete modeling
Machine-learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/258291
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/258291
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular mediaRangel, RafaelGimenez, Juan MarceloOñate, EugenioFranci, AlessandroGranular materialsThermal behaviorHierarchical multiscaleContinuum–discrete modelingMachine-learninghttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2https://purl.org/becyt/ford/2.1https://purl.org/becyt/ford/2This work presents a data-driven continuum-discrete multiscale methodology to simulate heat transfer through granular materials.The two scales are hierarchically coupled, where the effective thermal conductivity tensor required by the continuous method at the macroscale is obtained from offline microscale analyses.A set of granular media samples is created through the Discrete Element Method (DEM) to relate microstructure properties with thermal conductivity.The protocol for generating these Representative Volume Elements (RVEs) and homogenizing the microscale response is presented and validated by assessing the representativeness of the granular assemblies.The study found that two local properties, the porosity and the fabric of the material, are sufficient to accurately estimate a representative thermal conductivity tensor.The created dimensionless database of microscale results is used for training a surrogate model based on machine learning.In this way, effective thermal conductivity tensors that accurately reflect the local microstructure can be efficiently predicted from the surrogate model by taking the microstructural properties as inputs.The proposed multiscale methodology enables us to solve heat problems in granular media using a continuum approach with accuracy comparable to a pure discrete computational method but at significantly reduced computational cost.Fil: Rangel, Rafael. Universidad Politécnica de Catalunya; EspañaFil: Gimenez, Juan Marcelo. 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: Oñate, Eugenio. Universidad Politécnica de Catalunya; EspañaFil: Franci, Alessandro. Universidad Politécnica de Catalunya; EspañaElsevier2024-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/258291Rangel, Rafael; Gimenez, Juan Marcelo; Oñate, Eugenio; Franci, Alessandro; A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media; Elsevier; Computers And Geotechnics; 168; 2-2024; 1-130266-352XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compgeo.2024.106118info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:03:10Zoai:ri.conicet.gov.ar:11336/258291instacron: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 11:03:10.994CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
title A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
spellingShingle A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
Rangel, Rafael
Granular materials
Thermal behavior
Hierarchical multiscale
Continuum–discrete modeling
Machine-learning
title_short A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
title_full A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
title_fullStr A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
title_full_unstemmed A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
title_sort A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media
dc.creator.none.fl_str_mv Rangel, Rafael
Gimenez, Juan Marcelo
Oñate, Eugenio
Franci, Alessandro
author Rangel, Rafael
author_facet Rangel, Rafael
Gimenez, Juan Marcelo
Oñate, Eugenio
Franci, Alessandro
author_role author
author2 Gimenez, Juan Marcelo
Oñate, Eugenio
Franci, Alessandro
author2_role author
author
author
dc.subject.none.fl_str_mv Granular materials
Thermal behavior
Hierarchical multiscale
Continuum–discrete modeling
Machine-learning
topic Granular materials
Thermal behavior
Hierarchical multiscale
Continuum–discrete modeling
Machine-learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/2.1
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This work presents a data-driven continuum-discrete multiscale methodology to simulate heat transfer through granular materials.The two scales are hierarchically coupled, where the effective thermal conductivity tensor required by the continuous method at the macroscale is obtained from offline microscale analyses.A set of granular media samples is created through the Discrete Element Method (DEM) to relate microstructure properties with thermal conductivity.The protocol for generating these Representative Volume Elements (RVEs) and homogenizing the microscale response is presented and validated by assessing the representativeness of the granular assemblies.The study found that two local properties, the porosity and the fabric of the material, are sufficient to accurately estimate a representative thermal conductivity tensor.The created dimensionless database of microscale results is used for training a surrogate model based on machine learning.In this way, effective thermal conductivity tensors that accurately reflect the local microstructure can be efficiently predicted from the surrogate model by taking the microstructural properties as inputs.The proposed multiscale methodology enables us to solve heat problems in granular media using a continuum approach with accuracy comparable to a pure discrete computational method but at significantly reduced computational cost.
Fil: Rangel, Rafael. Universidad Politécnica de Catalunya; España
Fil: Gimenez, Juan Marcelo. 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: Oñate, Eugenio. Universidad Politécnica de Catalunya; España
Fil: Franci, Alessandro. Universidad Politécnica de Catalunya; España
description This work presents a data-driven continuum-discrete multiscale methodology to simulate heat transfer through granular materials.The two scales are hierarchically coupled, where the effective thermal conductivity tensor required by the continuous method at the macroscale is obtained from offline microscale analyses.A set of granular media samples is created through the Discrete Element Method (DEM) to relate microstructure properties with thermal conductivity.The protocol for generating these Representative Volume Elements (RVEs) and homogenizing the microscale response is presented and validated by assessing the representativeness of the granular assemblies.The study found that two local properties, the porosity and the fabric of the material, are sufficient to accurately estimate a representative thermal conductivity tensor.The created dimensionless database of microscale results is used for training a surrogate model based on machine learning.In this way, effective thermal conductivity tensors that accurately reflect the local microstructure can be efficiently predicted from the surrogate model by taking the microstructural properties as inputs.The proposed multiscale methodology enables us to solve heat problems in granular media using a continuum approach with accuracy comparable to a pure discrete computational method but at significantly reduced computational cost.
publishDate 2024
dc.date.none.fl_str_mv 2024-02
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/11336/258291
Rangel, Rafael; Gimenez, Juan Marcelo; Oñate, Eugenio; Franci, Alessandro; A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media; Elsevier; Computers And Geotechnics; 168; 2-2024; 1-13
0266-352X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/258291
identifier_str_mv Rangel, Rafael; Gimenez, Juan Marcelo; Oñate, Eugenio; Franci, Alessandro; A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media; Elsevier; Computers And Geotechnics; 168; 2-2024; 1-13
0266-352X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compgeo.2024.106118
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv 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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