Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling

Bibliographic Details
Main Author: Fernandes, C.
Publication Date: 2020
Other Authors: Faroughi, S. A., Nóbrega, J. M., McKinley, G. H.
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/69655
Summary: [extract] Objetives: explore the possibility of using Deep Learning (DL) techniques to evaluate the drag coefficient of small non-Brownian particles translating and settling in nonlinear viscoelastic fluids. The long-term objective is the development of a 3D numerical code for particle-laden viscoelastic flows (PLVF), which will contribute to understanding many advanced manufacturing and industrial operations, specifically the hydraulic fracturing process.
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spelling Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modellingEngenharia e Tecnologia::Engenharia Mecânica[extract] Objetives: explore the possibility of using Deep Learning (DL) techniques to evaluate the drag coefficient of small non-Brownian particles translating and settling in nonlinear viscoelastic fluids. The long-term objective is the development of a 3D numerical code for particle-laden viscoelastic flows (PLVF), which will contribute to understanding many advanced manufacturing and industrial operations, specifically the hydraulic fracturing process.Universidade do MinhoFernandes, C.Faroughi, S. A.Nóbrega, J. M.McKinley, G. H.2020-102020-10-01T00:00:00Zconference posterinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/69655engC. Fernandes, S.A. Faroughi, J.M. Nóbrega, G.H. McKinley, “Exploratory Project 2019 - Deep learning for particle-laden viscoelastic flow modelling”, MIT Portugal 2020 Annual Conference, Lisbon, Portugal, 15 October, 2020info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T05:49:40Zoai:repositorium.sdum.uminho.pt:1822/69655Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:31:29.486599Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
title Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
spellingShingle Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
Fernandes, C.
Engenharia e Tecnologia::Engenharia Mecânica
title_short Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
title_full Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
title_fullStr Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
title_full_unstemmed Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
title_sort Exploratory project 2019 - deep learning for particle-laden viscoelastic flow modelling
author Fernandes, C.
author_facet Fernandes, C.
Faroughi, S. A.
Nóbrega, J. M.
McKinley, G. H.
author_role author
author2 Faroughi, S. A.
Nóbrega, J. M.
McKinley, G. H.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Fernandes, C.
Faroughi, S. A.
Nóbrega, J. M.
McKinley, G. H.
dc.subject.por.fl_str_mv Engenharia e Tecnologia::Engenharia Mecânica
topic Engenharia e Tecnologia::Engenharia Mecânica
description [extract] Objetives: explore the possibility of using Deep Learning (DL) techniques to evaluate the drag coefficient of small non-Brownian particles translating and settling in nonlinear viscoelastic fluids. The long-term objective is the development of a 3D numerical code for particle-laden viscoelastic flows (PLVF), which will contribute to understanding many advanced manufacturing and industrial operations, specifically the hydraulic fracturing process.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
2020-10-01T00:00:00Z
dc.type.driver.fl_str_mv conference poster
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/69655
url http://hdl.handle.net/1822/69655
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv C. Fernandes, S.A. Faroughi, J.M. Nóbrega, G.H. McKinley, “Exploratory Project 2019 - Deep learning for particle-laden viscoelastic flow modelling”, MIT Portugal 2020 Annual Conference, Lisbon, Portugal, 15 October, 2020
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833595369549725696

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