Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes

In this work, artificial neural networks (ANNs) are used to characterize the convective heat transfer rate that occurs during the evaporation of a refrigerant flowing inside tubes of very small diameter. An experimental setup based on an inverse Rankine refrigeration cycle is used to obtain the heat...

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Authors: Ricardo Romero-Méndez, Patricia Lara-Vázquez, Francisco Oviedo-Tolentino, Héctor Martín Durán-García, Francisco Gerardo Pérez-Gutiérrez, Arturo Pacheco-Vega
Format: article
Status:Published version
Publication Date:2016
Country:México
Institution:Universidad Autónoma de San Luis Potosí
Repository:Redalyc-UASLP
OAI Identifier:oai:redalyc.org:40443470003
Online Access:https://www.redalyc.org/articulo.oa?id=40443470003
https://www.redalyc.org/journal/404/40443470003/
https://www.redalyc.org/journal/404/40443470003/html/
https://www.redalyc.org/journal/404/40443470003/40443470003.epub
https://www.redalyc.org/journal/404/40443470003/movil
Access Level:Open access
Keyword:Ingeniería
mini
tubes
thermal systems
compact evaporators
convective heat transfer
id MX_da112bbbf91f8903eeffa19deff7f890
oai_identifier_str oai:redalyc.org:40443470003
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spelling Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-TubesRicardo Romero-MéndezPatricia Lara-VázquezFrancisco Oviedo-TolentinoHéctor Martín Durán-GarcíaFrancisco Gerardo Pérez-GutiérrezArturo Pacheco-VegaIngenieríaminitubesthermal systemscompact evaporatorsconvective heat transferIn this work, artificial neural networks (ANNs) are used to characterize the convective heat transfer rate that occurs during the evaporation of a refrigerant flowing inside tubes of very small diameter. An experimental setup based on an inverse Rankine refrigeration cycle is used to obtain the heat transfer data in an R-134a refrigerant mini-tube evaporator set operated under constant heat flux conditions. A considerable amount of data was acquired to map the thermal performance of the evaporative process under analysis, 75% of which were used for training the ANN and 25% were reserved for prediction purposes. Several neural network configurations were trained and the most accurate was selected to predict the thermal behavior. The results obtained in this investigation reveal the convenience of using ANNs as an accurate predictive tool for determination of convective heat transfer rates inside mini-tube evaporators.Universidad Nacional Autónoma de México2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdf1405-7743https://www.redalyc.org/articulo.oa?id=40443470003https://www.redalyc.org/journal/404/40443470003/https://www.redalyc.org/journal/404/40443470003/html/https://www.redalyc.org/journal/404/40443470003/40443470003.epubhttps://www.redalyc.org/journal/404/40443470003/movilIngeniería. Investigación y Tecnología (México) Num.1 Vol.XVIIreponame:Redalyc-UASLPinstname:Universidad Autónoma de San Luis Potosíinstacron:UASLPenhttp://www.redalyc.org/revista.oa?id=404Ingeniería. Investigación y Tecnologíainfo:eu-repo/semantics/openAccessoai:redalyc.org:404434700032024-08-23T15:33:43Z
dc.title.none.fl_str_mv Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
title Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
spellingShingle Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
Ricardo Romero-Méndez
Ingeniería
mini
tubes
thermal systems
compact evaporators
convective heat transfer
title_short Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
title_full Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
title_fullStr Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
title_full_unstemmed Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
title_sort Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes
dc.creator.none.fl_str_mv Ricardo Romero-Méndez
Patricia Lara-Vázquez
Francisco Oviedo-Tolentino
Héctor Martín Durán-García
Francisco Gerardo Pérez-Gutiérrez
Arturo Pacheco-Vega
author Ricardo Romero-Méndez
author_facet Ricardo Romero-Méndez
Patricia Lara-Vázquez
Francisco Oviedo-Tolentino
Héctor Martín Durán-García
Francisco Gerardo Pérez-Gutiérrez
Arturo Pacheco-Vega
author_role author
author2 Patricia Lara-Vázquez
Francisco Oviedo-Tolentino
Héctor Martín Durán-García
Francisco Gerardo Pérez-Gutiérrez
Arturo Pacheco-Vega
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ingeniería
mini
tubes
thermal systems
compact evaporators
convective heat transfer
topic Ingeniería
mini
tubes
thermal systems
compact evaporators
convective heat transfer
description In this work, artificial neural networks (ANNs) are used to characterize the convective heat transfer rate that occurs during the evaporation of a refrigerant flowing inside tubes of very small diameter. An experimental setup based on an inverse Rankine refrigeration cycle is used to obtain the heat transfer data in an R-134a refrigerant mini-tube evaporator set operated under constant heat flux conditions. A considerable amount of data was acquired to map the thermal performance of the evaporative process under analysis, 75% of which were used for training the ANN and 25% were reserved for prediction purposes. Several neural network configurations were trained and the most accurate was selected to predict the thermal behavior. The results obtained in this investigation reveal the convenience of using ANNs as an accurate predictive tool for determination of convective heat transfer rates inside mini-tube evaporators.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 1405-7743
https://www.redalyc.org/articulo.oa?id=40443470003
https://www.redalyc.org/journal/404/40443470003/
https://www.redalyc.org/journal/404/40443470003/html/
https://www.redalyc.org/journal/404/40443470003/40443470003.epub
https://www.redalyc.org/journal/404/40443470003/movil
identifier_str_mv 1405-7743
url https://www.redalyc.org/articulo.oa?id=40443470003
https://www.redalyc.org/journal/404/40443470003/
https://www.redalyc.org/journal/404/40443470003/html/
https://www.redalyc.org/journal/404/40443470003/40443470003.epub
https://www.redalyc.org/journal/404/40443470003/movil
dc.language.none.fl_str_mv en
language_invalid_str_mv en
dc.relation.none.fl_str_mv http://www.redalyc.org/revista.oa?id=404
dc.rights.none.fl_str_mv Ingeniería. Investigación y Tecnología
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Ingeniería. Investigación y Tecnología
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional Autónoma de México
publisher.none.fl_str_mv Universidad Nacional Autónoma de México
dc.source.none.fl_str_mv Ingeniería. Investigación y Tecnología (México) Num.1 Vol.XVII
reponame:Redalyc-UASLP
instname:Universidad Autónoma de San Luis Potosí
instacron:UASLP
instname_str Universidad Autónoma de San Luis Potosí
instacron_str UASLP
institution UASLP
reponame_str Redalyc-UASLP
collection Redalyc-UASLP
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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