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dc.contributor.authorAvalos Gauna, Edgar
dc.contributor.authorPalafox Novack, Leon Felipe
dc.contributor.otherCampus Ciudad de Méxicoes
dc.date.accessioned2020-06-17T02:32:14Z
dc.date.available2020-06-17T02:32:14Z
dc.date.issued2019
dc.identifier.citationAvalos Gauna, E. y Palafox Novack, L. F. (2019). Heat transfer coefficient prediction of a porous material by implementing a machine learning model on a CFD data set. En: Proceedings of the 6 th International Conference of Fluid Flow, Heat and Mass Transfer (FFHMT'19) Ottawa, Canada, June, 2019, (FFHMT 149). Avestia Publishing. DOI: 10.11159/ffhmt19.149en
dc.identifier.isbn9781927877593
dc.identifier.issn2369-3029
dc.identifier.urihttps://hdl.handle.net/20.500.12552/5167
dc.identifier.urihttp://dx.doi.org/10.11159/ffhmt19.149
dc.description.abstractDuring many years, the search for new and improved materials has been an arduous task. It has mainly focused on experimentation and in recent years on computer aided techniques (i.e. numerical simulation). These two approaches defined the way material science works. Yet, both techniques have shown cost-efficiency disadvantages. Optimization algorithms, like the ones used in machine learning, have proven to be an alternative tool when dealing with lots of data and finding a particular solution. Even though the use of machine learning is a well stablished technique in other fields, its application in material science is relatively new. Material Informatics provides a new approach to analyse materials such as porous metals by employing previous data sets. This paper studies a new technique to predict the heat transfer coefficient of an open-cell porous structure while running water passes through the material. A CFD data set was employed by a Machine Learning technique in order to establish a relationship between the input parameters (porosity, pore size, pore distribution and flow rate) and the heat transfer coefficient of the sample. The results obtained from the analyses were compared with previous findings, concluding that by utilising a Machine Leaning technique is possible to obtain a more accurate and much better fit model. © 2019, Avestia Publishing.en
dc.language.isoengen
dc.publisherAvestia Publishingen
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofREPOSITORIO NACIONAL CONACYTes
dc.relation.ispartofOPENAIREes
dc.relation.ispartofseriesFFHMT 149
dc.rightsAcceso Abiertoes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0*
dc.rights.urihttp://avestia.international-aset.com/FFHMT2019_Proceedings/files/ethics.html
dc.sourceProceedings of the 6 th International Conference of Fluid Flow, Heat and Mass Transfer (FFHMT'19) Ottawa, Canada, June, 2019en
dc.subjectHeat transfer coefficienten
dc.subjectMachine learningen
dc.subjectMaterials informaticsen
dc.subjectPorosityen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.subject.classificationIngenieríaes
dc.titleHeat transfer coefficient prediction of a porous material by implementing a machine learning model on a CFD data seten
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dc.description.versionVersión del editores
dc.identifier.doi10.11159/ffhmt19.149


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