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Current status and future trends of computational methods to predict frost formation

dc.contributor.authorAguiar, Martim
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorSilva, Pedro Dinho da
dc.date.accessioned2019-10-25T16:00:56Z
dc.date.available2019-10-25T16:00:56Z
dc.date.issued2018
dc.description.abstractNowadays, the increasing energy prices and associated environmental concerns lead the refrigeration systems’ developers and manufacturers to develop more energy efficient and sustainable equipment and devices. On the most demanding systems, intense usage results in the fast accumulation of ice on the evaporator fins that reduces the efficiency and may even clog the system. These systems often have time-controlled defrost cycles, that heat the evaporator, melting the ice and allowing the system to keep working normally after the defrost cycle. This cycle consumes extra energy and causes a thermal imbalance on the refrigerated space, that may result in a worst refrigeration quality. If it was possible to avoid the defrosting cycle passively (without energy consumption) its efficiency would greatly increase, and the refrigeration temperature would be more stable. Currently defrost cycles cannot be avoided in an economically viable way, although new designs, materials and configurations show promising results, and are currently being investigated. These studies require experimental tests that may become expensive as several geometries, topologies, materials and surface treatment combinations should be evaluated. To access the efficiency before these experimental tests, computational models that simulate frost formation could predict with some accuracy which of the most promising configurations should be then tested experimentally. The present paper aims to review the computational methods to predict frost formation and compare them for possible usage in the computational study of evaporators. Additionally, the future trends of the simulations are discussed, taking into account physical and mathematical models, numerical procedures and the accuracy of the dynamic pattern of the predictions.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/7407
dc.language.isoengpt_PT
dc.publisherAGRO 2018 - International Congress on Organizational Managementpt_PT
dc.subjectDemand defrostingpt_PT
dc.subjectFrost measurementpt_PT
dc.subjectControlling strategypt_PT
dc.subjectFrost detectionpt_PT
dc.subjectEvaporator designpt_PT
dc.subjectFinned tube evaporatorspt_PT
dc.titleCurrent status and future trends of computational methods to predict frost formationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceCastelo Branco, Portugalpt_PT
oaire.citation.titleAGRO 2018 - International Congress on Organizational Managementpt_PT
person.familyNameAguiar
person.familyNameGaspar
person.familyNameSilva
person.givenNameMartim
person.givenNamePedro Dinis
person.givenNamePedro
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id3C14-B985-13A1
person.identifier.orcid0000-0003-0672-0378
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0003-2204-3397
person.identifier.ridN-3016-2013
person.identifier.ridA-6638-2016
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id7203088999
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication95cdd8b0-fb9e-409c-b8fd-b661bab52972
relation.isAuthorOfPublicationb69e2ba0-43af-4cf7-873e-090fd9fc6c94
relation.isAuthorOfPublication483bd890-f402-4ce4-b740-b37febcba37f
relation.isAuthorOfPublication.latestForDiscovery483bd890-f402-4ce4-b740-b37febcba37f

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