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수냉식 열전 복사 냉방 패널의 성능 예측 모델 개발

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dc.contributor.author김민성-
dc.contributor.author이재희-
dc.contributor.author정재원-
dc.date.accessioned2023-08-01T06:41:07Z-
dc.date.available2023-08-01T06:41:07Z-
dc.date.created2023-07-21-
dc.date.issued2022-04-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188446-
dc.description.abstractThe main objective of this research is to investigate desirable flow rate and temperature of cooling water which is produced by freecooling systems to obtain valid cooling capacity of the water-cooled thermoelectric radiant cooling panel(TERCP). A water-cooled TERCP was constructed and tested under a controlled laboratory environment by using the developed mock-up models based on the previous studies.The results showed that the temperature of cooling water to release heat from hot side of the thermoelectric modules(TEMs) is the maindesign variable to calculate the cooling capacity of the water-cooled TERCP and input power of TEMs. On the other hand, flow rate ofcooling water is brought to a minimum as heat removal factor.-
dc.language한국어-
dc.language.isoko-
dc.publisher(사)대한건축학회-
dc.title수냉식 열전 복사 냉방 패널의 성능 예측 모델 개발-
dc.title.alternativeDevelopment of performance prediction model for water-cooled thermoelectric radiant cooling panel-
dc.typeArticle-
dc.contributor.affiliatedAuthor정재원-
dc.identifier.bibliographicCitation대한건축학회 춘계학술발표대회 논문집, v.42, no.1, pp.357 - 358-
dc.relation.isPartOf대한건축학회 춘계학술발표대회 논문집-
dc.citation.title대한건축학회 춘계학술발표대회 논문집-
dc.citation.volume42-
dc.citation.number1-
dc.citation.startPage357-
dc.citation.endPage358-
dc.type.rimsART-
dc.type.docTypeProceeding-
dc.description.journalClass3-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassother-
dc.subject.keywordAuthor열전모듈-
dc.subject.keywordAuthor복사 냉방 패널-
dc.subject.keywordAuthor수냉 시스템-
dc.subject.keywordAuthor성능 예측 모델-
dc.subject.keywordAuthorThermoelectric module-
dc.subject.keywordAuthorradiant cooling panel-
dc.subject.keywordAuthorwater-cooled system-
dc.subject.keywordAuthorperformance prediction model-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11071977-
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