Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80797
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dc.contributor.authorAhmadi, MHen_US
dc.contributor.authorSadeghzadeh, Men_US
dc.contributor.authorRaffiee, AHen_US
dc.contributor.authorChau, KWen_US
dc.date.accessioned2019-05-28T01:09:28Z-
dc.date.available2019-05-28T01:09:28Z-
dc.date.issued2019-
dc.identifier.citationEngineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 327-336en_US
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/80797-
dc.description.abstractThermal performance of pulsating heat pipes (PHPs) is dependent to several factors. Inner and outer diameter of tube, filling ratio, thermal conductivity, heat input, inclination angle, and length of each section are the most influential factors in the design process of PHPs. Since water is a conventional working fluid for PHPs, thermal resistance and effective thermal conductivity of PHPs filled with water are modeled by applying a GMDH (group method of data handling) neural network. The input data of the GMDH model are collected from other experimental investigations to predict the physical properties including thermal resistance and effective thermal conductivity of PHPs filled with water as working fluid. The accuracy of the introduced models are examined through the R-2 tests and resulted in 0.9779 and 0.9906 for thermal resistance and effective thermal conductivity, respectively.en_US
dc.description.sponsorshipDepartment of Civil and Environmental Engineeringen_US
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic University, Department of Civil and Structural Engineeringen_US
dc.relation.ispartofEngineering applications of computational fluid mechanicsen_US
dc.rights© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Groupen_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Mohammad Hossein Ahmadi, Milad Sadeghzadeh, Amir Hossein Raffiee &Kwok-wing Chau (2019) Applying GMDH neural network to estimate the thermal resistance andthermal conductivity of pulsating heat pipes, Engineering Applications of Computational FluidMechanics, 13:1, 327-336 is available at https://dx.doi.org/10.1080/19942060.2019.1582109en_US
dc.subjectPulsating heat pipeen_US
dc.subjectThermal resistanceen_US
dc.subjectEffective thermal conductivityen_US
dc.subjectGMDHen_US
dc.titleApplying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage327en_US
dc.identifier.epage336en_US
dc.identifier.volume13en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2019.1582109en_US
dc.identifier.isiWOS:000461198500001-
dc.identifier.scopus2-s2.0-85065862838-
dc.identifier.eissn1997-003Xen_US
dc.description.validate201905 bcrc-
dc.description.oapublished_final-
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