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dc.contributor.authorUl Islam, Badaren_US
dc.contributor.authorMukhtar, Ahmaden_US
dc.contributor.authorSaqib, Sidraen_US
dc.contributor.authorMahmood, Abiden_US
dc.contributor.authorRafiq, Sikanderen_US
dc.contributor.authorHameed, Ayeshaen_US
dc.contributor.authorKhan, Muhammad Saaden_US
dc.contributor.authorHamid, Khaliden_US
dc.contributor.authorUllah, Samien_US
dc.contributor.authorAl-Sehemi, Abdullah G.en_US
dc.contributor.authorIbrahim, Muhammaden_US
dc.date.accessioned2020-07-01T05:22:07Z-
dc.date.available2020-07-01T05:22:07Z-
dc.date.issued1970-01-01en_US
dc.identifier.issn0930-7516en_US
dc.identifier.urihttp://dx.doi.org/10.1002/ceat.201900600en_US
dc.identifier.urihttp://hdl.handle.net/11536/154532-
dc.description.abstractThe synthesis of a nanofluid from multiwalled carbon nanotubes (MWCNTs) and Kapok seed oil by a one-step method is reported. The nanofluid showed excellent stability of nanoparticle dispersion in the base fluid. Furthermore, this study deals with the prediction of the thermal conductivity of the MWCNTs-kapok seed oil nanofluid. To improve the prediction of the thermal conductivity of the nanofluid, the artificial neural network (ANN) computing approach was used with different algorithms including the back-propagation, Levenberg-Marquardt, and genetic algorithm (GA). Finally, the ANN-GA model is recommended for the prediction of thermal conductivity with higher accuracy.en_US
dc.language.isoen_USen_US
dc.subjectKapok seed oilen_US
dc.subjectMultiwalled carbon nanotubesen_US
dc.subjectNanofluiden_US
dc.subjectThermal conductivityen_US
dc.titleThermal Conductivity of Multiwalled Carbon Nanotubes-Kapok Seed Oil-Based Nanofluiden_US
dc.typeArticleen_US
dc.identifier.doi10.1002/ceat.201900600en_US
dc.identifier.journalCHEMICAL ENGINEERING & TECHNOLOGYen_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department機械工程學系zh_TW
dc.contributor.departmentDepartment of Mechanical Engineeringen_US
dc.identifier.wosnumberWOS:000539601100001en_US
dc.citation.woscount0en_US
Appears in Collections:Articles