Please use this identifier to cite or link to this item: http://ir.swu.ac.th/jspui/handle/123456789/12112
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dc.contributor.authorNaphon P.
dc.contributor.authorArisariyawong T.
dc.contributor.authorWiriyasart S.
dc.contributor.authorSrichat A.
dc.date.accessioned2021-04-05T03:01:57Z-
dc.date.available2021-04-05T03:01:57Z-
dc.date.issued2020
dc.identifier.issn2214157X
dc.identifier.other2-s2.0-85083505813
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85083505813&doi=10.1016%2fj.csite.2020.100605&partnerID=40&md5=1d2d92b883575b7ae0bd7091b55042e1
dc.identifier.urihttp://ir.swu.ac.th/jspui/handle/123456789/12112-
dc.description.abstractApplication of adaptive neuro-fuzzy inference system to analyze friction factor and the Nusselt number of pulsating nanofluids in the helically corrugated tube with magnetic field effect is presented. Based on the optimum adaptive neuro-fuzzy inference system (ANFIS) model configuration, it has four input parameters; pulsating flow frequency, nanofluids concentration, mass flow rate and power input. The present experimental data are divided into two subsets for training and testing processes of ANFIS network. ANFIS tunes a fuzzy inference system with back-propagation algorithm and least square estimation approaches to determine the friction factor and the Nusselt number. The predicted results of the proposed ANFIS model are compared with the measured data. There is an excellent agreement between the predicted results and the experimental results and gives average errors of ±2.5%-±5.0%, ±2.5% for the friction factor and Nusselt number, respectively. The ANFIS model is an alternative powerful and reliable method as compared with other methods. It can be used with confidence for predicting thermal performance of the complex thermal systems. © 2020 The Authors.
dc.subjectBackpropagation
dc.subjectFriction
dc.subjectFuzzy neural networks
dc.subjectFuzzy systems
dc.subjectInference engines
dc.subjectMagnetic fields
dc.subjectNanofluidics
dc.subjectNusselt number
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectComplex thermal system
dc.subjectFuzzy inference systems
dc.subjectLeast square estimation
dc.subjectModel configuration
dc.subjectReliable methods
dc.subjectThermal Performance
dc.subjectTraining and testing
dc.subjectFuzzy inference
dc.titleANFIS for analysis friction factor and Nusselt number of pulsating nanofluids flow in the fluted tube under magnetic field
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationCase Studies in Thermal Engineering. Vol 18, (2020)
dc.identifier.doi10.1016/j.csite.2020.100605
Appears in Collections:Scopus 1983-2021

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