Please use this identifier to cite or link to this item: http://ir.swu.ac.th/jspui/handle/123456789/12791
Title: Artificial neural network analysis the pulsating Nusselt number and friction factor of TiO2/water nanofluids in the spirally coiled tube with magnetic field
Authors: Naphon P.
Wiriyasart S.
Arisariyawong T.
Keywords: Backpropagation
Friction
Heat transfer
Magnetic fields
Magnetic levitation vehicles
Magnetism
Nanofluidics
Neural networks
Nusselt number
Titanium dioxide
Bayesian regulation
Heat transfer and pressure drop
Levenberg Marquardt backpropagation
Nanofluids
Resilient backpropagation
Scaled conjugate gradients
Thermal Performance
Training algorithms
Backpropagation algorithms
Issue Date: 2018
Abstract: The application of artificial neural network to analyze the pulsating nanofluids heat transfer and pressure drop in the spirally coiled tube with magnetic field are presented. Four different training algorithms of Levenberg-Marquardt Backwardpropagation (LMB), Scaled Conjugate Gradient Backpropagation (SCGB), Bayesian Regulation Backpropagation (BRB), and Resilient Backpropagation (RB) are applied to adjust errors for obtaining the optimal ANN model. The results obtained from the artificial neural network are compared those from the present experiment. It is found that the Levenberg- Marquardt Backpropagation algorithm gives the minimum MSE, and maximum R as compared with other training algorithms. Based on the optimal ANN model, the majority of the data falls within ±2.5%, ±5% of the Nusselt number and friction factor, respectively. The obtained optimal ANN has been applied to predict the thermal performance of the spirally coiled tube with magnetic field. © 2017 Elsevier Ltd
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034738832&doi=10.1016%2fj.ijheatmasstransfer.2017.11.091&partnerID=40&md5=ed9e0faa6c6ba24f6947bb027b1261ff
http://ir.swu.ac.th/jspui/handle/123456789/12791
ISSN: 179310
Appears in Collections:Scopus 1983-2021

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