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Factor analysis for forced and mixed convection laminar heat transfer in a horizontal tube using artificial neural network
Tam L.M.1; Tam H.K.1; Ghajar A.J.2; Tam S.C.1
2007-12-01
Conference Name7th ASME/JSME Thermal Engineering and Summer Heat Transfer Conference
Source Publication2007 Proceedings of the ASME/JSME Thermal Engineering Summer Heat Transfer Conference - HT 2007
Volume1
Pages1013-1020
Conference DateJUL 08-12, 2007
Conference PlaceVancouver, CANADA
Abstract

Artificial neural network (ANN) has shown its superior predictive power compared to the conventional approaches in many studies. However, it has always been treated as a "black box" because it provides little explanation on the relative influence of the independent variables in the prediction process. In our previous work (Tam et al., 2006), an index of contribution extracted from the ANN correlation was primarily introduced to analyze the relative importance of the associated independent variables on our forced convective turbulent heat transfer data in a horizontal tube (Ghajar and Tam, 1994). The most and the least important variables were determined quantitatively and found to be thoroughly conforming to the empirical correlation and physical phenomena. In this study, we have extended the method to a more complicated data set, forced and mixed convection developing laminar flow in a horizontal tube with uniform wall heat flux. The parameters influencing the Nusselt number for this data set were Reynolds number, Grashof number, Prandtl number, the length-to-diameter ratio, and the bulk-to-wall viscosity ratio. Due to the complexity of the problem it is difficult to determine the influence of the individual independent variables. According to literature, for laminar heat transfer involving entrance and mixed convection effects, Rayleigh number and Graetz number are both important. Through the re-arrangement of those variables, the factor analysis clearly showed that the Rayleigh number has a significant influence on the mixed convection heat transfer data and the forced convection heat transfer data is more influenced by the Graetz number. The results clearly indicate that the factor analysis method can be used to provide an insight into the influence of different variables or a combination of them on complicated heat transfer problems. 

DOI10.1115/HT2007-32155
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Mechanics
WOS SubjectEngineering, Mechanical ; Mechanics
WOS IDWOS:000254029000129
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
DEPARTMENT OF MATHEMATICS
Affiliation1.University of Macau, Taipa, Macau, China
2.Oklahoma State University - Stillwater
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Tam L.M.,Tam H.K.,Ghajar A.J.,et al. Factor analysis for forced and mixed convection laminar heat transfer in a horizontal tube using artificial neural network[C],2007:1013-1020.
APA Tam L.M.,Tam H.K.,Ghajar A.J.,&Tam S.C..(2007).Factor analysis for forced and mixed convection laminar heat transfer in a horizontal tube using artificial neural network.2007 Proceedings of the ASME/JSME Thermal Engineering Summer Heat Transfer Conference - HT 2007,1,1013-1020.
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