NDLI: Development of a Heat Transfer Correlation for the Transitional Flow in a Horizontal Tube Using Support Vector Machines

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Tam, L. M. Ghajar, A. J. Tam, H. K. Tam, S. C.
Copyright Year	2008
Abstract	In this paper the Support Vector Machines (SVM) method is used to correlate the transitional forced and mixed convection experimental data of Ghajar and Tam (1994) that were obtained along a stainless steel horizontal circular tube fitted with re-entrant, square-edged, and bell-mouth inlets under uniform wall heat flux boundary condition. The SVM method has been chosen to further improve the accuracy of the correlations that were developed by Ghajar and his co-workers using the traditional least-squares method (Ghajar and Tam, 1994) and more recently the artificial neural networks (ANN) method (Ghajar et al., 2004). Using the ANN method improved the accuracy of their correlation. However, there are drawbacks associated with ANN method. One of the major problems with the ANN method is that it does not provide a unique correlation due to different coefficient matrices. The SVM method used in this study eliminated the drawbacks associated with the ANN method and provided a unique correlation with comparable accuracy as the ANN method. For the experimental data used, majority of the data points were predicted within 5% deviation. Comparisons were made regarding the accuracy of the developed correlation and its characteristic using SVM and ANN methods. The results showed that SVM is a good method to correlate complex heat transfer data.
Sponsorship	Heat Transfer Division
Starting Page	527
Ending Page	536
Page Count	10
File Format	PDF
ISBN	9780791848470
DOI	10.1115/HT2008-56093
e-ISBN	0791838323
Volume Number	Heat Transfer: Volume 1
Conference Proceedings	ASME 2008 Heat Transfer Summer Conference collocated with the Fluids Engineering, Energy Sustainability, and 3rd Energy Nanotechnology Conferences
Language	English
Publisher Date	2008-08-10
Publisher Place	Jacksonville, Florida, USA
Access Restriction	Subscribed
Subject Keyword	Support vector machines Stainless steel Artificial neural networks Boundary-value problems Mixed convection Flow (dynamics) Heat transfer Heat flux
Content Type	Text
Resource Type	Article

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