Estimation of Heat Transfer and Pressure Drop in an In-Line Flat Tubes Bundle by Radial Basis Function Network (RBFN)
This paper presents how to predict the heat transfer and pressure drop for in-line flat tubes configuration in a crossflow using an artificial neural networks (ANNs). The numerical study of a 2-D steady state and incompressible laminar flow in a tube configuration is also considered in this study....
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/5600/1/Tahseen_MUCET2013.pdf http://umpir.ump.edu.my/id/eprint/5600/ |
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| Summary: | This paper presents how to predict the heat transfer and pressure drop for in-line flat tubes configuration in a
crossflow using an artificial neural networks (ANNs). The numerical study of a 2-D steady state and incompressible laminar flow in a tube configuration is also considered in this study. A finite volume technique and body-fitted coordinate system used to solve the Navier-Stokes and energy equations. The Reynolds number based on hydraulic diameter varies from 10 to 320. Heat transfer coefficient and pressure drop results are presented for tube configurations at three transverse pitches are 2.5, 3.0 and 4.5 with two longitudinal pitches are 3.0 and 6.0. The predicted results for average Nusselt number and dimensionless pressure show a good agreement with available previous work. The accuracy between actual values and ANNs approach model results was obtained with a mean relative error less than 4.10% for average Nusselt number and less than 4.8% for pressure drop. |
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