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Heat Transfer Correlation for Two-Phase Flow in Vertical Pipes Using Artificial Neural Network

[+] Author Affiliations
A. J. Ghajar

Oklahoma State University, Stillwater, OK

L. M. Tam, H. K. Tam

University of Macau, Taipa, Macau, China

Paper No. IMECE2003-41823, pp. 101-106; 6 pages
doi:10.1115/IMECE2003-41823
From:
  • ASME 2003 International Mechanical Engineering Congress and Exposition
  • Heat Transfer, Volume 4
  • Washington, DC, USA, November 15–21, 2003
  • Conference Sponsors: Heat Transfer Division
  • ISBN: 0-7918-3718-1 | eISBN: 0-7918-4663-6, 0-7918-4664-4, 0-7918-4665-2
  • Copyright © 2003 by ASME

abstract

In many industrial applications, such as the flow of natural gas and oil in flowlines and wellbores, the knowledge of nonboiling two-phase, two-component (liquid and permanent gas) heat transfer is required. Several heat transfer correlations for forced convective heat transfer during gas-liquid two-phase flow in vertical pipes have been published over the past 40 years. These correlations were developed based on limited experimental data and are only applicable to certain flow patterns and fluid combinations. Kim et al. (2000) proposed a heat transfer correlation for turbulent gas-liquid flow in vertical pipes with different flow patterns and fluid combinations. Their correlation was developed using four sets of experimental data (a total of 255 data points) for vertical pipes. The form of their correlation was based on the major nondimensional parameters affecting two-phase heat transfer. The coefficients of their correlation were found by using the traditional least squares regression. Their correlation predicted the experimental data with a deviation range of −64.71% and 39.55% Majority of the experimental data (245 data points or 96% of the data) were predicted within the ±30% range. The purpose of this study is to apply the method of artificial neural network (ANN) to develop a more accurate correlation. It has been shown that ANN has excellent capability of handling complicated flows. The same sets of experimental data used by Kim et al. (2000) were used in this study. The ANN method employed in this study was a three-layer feedforward network, which is a high dimensional nonlinear regression. To avoid over or underfitting, the data were separated into two sets. One set was used for the network training and the other set was used for testing. The new correlation outperforms the traditional least squares correlation and predicts the experimental data within the ±15% range. Since the ANN correlation is like a black box, the knowledge extraction from ANN correlation is also discussed in this study.

Copyright © 2003 by ASME

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