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Research Papers

Prediction of Two-Phase Heat Transfer Coefficients in a Horizontal Pipe for Different Inclined Positions With Artificial Neural Networks

[+] Author and Article Information
Najmeh Sobhanifar

Young Researchers and Elite Club,
Shoushtar Branch,
Islamic Azad University,
Shoushtar 64517-41117, Iran
e-mail: sobhanifar.najme@yahoo.com

Ebrahim Ahmadloo

Young Researchers and Elite Club,
Darab Branch,
Islamic Azad University,
Darab 74817-83143, Iran
e-mail: ebrahimahmadloo@yahoo.com

Sadreddin Azizi

Department of Chemical Engineering,
Yasouj University,
Yasouj 75918-74831, Iran
e-mail: sadraazizii@yahoo.com

1Corresponding author.

Manuscript received April 24, 2014; final manuscript received October 6, 2014; published online March 17, 2015. Assoc. Editor: Giulio Lorenzini.

J. Heat Transfer 137(6), 061009 (Jun 01, 2015) (6 pages) Paper No: HT-14-1236; doi: 10.1115/1.4029865 History: Received April 24, 2014; Revised October 06, 2014; Online March 17, 2015

This paper presents the application of artificial neural network (ANN) in prediction of heat transfer coefficients (HTCs) of two-phase flow of air–water in a pipe in the horizontal and slightly upward inclined (2, 5, and 7 deg) positions. For this purpose, the superficial liquid and gas Reynolds numbers and the inclination of the pipe were used as input parameters, while the HTCs of two-phase flow were used as output parameters in training and testing of the multilayered, feedforward, backpropagation neural networks. In this present study, experimental data were taken from literature and then used for the ANN model. The superficial liquid and gas Reynolds numbers ranged from 740 to 26,100 and 560 to 47,600 for water and air, respectively. The mean deviations against experimental data were determined for the model. Results showed that the network predictions were in very good agreement with the experimental HTC data, whereas the correlation showed more deviations. Finally, results showed that the accuracy between the neural network predictions and experimental data was achieved with mean relative error (MRE) of 2.92% and correlation coefficient (R) that was 0.997 for all datasets, which suggests the reliability of the ANNs as a strong tool for predicting HTCs with two-phase flows.

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Figures

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Fig. 1

Network configuration with 3-15-1 neurons for heat transfer prediction

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Fig. 2

Determination of the number of optimum neurons

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Fig. 3

Reduction in MSE during the training process for the network with 3-15-1 configuration

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Fig. 4

Comparison between the experimental data and the neural network predictions for all dataset

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