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RESEARCH PAPERS

A Generalized Neural Network Model of Refrigerant Mass Flow Through Adiabatic Capillary Tubes and Short Tube Orifices

[+] Author and Article Information
Ling-Xiao Zhao, Liang-Liang Shao

Institute of Refrigeration and Cryogenics, Shanghai Jiaotong University, Shanghai 200240, China

Chun-Lu Zhang1

China R&D Center, Carrier Corporation, No. 3239 Shen Jiang Road, Pudong, Shanghai 201206, Chinachunlu.Zhang@carrier.utc.com

Liang Yang

China R&D Center, Carrier Corporation, No. 3239 Shen Jiang Road, Pudong, Shanghai 201206, China

1

Corresponding author.

J. Fluids Eng 129(12), 1559-1564 (May 29, 2007) (6 pages) doi:10.1115/1.2801352 History: Received November 19, 2006; Revised May 29, 2007

Adiabatic capillary tubes and short tube orifices are widely used as expansive devices in refrigeration, residential air conditioners, and heat pumps. In this paper, a generalized neural network has been developed to predict the mass flow rate through adiabatic capillary tubes and short tube orifices. The input/output parameters of the neural network are dimensionless and derived from the homogeneous equilibrium flow model. Three-layer backpropagation (BP) neural network is selected as a universal function approximator. Log sigmoid and pure linear transfer functions are used in the hidden layer and the output layer, respectively. The experimental data of R12, R22, R134a, R404A, R407C, R410A, and R600a from the open literature covering capillary and short tube geometries, subcooled and two-phase inlet conditions, are collected for the BP network training and testing. Compared with experimental data, the overall average and standard deviations of the proposed neural network are 0.75% and 8.27% of the measured mass flow rates, respectively.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Architecture of three-layer perceptron network

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Figure 2

Comparison of predicted and measured mass flow rates through adiabatic capillary tubes

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Figure 3

Comparison of predicted and measured mass flow rates through short tube orifices

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Figure 4

Comparison of predicted and measured mass flow rates under SC inlet conditions

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Figure 5

Comparison of predicted and measured mass flow rates under TP inlet conditions

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Figure 6

Comparison of predicted and measured mass flow rates (36) through adiabatic capillary tubes

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Figure 7

Comparison of predicted and measured mass flow rates (34) through short tube orifices

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Figure 8

Mass flow rate change with inlet conditions

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Figure 9

Mass flow rate change with length-to-diameter ratio

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