Performance prediction of adiabatic capillary tubes by conventional and ANN approaches: a comparison.(artificial neural networks)(Report): An article from: ASHRAE Transactions
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ISBN / ASINB002EPNCAE
ISBN-13978B002EPNCA0
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This digital document is an article from ASHRAE Transactions, published by American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. on January 1, 2009. The length of the article is 6156 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.
From the author: An experimental study of adiabatic capillary tubes was conducted to evaluate the flow characteristics of refrigerant HFC-134a. The effect of various input parameters, such as capillary tube diameter, length, and inlet subcooling on the mass flow rate of HFC-134a, were investigated. Moreover, a comparison was made for the mass flow rate of refrigerant HFC-134a in instrumented and noninstrumented capillary tubes. It was found that the provision of taps for pressure measurement on the capillary tube surface has a negligible effect on the mass flow rate of HFC-134a. The data obtained from the experiments were analyzed, and a semi-empirical correlation using a multiple-variable regression analysis was developed. The proposed correlation predicts that more than 86% of the data lies in the error band of [+ or -]10%. Furthermore, an artificial neural network (ANN) model using a feed-forward backpropagation algorithm was developed to predict the mass flow rate from the given set of input parameters. These two approaches were compared, and ANN was found to predict the mass flow rate far more accurately than the conventional empirical correlation developed by regression.
Citation Details
Title: Performance prediction of adiabatic capillary tubes by conventional and ANN approaches: a comparison.(artificial neural networks)(Report)
Author: Mohd. Kaleem Khan
Publication:ASHRAE Transactions (Magazine/Journal)
Date: January 1, 2009
Publisher: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.
Volume: 115 Issue: 1 Page: 93(13)
Article Type: Report
Distributed by Gale, a part of Cengage Learning
From the author: An experimental study of adiabatic capillary tubes was conducted to evaluate the flow characteristics of refrigerant HFC-134a. The effect of various input parameters, such as capillary tube diameter, length, and inlet subcooling on the mass flow rate of HFC-134a, were investigated. Moreover, a comparison was made for the mass flow rate of refrigerant HFC-134a in instrumented and noninstrumented capillary tubes. It was found that the provision of taps for pressure measurement on the capillary tube surface has a negligible effect on the mass flow rate of HFC-134a. The data obtained from the experiments were analyzed, and a semi-empirical correlation using a multiple-variable regression analysis was developed. The proposed correlation predicts that more than 86% of the data lies in the error band of [+ or -]10%. Furthermore, an artificial neural network (ANN) model using a feed-forward backpropagation algorithm was developed to predict the mass flow rate from the given set of input parameters. These two approaches were compared, and ANN was found to predict the mass flow rate far more accurately than the conventional empirical correlation developed by regression.
Citation Details
Title: Performance prediction of adiabatic capillary tubes by conventional and ANN approaches: a comparison.(artificial neural networks)(Report)
Author: Mohd. Kaleem Khan
Publication:ASHRAE Transactions (Magazine/Journal)
Date: January 1, 2009
Publisher: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.
Volume: 115 Issue: 1 Page: 93(13)
Article Type: Report
Distributed by Gale, a part of Cengage Learning
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