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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/18011
Title: Performance prediction of adiabatic capillary tubes by conventional and ann approaches: A comparison
Authors: Khan M.K.
Kumar R.
Sahoo, Pradeep K.
Published in: Proceedings of ASHRAE Transactions
Abstract: 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 massjlow 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 ±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. © 2009 ASHRAE.
Citation: Proceedings of ASHRAE Transactions, (2009), 93- 105. Chicago, IL
URI: http://repository.iitr.ac.in/handle/123456789/18011
Issue Date: 2009
Keywords: Adiabatic capillary tube
Artificial neural network models
Empirical correlations
Experimental studies
Feed-Forward
Flow characteristic
HFC-134A
Inlet subcooling
Input parameter
Mass flow rate
Performance prediction
Semi-empirical correlation
Backpropagation
Backpropagation algorithms
Capillary tubes
Flow rate
Heating
Mass transfer
Nanofluidics
Neural networks
Pipe flow
Pressure effects
Pressure measurement
Refrigerants
Regression analysis
Tubes (components)
ISSN: 12505
Author Scopus IDs: 22834936000
55389796000
22835953900
Author Affiliations: Khan, M.K., Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna, India
Kumar, R., Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, India
Sahoo, P.K., Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, India
Corresponding Author: Khan, M. K.; Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna, India
Appears in Collections:Conference Publications [ME]

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