Generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes using artificial neural network

International Journal of Refrigeration

Volumn 28, Issue 4, 2005, Pages 506-514

  Zhang, Chun Lu ^a,b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Carrier Corporation   (China)

Author keywords

Capillary; Flow; Modelling; Neural network; R12; R134a; R22; R290; R407C; R410A; R600a; Refrigerant; Tube

Indexed keywords

DATA REDUCTION; FLOW OF FLUIDS; FUNCTIONS; MATHEMATICAL MODELS; NETWORKS (CIRCUITS); NEURAL NETWORKS; SURFACE TREATMENT;

CAPILLARY; R12; R134A; R22; R290; R407C; R410A; R600A; TUBE;

REFRIGERANTS;

EID: 18144380345     PISSN: 01407007     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijrefrig.2004.11.004     Document Type: Article

References (39)

1. ASHRAE ASHRAE handbook - refrigeration 2002 American Society of Heating, Refrigerating and Air-Conditioning Engineers Atlanta Chapter 45
2. R.Y. Li, S. Lin, and Z.H. Chen Numerical modeling of thermodynamic non-equilibrium flow of refrigerant through capillary tubes ASHRAE Trans 96 1 1990 542 549
3. S.J. Kuehl, and V.W. Goldschmidt Modeling of steady flows of R-22 through capillary tubes ASHRAE Trans 97 1 1991 139 148
4. F. Escanes, C.D. Perez-Segarra, and A. Oliva Numerical simulation of capillary-tube expansion devices Int J Refrig 18 2 1995 113 122
5. R.R. Bittle, and M.B. Pate A theoretical model for predicting adiabatic capillary tube performance with alternative refrigerants ASHRAE Trans 102 2 1996 52 64
6. O. Garcia-Valladares, C.D. Perez-Segarra, and A. Oliva Numerical simulation of capillary tube expansion devices behavior with pure and mixed refrigerants considering metastable region. Part I: mathematical formulation and numerical model Appl Therm Eng 22 2 2002 173 182
7. C.L. Zhang, and G.L. Ding Modified general equation for the design of capillary tubes ASME J Fluids Eng 123 4 2001 914 919
8. C.L. Zhang, and G.L. Ding Approximate analytic solutions of adiabatic capillary tube Int J Refrig 27 1 2004 17 24
9. H. Wijaya Adiabatic capillary tube test data for HFC-134a Proceedings of the IIR-Purdue Refrigeration Conference, West Lafayette, Ind. vol. 1 1992 p. 63-71
10. F.A.S. Fiorelli, A.A.S. Huerta, and O.M. Silvares Experimental analysis of refrigerant mixtures flow through adiabatic capillary tubes Exp Therm Fluid Sci 26 2002 499 512
11. F.A.S. Fiorelli, and O.M. Silvares Refrigerant mixtures flow through capillary tubes: a comparison between homogeneous and separated-flow models HVAC&R Res 9 1 2003 33 53
12. S.F. Yana Motta, J.A.R. Parise, and S.L. Braga A visual study of R-404A/oil flow through adiabatic capillary tubes Int J Refrig 25 2002 586 596
13. M. Fukuta, T. Yanagisawa, T. Arai, and Y. Ogi Influences of miscible and immiscible oils on flow characteristics through capillary tube - part I: experimental study Int J Refrig 26 2003 823 829
14. O. Garcia-Valladares Review of numerical simulation of capillary tube using refrigerant mixtures Appl Therm Eng 24 2004 949 966
15. P.K. Bansal, and A.S. Rupasinghe An empirical model for sizing capillary tubes Int J Refrig 19 8 1996 497 505
16. D. Jung, C. Park, and B. Park Capillary tube selection for HCFC22 alternatives Int J Refrig 22 8 1999 604 614
17. S.L. Chen, C.H. Liu, C.S. Cheng, and C.S. Jwo Simulation of refrigerants flowing through adiabatic capillary tubes HVAC&R Res 6 2 2000 101 115
18. V. Trisaksri, and S. Wongwises Correlations for sizing adiabatic capillary tubes Int J Energy Res 27 2003 1145 1164
19. R.R. Bittle, D.A. Wolf, and M.B. Pate A generalized performance prediction method for adiabatic capillary tubes HVAC&R Res 4 1 1998 27 43
20. C. Melo, R.T.S. Ferreira, C.B. Neto, J.M. Goncalves, and M.M. Mezavila An experimental analysis of adiabatic capillary tubes Appl Therm Eng 19 6 1999 669 684
21. C.Z. Wei, Y.T. Lin, C.C. Wang, and J.S. Leu Experimental study of the performance of capillary tubes for R-407C refrigerant ASHRAE Trans 105 2 1999 634 638
22. S.G. Kim, M.S. Kim, and S.T. Ro Experimental investigation of the performance of R22, R407C and R410A in several capillary tubes for air-conditioners Int J Refrig 25 2002 521 531
23. J. Choi, Y. Kim, and H.Y. Kim A generalized correlation for refrigerant mass flow rate through adiabatic capillary tubes Int J Refrig 26 7 2003 881 888
24. J. Choi, Y. Kim, and J.T. Chung An empirical correlation and rating charts for the performance of adiabatic capillary tubes with alternative refrigerants Appl Therm Eng 24 1 2004 29 41
25. K. Hornik, M. Stinchcombe, and H. White Multilayer feedforward networks are universal approximators Neural Networks 2 1989 359 366
26. M.T. Hagan, H.B. Demuth, and M. Beale Neural network design 1996 PWS Publishing Company New York
27. S.A. Kalogirou Applications of artificial neural-networks for energy systems Appl Energy 67 2000 17 35
28. D.J. Swider, M.W. Browne, P.K. Bansal, and V. Kecman Modeling of vapor-compression liquid chillers with neural networks Appl Therm Eng 21 2001 311 329
29. H. Bechtler, M.W. Browne, P.K. Bansal, and V. Kecman New approach to dynamic modeling of vapor-compression liquid chillers: artificial neural networks Appl Therm Eng 21 2001 941 953
30. E. Arcaklioglu, A. Erisen, and R. Yilmaz Artificial neural network analysis of heat pumps using refrigerant mixtures Energy Conversion Mgmt 45 2004 1917 1929
31. G. Diaz, M. Sen, K.T. Yang, and R.L. McClain Simulation of heat exchanger performance by artificial neural networks HVAC&R Res 5 3 1999 195 208
32. A. Pacheco-Vega, M. Sen, K.T. Yang, and R.L. McClain Neural network analysis of in-tube refrigerating heat exchanger with limited experimental data Int J Heat Mass Transfer 44 2001 763 770
33. A. Pacheco-Vega, G. Diaz, M. Sen, K.T. Yang, and R.L. McClain Heat rate predictions in humid air-water heat exchangers using correlations and neural networks ASME J Heat Transfer 123 2001 348 354
34. C.L. Zhang, G.L. Ding, and H. Li Thermodynamic calculation of refrigeration compressors with modified artificial neural network method Chin J Mech Eng 37 1 2001 75 77 (in Chinese)
35. A. Chouai, S. Laugier, and D. Richon Modeling of thermodynamic properties using neural networks: application to refrigerants Fluid Phase Equilib 199 1-2 2002 53 62
36. G. Scalabrin, and G. Cristofoli The viscosity surfaces of R152a in the form of multilayer feed forward neural networks Int J Refrig 26 3 2003 302 314
37. X.H. Yu, and G.A. Chen Efficient back-propagation learning using optimal learning rate and momentum Neural Networks 10 3 1997 517 527
38. M.O. McLinden, S.A. Klein, E.W. Lemmon, A.P. Peskin. NIST thermodynamic and transport properties of refrigerants and refrigerant mixtures (REFPROP), Version 6.01. Gaithersburg, MD: National Institute of Standards and Technology; 1998.
39. S.W. Churchill Frictional equation spans all fluid flow regions Chem Eng 84 24 1977 91 92