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Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach

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Abstract

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system. Various experiments were conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The results showed that mass flow rate through helical coil capillary tube was found lower than straight capillary tube by about 5–16%. Dimensionless correlation and Artificial Neural Network (ANN) models were developed to predict mass flow rate. It was found that dimensionless correlation and ANN model predictions agreed well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 and 0.275 and mean absolute percentage error of 4.75% and 2.31% respectively. The results suggested that ANN model shows better statistical prediction than dimensionless correlation model.

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References

  1. Bansal PK, Rupasinghe AS (1996) An empirical model for sizing capillary tubes. Int J Refrig 19(8):497–505

    Article  Google Scholar 

  2. Jung D, Park C, Park B (1999) Capillary tube selection for HCFC22 alternatives. Int J Refrig 22(8):604–614

    Article  Google Scholar 

  3. Chen SL, Liu CH, Cheng CS, Jwo CS (2000) Simulation of refrigerants flowing through adiabatic capillary tubes. HVAC R Res 6(2):101–115

    Article  Google Scholar 

  4. Trisaksri V, Wongwises S (2003) Correlations for sizing adiabatic capillary tubes. Int J Energy Res 27:1145–1164

    Article  Google Scholar 

  5. Choi J, Kim Y, Kim HY (2003) A generalized correlation for refrigerant mass flow rate through adiabatic capillary tubes. Int J Refrig 26(8):881–888

    Article  Google Scholar 

  6. Jabaraj DB, Kathirvel AV, Lal DM (2006) Flow characteristics of HFC407C/HC600a/HC290 refrigerant mixture in adiabatic capillary tubes. Appl Therm Eng 26:1621–1628

    Article  Google Scholar 

  7. Park C, Lee S, Kang H, Kim Y (2007) Experimentation and modeling of refrigerant flow through coiled capillary tube. Int J Refrig 30:1168–1175

    Article  Google Scholar 

  8. Punia SS, Singh J (2015) An experimental study of the flow of LPG as refrigerant inside an adiabatic helical coiled capillary tube in vapour compression refrigeration system. Heat Mass Transf 51(11):1571–1577

    Article  Google Scholar 

  9. Javidmand P, Hoffmann KA (2015) Numerical-based non-dimensional analysis of critical flow of R-12, R-22, and R-134a through horizontal capillary tubes. Int J Refrig 58:58–68

    Article  Google Scholar 

  10. Dan Z, Guoliang D, Xu Y (2017) Development of correlation for mass flow rate through varying diameter capillary tube. CIESC J 68(1):57–62

    Google Scholar 

  11. Gill J, Singh J (2017) Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system. Int J Refrig 78:166–175

    Article  Google Scholar 

  12. Solomon S (ed) (2007) Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge University Press, Cambridge

  13. Sekhar SJ, Lal DM (2005) HFC134a/HC600a/HC290 mixture a retrofit for CFC12 systems. Int J Refrig 28:735–743

    Article  Google Scholar 

  14. Mohanraj M, Muraleedharan C, Jayaraj S (2011) A review of recent developments in new refrigerant mixtures for vapor compression based refrigeration, air conditioning and heat pump units. Int J Energy Res 35(8):647–669

    Article  Google Scholar 

  15. Akash BA, Said SA (2003) Assessment of LPG as a possible alternative to R-12 in a domestic refrigerator. Energy Convers Manage 44:381–388

    Article  Google Scholar 

  16. Fatouh M, El KM (2006) Experimental evaluation of a domestic refrigerator working with LPG. Appl Therm Eng 26:1593–1603

    Article  Google Scholar 

  17. Ahamed JU, Saidur R, Masjuki HH, Sattar MA (2012) Energy and thermodynamic performance of LPG as an alternative refrigerant to R-134a in a domestic refrigerator. Energy Sci Res 29(1):597–610

    Google Scholar 

  18. Srinivas P, Chandra RP, Kumar MR, Reddy N (2014) Experimental investigation of LPG as refrigerant in a domestic refrigerator. J Mech Eng Res Technol 2(1):470–476

    Google Scholar 

  19. Taiwo Babarinde OS, Ohunakin DS, Adelekan SA, Oyedepo AS (2015) Experimental study of LPG and R134a refrigerants in vapor compression refrigeration. Int J Energy Clean Environ 16(1–4):71–80

    Article  Google Scholar 

  20. Adelekan DS, Ohunakin OS, Babarinde TO, Odunfa MK, Leramo RO, Oyedepo SO, Badejo DC (2017) Experimental performance of LPG refrigerant charges with varied concentration of TiO2 nano-lubricants in a domestic refrigerator. Case Stud Therm Eng 9:55–61

    Article  Google Scholar 

  21. Mohamed EM (2015) Energy and exergy analysis of LPG (liquefied petroleum gas) as a drop in replacement for R134a in domestic refrigerators. Energy 86:344–353

    Article  Google Scholar 

  22. Yang Z, Liu B, Zhao H (2004) Experimental study of the inert effect of R134a and R227ea on explosion limits of the flammable refrigerants. Exp Thermal Fluid Sci 28:557–563

    Article  Google Scholar 

  23. Avinash P, Jabaraj DB, Lal DM (2005) Beyond for air conditioners – an outlook. IRHACE J 17:20–23

    Google Scholar 

  24. Tashtoush B, Tahat M, Shudeifat MA (2002) Experimental study of new refrigerant mixtures to replace R12 in domestic refrigerators. Appl Therm Eng 22:495–506

    Article  Google Scholar 

  25. Gill J, Singh J (2017) Energetic and Exergetic performance analysis of the vapor compression refrigeration system using adaptive neuro-fuzzy inference system approach. Exp Thermal Fluid Sci 88:246–260

  26. Zhang C-L (2005) Generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes using the artificial neural network. Int J Refrig 28(4):506–514

    Article  MathSciNet  Google Scholar 

  27. Zhang CL, Zhao LX (2007) Model-based neural network correlation for refrigerant mass flow rates through adiabatic capillary tubes. Int J Refrig 30:690–698

    Article  Google Scholar 

  28. Islamoglu Y, Kurt A, Parmaksizoglu C (2005) Performance prediction for nonadiabatic capillary tube suction line heat exchanger: an artificial neural network approach. Energy Convers Manag 46:223–232

    Article  Google Scholar 

  29. Vins V, Vacek V (2009) Mass flow rate correlation for two-phase flow of R218 through a capillary tube. Appl Therm Eng 29(14):2816–2823

    Article  Google Scholar 

  30. Shao L-L, Wang J-C, Jin X-C, Zhang C-L (2013) Assessment of existing dimensionless correlations of refrigerant flow through adiabatic capillary tubes. Int J Refrig 36(1):270–278

    Article  Google Scholar 

  31. Cao X, Li ZY, Shao LL, Zhang CL (2016) Refrigerant flows through electronic expansion valve: experiment and neural network modeling. Appl Therm Eng 92:210–218

    Article  Google Scholar 

  32. International Standard Organization (ISO) (1991) International Standard-8187, household refrigerating appliances (refrigerators/freezers) characteristics and test methods. International Organization for Standardization, Geneva

    Google Scholar 

  33. Elmolla ES, Chaudhuri M, Eltoukhy MM (2010) The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. J Hazard Mater 179:127–134

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, IKGPTU, Kapurthala, Punjab, India

    Jatinder Gill

  2. Faculty of Mechanical Engineering Department, BCET, Gurdaspur, Punjab, India

    Jagdev Singh

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  1. Jatinder Gill
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  2. Jagdev Singh
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Correspondence to Jatinder Gill.

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Gill, J., Singh, J. Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach. Heat Mass Transfer 54, 1975–1987 (2018). https://doi.org/10.1007/s00231-018-2301-y

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  • DOI: https://doi.org/10.1007/s00231-018-2301-y

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