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Investigation of boiling heat transfer coefficients of different refrigerants for low fin, Turbo-B and Thermoexcel-E enhanced tubes using computational smart schemes

  • Afsaneh Mehralizadeh
  • Seyed Reza ShabanianEmail author
  • Gholamreza Bakeri
Article
  • 11 Downloads

Abstract

The design and manufacture of highly efficient evaporators and heat exchangers in cooling machinery need an accurate estimation of the boiling heat transfer coefficient of the refrigerants. In the present study, the boiling heat transfer coefficients of different refrigerants were predicted using machine learning methods and were compared to the existing empirical correlations. For this purpose, four models of ANN, ANFIS, ELM and SVM were developed by using MATLAB functions. 320 data were collected on the boiling heat transfer coefficient of the refrigerants in the plain and enhanced (low fin, Turbo-B, Thermo excel-E) tubes. The percent deviation from the actual value was between − 2.05 and 1.36% for the ANN, − 4.97 and 8.72% for the ANFIS, − 38.11 and 83.01% for the ELM and − 17.35 and 78.37% for the SVM. The results show that the proposed ANN and ANFIS models are reliable models for predicting the boiling heat transfer coefficient. They have a better performance than the ELM and SVM models. The values of RMSE, AARD and R2 for the best model were 74 W m−2 K−1, 0.399% and 0.99993 for the ANN, 306 W m−2 K−1, 1.117% and 0.99883 for the ANFIS, 2163 W m−2 K−1, 15.539% and 0.94191 for the ELM and 2212 W m−2 K−1, 14.905% and 0.93921 for the SVM. The intelligent algorithms of ANN and ANFIS have more accurate predictions than empirical correlations.

Keywords

Boiling heat transfer coefficient Refrigerant Soft computing approaches 

Notes

Acknowledgements

The authors acknowledge the funding support of Babol Noshirvani University of Technology through Grant Program No. BNUT/388003/97. The authors would also like to thank the National Iranian Oil Engineering & Construction Co. for their financial support of this project.

References

  1. 1.
    Kamel MS, Lezsovits F, Hussein AK. Experimental studies of flow boiling heat transfer by using nanofluids. J Therm Anal Calorim. 2019;15:17.  https://doi.org/10.1007/s10973-019-08333-2.CrossRefGoogle Scholar
  2. 2.
    Vafaei S, Borca-Tasciuc T. Role of nanoparticles on nanofluid boiling phenomenon: nanoparticle deposition. Chem Eng Res Des. 2014;92(5):842–56.CrossRefGoogle Scholar
  3. 3.
    Darvish K, Ehyaei M, Atabi F, Rosen M. Selection of optimum working fluid for organic Rankine cycles by exergy and exergy-economic analyses. Sustainability. 2015;7(11):15362–83.CrossRefGoogle Scholar
  4. 4.
    Kim J. Review of nucleate pool boiling bubble heat transfer mechanisms. Int J Multiph Flow. 2009;35(12):1067–76.CrossRefGoogle Scholar
  5. 5.
    Thome JR. Boiling of new refrigerants: a state-of-the-art review. Int J Refrig. 1996;19(7):435–57.CrossRefGoogle Scholar
  6. 6.
    Jung D, Kim Y, Ko Y, Song K. Nucleate boiling heat transfer coefficients of pure halogenated refrigerants. Int J Refrig. 2003;26(2):240–8.CrossRefGoogle Scholar
  7. 7.
    Jung D, Lee H, Bae D, Oho S. Nucleate boiling heat transfer coefficients of flammable refrigerants. Int J Refrig. 2004;27(4):409–14.CrossRefGoogle Scholar
  8. 8.
    Jung D, An K, Park J. Nucleate boiling heat transfer coefficients of HCFC22, HFC134a, HFC125 and HFC32 on various enhanced tubes. Int J Refrig. 2004;27(2):202–6.CrossRefGoogle Scholar
  9. 9.
    Jung D, Lee H, Bae D, Ha J. Nucleate boiling heat transfer coefficients of flammable refrigerants on various enhanced tubes. Int J Refrig. 2005;28(3):451–5.CrossRefGoogle Scholar
  10. 10.
    Del Col D. Flow boiling of halogenated refrigerants at high saturation temperature in a horizontal smooth tube. Exp Thermal Fluid Sci. 2010;34(2):234–45.CrossRefGoogle Scholar
  11. 11.
    Liu Z, Winterton R. A general correlation for saturated and subcooled flow boiling in tubes and annuli, based on a nucleate pool boiling equation. Int J Heat Mass Transf. 1991;34(11):2759–66.CrossRefGoogle Scholar
  12. 12.
    Wojtan L, Ursenbacher T, Thome JR. Investigation of flow boiling in horizontal tubes: part II—Development of a new heat transfer model for stratified-wavy, dryout and mist flow regimes. Int J Heat Mass Transf. 2005;48(14):2970–85.CrossRefGoogle Scholar
  13. 13.
    Hu H, Ding G, Wei W, Huang X, Wang Z. Heat transfer characteristics of refrigerant-oil mixtures flow boiling in a horizontal C-shape curved smooth tube. Int J Refrig. 2010;33(5):932–43.CrossRefGoogle Scholar
  14. 14.
    Oh J-T, Pamitran AS, Choi K-I, Hrnjak P. Experimental investigation on two-phase flow boiling heat transfer of five refrigerants in horizontal small tubes of 0.5, 1.5 and 30 mm inner diameters. Int J Heat Mass Transfer. 2011;54(9–10):2080–8.CrossRefGoogle Scholar
  15. 15.
    Zhu Y, Hu H, Sun S, Ding G. Heat transfer measurements and correlation of refrigerant flow boiling in tube filled with copper foam. Int J Refrig. 2014;38:215–26.CrossRefGoogle Scholar
  16. 16.
    Kundu A, Kumar R, Gupta A. Comparative experimental study on flow boiling heat transfer characteristics of pure and mixed refrigerants. Int J Refrig. 2014;45:136–47.CrossRefGoogle Scholar
  17. 17.
    Mancin S, Diani A, Rossetto L. Experimental measurements of R134a flow boiling inside a 34-mm ID microfin tube. Heat Transfer Eng. 2015;36(14–15):1218–29.CrossRefGoogle Scholar
  18. 18.
    Ji W-T, Zhao C-Y, Zhang D-C, Yoshioka S, He Y-L, Tao W-Q. Effect of vapor flow on the falling film evaporation of R134a outside a horizontal tube bundle. Int J Heat Mass Transf. 2016;92:1171–81.CrossRefGoogle Scholar
  19. 19.
    Yang C-Y, Nalbandian H, Lin F-C. Flow boiling heat transfer and pressure drop of refrigerants HFO-1234yf and HFC-134a in small circular tube. Int J Heat Mass Transf. 2018;121:726–35.CrossRefGoogle Scholar
  20. 20.
    Jige D, Sagawa K, Inoue N. Effect of tube diameter on boiling heat transfer and flow characteristic of refrigerant R32 in horizontal small-diameter tubes. Int J Refrig. 2017;76:206–18.CrossRefGoogle Scholar
  21. 21.
    Jiang G, Tan J, Nian Q, Tang S, Tao W. Experimental study of boiling heat transfer in smooth/micro-fin tubes of four refrigerants. Int J Heat Mass Transf. 2016;98:631–42.CrossRefGoogle Scholar
  22. 22.
    He G, Liu F, Cai D, Jiang J. Experimental investigation on flow boiling heat transfer performance of a new near azeotropic refrigerant mixture R290/R32 in horizontal tubes. Int J Heat Mass Transf. 2016;102:561–73.CrossRefGoogle Scholar
  23. 23.
    Dang C, Jia L, Xu M, Huang Q, Peng Q. Experimental study on flow boiling characteristics of pure refrigerant (R134a) and zeotropic mixture (R407C) in a rectangular micro-channel. Int J Heat Mass Transf. 2017;104:351–61.CrossRefGoogle Scholar
  24. 24.
    Chouai A, Laugier S, Richon D. Modeling of thermodynamic properties using neural networks: application to refrigerants. Fluid Phase Equilib. 2002;199(1–2):53–62.CrossRefGoogle Scholar
  25. 25.
    Granryd E. Hydrocarbons as refrigerants—an overview. Int J Refrig. 2001;24(1):15–24.CrossRefGoogle Scholar
  26. 26.
    Scalabrin G, Condosta M, Marchi P. Modeling flow boiling heat transfer of pure fluids through artificial neural networks. Int J Therm Sci. 2006;45(7):643–63.CrossRefGoogle Scholar
  27. 27.
    Li M, Dang C, Hihara E. Flow boiling heat transfer of HFO1234yf and HFC32 refrigerant mixtures in a smooth horizontal tube: part II. Prediction method. Int J Heat Mass Transfer. 2013;64:591–608.CrossRefGoogle Scholar
  28. 28.
    Balcilar M, Aroonrat K, Dalkilic A, Wongwises S. A numerical correlation development study for the determination of Nusselt numbers during boiling and condensation of R134a inside smooth and corrugated tubes. Int Commun Heat Mass Transfer. 2013;48:141–8.CrossRefGoogle Scholar
  29. 29.
    Balcilar M, Dalkilic A, Suriyawong A, Yiamsawas T, Wongwises S. Investigation of pool boiling of nanofluids using artificial neural networks and correlation development techniques. Int Commun Heat Mass Transfer. 2012;39(3):424–31.CrossRefGoogle Scholar
  30. 30.
    Mehendale S. A new heat transfer coefficient correlation for pure refrigerants and near-azeotropic refrigerant mixtures flow boiling within horizontal microfin tubes. Int J Refrig. 2018;86:292–311.CrossRefGoogle Scholar
  31. 31.
    Tang W, Li W. A new heat transfer model for flow boiling of refrigerants in micro-fin tubes. Int J Heat Mass Transf. 2018;126:1067–78.CrossRefGoogle Scholar
  32. 32.
    Hassanpour M, Vaferi B, Masoumi ME. Estimation of pool boiling heat transfer coefficient of alumina water-based nanofluids by various artificial intelligence (AI) approaches. Appl Therm Eng. 2018;128:1208–22.CrossRefGoogle Scholar
  33. 33.
    Zendehboudi A, Tatar A. Utilization of the RBF network to model the nucleate pool boiling heat transfer properties of refrigerant-oil mixtures with nanoparticles. J Mol Liq. 2017;247:304–12.CrossRefGoogle Scholar
  34. 34.
    Belman-Flores J, Mota-Babiloni A, Ledesma S, Makhnatch P. Using ANNs to approach to the energy performance for a small refrigeration system working with R134a and two alternative lower GWP mixtures. Appl Therm Eng. 2017;127:996–1004.CrossRefGoogle Scholar
  35. 35.
    Barroso-Maldonado J, Belman-Flores J, Ledesma S, Aceves S. Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks. Cryogenics. 2018;92:60–70.CrossRefGoogle Scholar
  36. 36.
    Azizi S, Ahmadloo E. Prediction of heat transfer coefficient during condensation of R134a in inclined tubes using artificial neural network. Appl Therm Eng. 2016;106:203–10.CrossRefGoogle Scholar
  37. 37.
    Alamolhoda S, Kazemeini M, Zaherian A, Zakerinasab M. Reaction kinetics determination and neural networks modeling of methanol dehydration over nano γ-Al2O3 catalyst. J Ind Eng Chem. 2012;18(6):2059–68.CrossRefGoogle Scholar
  38. 38.
    Huang C, Bensoussan A, Edesess M, Tsui KL. Improvement in artificial neural network-based estimation of grid connected photovoltaic power output. Renew Energy. 2016;97:838–48.CrossRefGoogle Scholar
  39. 39.
    Zendehboudi A, Tatar A, Li X. A comparative study and prediction of the liquid desiccant dehumidifiers using intelligent models. Renew Energy. 2017;114:1023–35.CrossRefGoogle Scholar
  40. 40.
    Istadi I, Amin NAS. Modelling and optimization of catalytic–dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network—genetic algorithm technique. Chem Eng Sci. 2007;62(23):6568–81.CrossRefGoogle Scholar
  41. 41.
    Lahiri S, Ghanta K. Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines. Chem Eng Sci. 2008;63(6):1497–509.CrossRefGoogle Scholar
  42. 42.
    Abdollahzadeh G, Shabanian SM. Experimental and numerical analysis of beam to column joints in steel structures. Front Struct Civ Eng. 2018;12(4):642–61.CrossRefGoogle Scholar
  43. 43.
    Amiri A, Karami A, Yousefi T, Zanjani M. Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders. Pol J Chem Technol. 2012;14(4):46–52.CrossRefGoogle Scholar
  44. 44.
    Shabanian SR, Lashgari S, Hatami T. Application of intelligent methods for the prediction and optimization of thermal characteristics in a tube equipped with perforated twisted tape. Numer Heat Transfer Part A Appl. 2016;70(1):30–47.CrossRefGoogle Scholar
  45. 45.
    Abdolahzadeh G, Shabanian SM, Tavakol A. Experimental and numerical evaluation of rigid column to baseplate connection under cyclic loading. Struct Des Tall Spec Build. 2019;28(6):e1596.CrossRefGoogle Scholar
  46. 46.
    Gill J, Singh J, Ohunakin OS, Adelekan DS. ANN approach for irreversibility analysis of vapor compression refrigeration system using R134a/LPG blend as replacement of R134a. J Therm Anal Calorim. 2019;135(4):2495–511.CrossRefGoogle Scholar
  47. 47.
    Jang J-S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993;23(3):665–85.CrossRefGoogle Scholar
  48. 48.
    Gill J, Singh J. Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system. Int J Refrig. 2017;78:166–75.CrossRefGoogle Scholar
  49. 49.
    Amid S, Gundoshmian TM. Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF) and ANFIS models. Environ Progress Sustain Energy. 2017;36(2):577–85.CrossRefGoogle Scholar
  50. 50.
    Shabanian SR, Edrisi S, Khoram FV. Prediction and optimization of hydrogen yield and energy conversion efficiency in a non-catalytic filtration combustion reactor for jet A and butanol fuels. Kor J Chem Eng. 2017;34(8):2188–97.CrossRefGoogle Scholar
  51. 51.
    Huang G-B, Zhu Q-Y, Mao K, Siew C-K, Saratchandran P, Sundararajan N. Can threshold networks be trained directly? IEEE Trans Circuits Syst II Express Briefs. 2006;53(3):187–91.CrossRefGoogle Scholar
  52. 52.
    Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: a new learning scheme of feedforward neural networks. Neural networks. 2004;2:985–90.Google Scholar
  53. 53.
    Zhu Q-X, Wang X, He Y-L, Xu Y. An improved extreme learning machine integrated with nonlinear principal components and its application to modeling complex chemical processes. Appl Therm Eng. 2018;130:745–53.CrossRefGoogle Scholar
  54. 54.
    Cortes C, Vapnik V. Support-vector networks machine learning, vol. 20. Boston, MA: Kluwer Academic Publisher; 1995. p. 237–97.Google Scholar
  55. 55.
    Shabanian SR, Abdoos AA. A hybrid soft computing approach based on feature selection for estimation of filtration combustion characteristics. Neural Comput Appl. 2018;30(12):3749–57.CrossRefGoogle Scholar
  56. 56.
    Najafi G, Ghobadian B, Moosavian A, Yusaf T, Mamat R, Kettner M, Azmi W. SVM and ANFIS for prediction of performance and exhaust emissions of a SI engine with gasoline–ethanol blended fuels. Appl Therm Eng. 2016;95:186–203.CrossRefGoogle Scholar
  57. 57.
    Musayev A, Madatova S, Rustamov S. Evaluation of the impact of the tax legislation reforms on the tax potential by fuzzy inference method. Proc Comput Sci. 2016;102:507–14.CrossRefGoogle Scholar
  58. 58.
    Guyon I. Practical feature selection: from correlation to causality Mining massive data sets for security: advances in data mining, search, social networks and text mining and their applications to security. Amsterdam: IOS Press; 2008. p. 27–43.Google Scholar
  59. 59.
    Stoppiglia H, Dreyfus G, Dubois R, Oussar Y. Ranking a random feature for variable and feature selection. J Mach Learn Res. 2003;3:1399–414.Google Scholar
  60. 60.
    Zhao B, Su Y, Tao W. Mass transfer performance of CO2 capture in rotating packed bed: dimensionless modeling and intelligent prediction. Appl Energy. 2014;136:132–42.CrossRefGoogle Scholar
  61. 61.
    Stephan K, Abdelsalam M. Heat-transfer correlations for natural convection boiling. Int J Heat Mass Transf. 1980;23(1):73–87.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Department of Chemical EngineeringBabol Noshirvani University of TechnologyBabolIran

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