Advertisement

Journal of Thermal Analysis and Calorimetry

, Volume 132, Issue 2, pp 1213–1239 | Cite as

Connectionist intelligent model estimates of convective heat transfer coefficient of nanofluids in circular cross-sectional channels

  • Alireza Baghban
  • Fathollah Pourfayaz
  • Mohammad Hossein Ahmadi
  • Alibakhsh Kasaeian
  • Seyed Mohsen Pourkiaei
  • Giulio Lorenzini
Article

Abstract

Nanofluids are kind of fluids, which have a wide range of applications in different fields such as industry or engineering systems. The present study efforts to find accurate relationships between the convective heat transfer coefficient of the nanofluids containing the silica nanoparticles as a function of Reynolds number, Prandtl number, and mass fraction nanofluid. To that end, a number of seven different models including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), least square support vector machine (LSSVM), genetic programming (GP), principal component analysis (PCA), and committee machine intelligent system (CMIS) have been implemented according to experimental databases designed for measuring the convective heat transfer coefficient of nanofluid in circular cross-sectional channels. Results indicated the satisfactory capability of suggested models, especially CMIS model in order to estimate the convective heat transfer coefficient of nanofluid. The obtained statistical analyses such as the mean square error and R-squared (R 2) for the ANFIS, ANN, SVM, LSSVM, PCA, GP, and CMIS were 380.6671 and 0.9946, 215.062 and 0.9969, 335.748 and 0.9951, 298.88 and 0.9959, 1601.336 and 0.977, 1891.861 and 0.973, and 205.366 and 0.9970 correspondingly. We expect that these suggested models can help engineers who deal with heat transfer phenomenon to have great predictive tools for estimating convective heat transfer coefficient of nanofluid.

Keywords

Convective heat transfer Reynolds number Prandtl number Intelligent models Optimization 

Notes

Acknowledgements

The authors would like to thank the reviewers for their valuable comments, which have been utilized in improving the quality of the paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

References

  1. 1.
    Khanjari Y, Pourfayaz F, Kasaeian A. Numerical investigation on using of nanofluid in a water-cooled photovoltaic thermal system. Energy Convers Manag. 2016;122:263–78.CrossRefGoogle Scholar
  2. 2.
    Amin TE, Roghayeh G, Fatemeh R, Fatollah P. Evaluation of nanoparticle shape effect on a nanofluid based flat-plate solar collector efficiency. Energy Explor Exploit. 2015;33(5):659–76.CrossRefGoogle Scholar
  3. 3.
    Lee S, Choi S-S, Li S, Eastman J. Measuring thermal conductivity of fluids containing oxide nanoparticles. J Heat Transfer. 1999;121(2):280–9.CrossRefGoogle Scholar
  4. 4.
    Das SK, Putra N, Thiesen P, Roetzel W. Temperature dependence of thermal conductivity enhancement for nanofluids. J Heat Transfer. 2003;125(4):567–74.CrossRefGoogle Scholar
  5. 5.
    Masuda H, Ebata A, Teramae K. Alteration of thermal conductivity and viscosity of liquid by dispersing ultra-fine particles: dispersion of Al2O3, SiO2 and TiO2 ultra-fine particles. Netsu Bussei. 1993;7(4):227–33.CrossRefGoogle Scholar
  6. 6.
    Oh D-W, Jain A, Eaton JK, Goodson KE, Lee JS. Thermal conductivity measurement and sedimentation detection of aluminum oxide nanofluids by using the 3ω method. Int J Heat Fluid Flow. 2008;29(5):1456–61.CrossRefGoogle Scholar
  7. 7.
    Hung T-C, Yan W-M, Wang X-D, Chang C-Y. Heat transfer enhancement in microchannel heat sinks using nanofluids. Int J Heat Mass Transf. 2012;55(9):2559–70.CrossRefGoogle Scholar
  8. 8.
    Öztop HF, Estellé P, Yan W-M, Al-Salem K, Orfi J, Mahian O. A brief review of natural convection in enclosures under localized heating with and without nanofluids. Int Commun Heat Mass Transfer. 2015;60:37–44.CrossRefGoogle Scholar
  9. 9.
    Nasiri M, Etemad SG, Bagheri R. Experimental heat transfer of nanofluid through an annular duct. Int Commun Heat Mass Transfer. 2011;38(7):958–63.CrossRefGoogle Scholar
  10. 10.
    Nasrin R, Alim M. Semi-empirical relation for forced convective analysis through a solar collector. Sol Energy. 2014;105:455–67.CrossRefGoogle Scholar
  11. 11.
    Sahin B, Gültekin GG, Manay E, Karagoz S. Experimental investigation of heat transfer and pressure drop characteristics of Al2O3–water nanofluid. Exp Thermal Fluid Sci. 2013;50:21–8.CrossRefGoogle Scholar
  12. 12.
    Saeedinia M, Akhavan-Behabadi M, Nasr M. Experimental study on heat transfer and pressure drop of nanofluid flow in a horizontal coiled wire inserted tube under constant heat flux. Exp Thermal Fluid Sci. 2012;36:158–68.CrossRefGoogle Scholar
  13. 13.
    Moghadassi A, Masoud Hosseini S, Henneke D, Elkamel A. A model of nanofluids effective thermal conductivity based on dimensionless groups. J Therm Anal Calorim. 2009;96(1):81–4.CrossRefGoogle Scholar
  14. 14.
    Barbés B, Páramo R, Blanco E, Pastoriza-Gallego MJ, Pineiro MM, Legido JL, et al. Thermal conductivity and specific heat capacity measurements of Al2O3 nanofluids. J Therm Anal Calorim. 2013;111(2):1615–25.CrossRefGoogle Scholar
  15. 15.
    Huminic G, Huminic A. Application of nanofluids in heat exchangers: a review. Renew Sustain Energy Rev. 2012;16(8):5625–38.CrossRefGoogle Scholar
  16. 16.
    Sarkar J. A critical review on convective heat transfer correlations of nanofluids. Renew Sustain Energy Rev. 2011;15(6):3271–7.CrossRefGoogle Scholar
  17. 17.
    Vajjha RS, Das DK, Kulkarni DP. Development of new correlations for convective heat transfer and friction factor in turbulent regime for nanofluids. Int J Heat Mass Transf. 2010;53(21):4607–18.CrossRefGoogle Scholar
  18. 18.
    Lu G, Wang X-D, Duan Y-Y. A critical review of dynamic wetting by complex fluids: from Newtonian fluids to non-Newtonian fluids and nanofluids. Adv Coll Interface Sci. 2016;236:43–62.CrossRefGoogle Scholar
  19. 19.
    Lu G, Duan Y-Y, Wang X-D. Surface tension, viscosity, and rheology of water-based nanofluids: a microscopic interpretation on the molecular level. J Nanopart Res. 2014;16(9):2564.CrossRefGoogle Scholar
  20. 20.
    Yang L, Du K, Zhang X. A theoretical investigation of thermal conductivity of nanofluids with particles in cylindrical shape by anisotropy analysis. Powder Technol. 2017;314:328–338.CrossRefGoogle Scholar
  21. 21.
    Valinataj-Bahnemiri P, Ramiar A, Manavi S, Mozaffari A. Heat transfer optimization of two phase modeling of nanofluid in a sinusoidal wavy channel using Artificial Bee Colony technique. Eng Sci Technol Int J. 2015;18(4):727–37.CrossRefGoogle Scholar
  22. 22.
    Islam M, Shabani B, Rosengarten G, Andrews J. The potential of using nanofluids in PEM fuel cell cooling systems: a review. Renew Sustain Energy Rev. 2015;48:523–39.CrossRefGoogle Scholar
  23. 23.
    Baghban A, Ahmadi MA, Pouladi B, Amanna B. Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique. J Supercrit Fluids. 2015;101:184–92.CrossRefGoogle Scholar
  24. 24.
    Baghban A, Ahmadi MA, Shahraki BH. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. J Supercrit Fluids. 2015;98:50–64.CrossRefGoogle Scholar
  25. 25.
    Baghban A, Bahadori M, Rozyn J, Lee M, Abbas A, Bahadori A, et al. Estimation of air dew point temperature using computational intelligence schemes. Appl Therm Eng. 2016;93:1043–52.CrossRefGoogle Scholar
  26. 26.
    Mohanraj M, Jayaraj S, Muraleedharan C. Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—a review. Renew Sustain Energy Rev. 2012;16(2):1340–58.CrossRefGoogle Scholar
  27. 27.
    Kurt H, Kayfeci M. Prediction of thermal conductivity of ethylene glycol–water solutions by using artificial neural networks. Appl Energy. 2009;86(10):2244–8.CrossRefGoogle Scholar
  28. 28.
    Durairaj M, Thamilselvan P. Applications of artificial neural network for IVF data analysis and prediction. J Eng Comput Appl Sci (JEC and AS). 2013;2(9):11–5.Google Scholar
  29. 29.
    Kalogirou SA. Applications of artificial neural-networks for energy systems. Appl Energy. 2000;67(1):17–35.CrossRefGoogle Scholar
  30. 30.
    Bhoopal RS, Sharma P, Singh R, Beniwal R. Applicability of artificial neural networks to predict effective thermal conductivity of highly porous metal foams. J Porous Media. 2013;7:585–96.CrossRefGoogle Scholar
  31. 31.
    Jang J-S, Sun C-T. Neuro-fuzzy modeling and control. Proc IEEE. 1995;83(3):378–406.CrossRefGoogle Scholar
  32. 32.
    Jang J-SR, Sun C-T. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River: Prentice-Hall Inc; 1996.Google Scholar
  33. 33.
    Buragohain M, Mahanta C. A novel approach for ANFIS modelling based on full factorial design. Appl Soft Comput. 2008;8(1):609–25.CrossRefGoogle Scholar
  34. 34.
    Ying L-C, Pan M-C. Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag. 2008;49(2):205–11.CrossRefGoogle Scholar
  35. 35.
    Ozturk A, Arslan A, Hardalac F. Comparison of neuro-fuzzy systems for classification of transcranial Doppler signals with their chaotic invariant measures. Expert Syst Appl. 2008;34(2):1044–55.CrossRefGoogle Scholar
  36. 36.
    Wang S-C. Artificial neural network. Interdisciplinary computing in java programming. Berlin: Springer; 2003. p. 81–100.CrossRefGoogle Scholar
  37. 37.
    Hagan MT, Demuth HB, Beale MH. Neural network design. Boston: PWS Publisher; 1996.Google Scholar
  38. 38.
    Harrington PdB. Sigmoid transfer functions in backpropagation neural networks. Anal Chem. 1993;65(15):2167–8.CrossRefGoogle Scholar
  39. 39.
    Buntine WL, Weigend AS. Bayesian back-propagation. Complex Syst. 1991;5(6):603–43.Google Scholar
  40. 40.
    Chauvin Y, Rumelhart DE. Backpropagation: theory, architectures, and applications. Hove: Psychology Press; 1995.Google Scholar
  41. 41.
    Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293–300.CrossRefGoogle Scholar
  42. 42.
    Steinwart I, Christmann A. Support vector machines. Berlin: Springer; 2008.Google Scholar
  43. 43.
    Suykens JA, Vandewalle J. Recurrent least squares support vector machines. IEEE Trans Circuits Syst I Fundam Theory Appl. 2000;47(7):1109–14.CrossRefGoogle Scholar
  44. 44.
    Bair E, Hastie T, Paul D, Tibshirani R. Prediction by supervised principal components. J Am Stat Assoc. 2006;101(473):119–137.CrossRefGoogle Scholar
  45. 45.
    Björck Ȧ, Pereyra V. Solution of Vandermonde systems of equations. Math Comput. 1970;24(112):893–903.CrossRefGoogle Scholar
  46. 46.
    Macon N, Spitzbart A. Inverses of Vandermonde matrices. Am Math Mon. 1958;65(2):95–100.CrossRefGoogle Scholar
  47. 47.
    Banzhaf W, Nordin P, Keller RE, Francone FD. Genetic programming: an introduction. San Francisco: Morgan Kaufmann Publishers; 1998.CrossRefGoogle Scholar
  48. 48.
    Poli R, Koza J. Genetic programming. Search methodologies. Berlin: Springer; 2014. p. 143–85.CrossRefGoogle Scholar
  49. 49.
    Nilsson NJ. Learning machines: foundations of trainable pattern-classifying systems. New York City: McGraw-Hill; 1965.Google Scholar
  50. 50.
    Can M. Committee machine networks to diagnose cardiovascular diseases. SouthEast Eur J Soft Comput. 2013;2(1):76-83.Google Scholar
  51. 51.
    Genest C, Zidek JV. Combining probability distributions: a critique and an annotated bibliography. Stat Sci. 1986;1(1):114–135.CrossRefGoogle Scholar
  52. 52.
    Xu L, Krzyzak A, Suen CY. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern. 1992;22(3):418–35.CrossRefGoogle Scholar
  53. 53.
    Hashem S, Schmeiser B. Approximating a function and its derivatives using MSE-optimal linear combinations of trained feedforward neural networks. CiteseerX. 1993.Google Scholar
  54. 54.
    Pourfayaz F, Sanjarian N, Kasaeian A, Razi Astaraie F, Sameti M, Nasirivatan S. An experimental comparison of SiO2/water nanofluid heat transfer in square and circular cross-section channels. J Therm Anal Calorim. 2017.  https://doi.org/10.1007/s10973-017-6500-4.Google Scholar
  55. 55.
    Levenberg K. A method for the solution of certain non-linear problems in least squares. Quart Appl Math. 1944;2:164–8.CrossRefGoogle Scholar
  56. 56.
    Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math. 1963;11(2):431–41.CrossRefGoogle Scholar
  57. 57.
    Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. Hoboken: Wiley; 2005.Google Scholar
  58. 58.
    Hosseinzadeh M, Hemmati-Sarapardeh A. Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids. J Mol Liq. 2014;200:340–8.CrossRefGoogle Scholar
  59. 59.
    Mohammadi AH, Gharagheizi F, Eslamimanesh A, Richon D. Evaluation of experimental data for wax and diamondoids solubility in gaseous systems. Chem Eng Sci. 2012;81:1–7.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  • Alireza Baghban
    • 1
  • Fathollah Pourfayaz
    • 2
  • Mohammad Hossein Ahmadi
    • 3
  • Alibakhsh Kasaeian
    • 2
  • Seyed Mohsen Pourkiaei
    • 2
  • Giulio Lorenzini
    • 4
  1. 1.Department of Chemical Engineering, Faculty of EngineeringUniversity of TehranTehranIran
  2. 2.Department of Renewable Energies, Faculty of New Sciences and TechnologiesUniversity of TehranTehranIran
  3. 3.Faculty of Mechanical EngineeringShahrood University of TechnologyShahroodIran
  4. 4.Dipartimento di Ingegneria e ArchitetturaUniversità degli Studi di ParmaParmaItaly

Personalised recommendations