Effect of the magnetic field on the heat transfer coefficient of a Fe3O4-water ferrofluid using artificial intelligence and CFD simulation

  • Ali Khosravi
  • Mohammad MalekanEmail author
Regular Article


A ferrofluid is a magnetic fluid which is composed of magnetic nanoparticles with the size of 5-15nm immersed in a base fluid (such as water, oil, etc.). Although the amount of thermal conductivity of the magnetic nanoparticles is lower than that of metallic and metallic oxide nanoparticles, their constructability by magnetic field makes them ideal to be used in heat transfer applications. In this study, the heat transfer coefficient (HTC) of the Fe3O4 nanoparticles dispersed in water under constant and alternating magnetic field is investigated by artificial intelligence methods and CFD simulation. Multilayer feed-forward neural network, group method of data handling type neural network, support vector regression model and adaptive neuro-fuzzy inference system are developed to predict the HTC of the Fe3O4-water ferrofluid under magnetic field. Volume fraction of nanoparticle, intensity of the magnetic field, frequency of the magnetic field, Reynolds number and dimensionless distance of the tube are selected as input variables of the networks and the HTC is selected as output variable of the network. The results show that artificial intelligence methods can successfully predict the target with very good accuracy.


  1. 1.
    A. Khosravi, R.N.N. Koury, L. Machado, Int. J. Refrig. 86, 463 (2018)CrossRefGoogle Scholar
  2. 2.
    H.R. Goshayeshi, A. Khosravi, M.A. Karizaki, Adv. Mater. Res. 856, 98 (2014)CrossRefGoogle Scholar
  3. 3.
    N. Sandeep, Adv. Powder Technol. 28, 2288 (2017)CrossRefGoogle Scholar
  4. 4.
    P. Naphon, S. Wiriyasart, T. Arisariyawong, T. Nualboonrueng, Int. J. Heat Mass Transfer 110, 739 (2017)CrossRefGoogle Scholar
  5. 5.
    M. Bahiraei, S.M. Majd, Adv. Powder Technol. 27, 673 (2016)CrossRefGoogle Scholar
  6. 6.
    S. Ahangar Zonouzi et al., Exp. Therm. Fluid Sci. 91, 155 (2018)CrossRefGoogle Scholar
  7. 7.
    R. Azizian, E. Doroodchi, T. McKrell, J. Buongiorno, L.W. Hu, B. Moghtaderi, Int. J. Heat Mass Transfer 68, 94 (2014)CrossRefGoogle Scholar
  8. 8.
    L. Sha, Y. Ju, H. Zhang, J. Wang, Appl. Therm. Eng. 113, 566 (2017)CrossRefGoogle Scholar
  9. 9.
    A. Ghofrani, M. Dibaei, A.H. Sima, M. Shafii, Exp. Therm. Fluid Sci. 49, 193 (2013)CrossRefGoogle Scholar
  10. 10.
    L. Syam Sundar, M.T. Naik, K.V. Sharma, M.K. Singh, T.C. Siva Reddy, Exp. Therm. Fluid Sci. 37, 65 (2012)CrossRefGoogle Scholar
  11. 11.
    M. Sheikhbahai, M. Nasr Esfahany, N. Etesami, Int. J. Therm. Sci. 62, 149 (2012)CrossRefGoogle Scholar
  12. 12.
    F. Asadzadeh, M. Nasr Esfahany, N. Etesami, Int. J. Therm. Sci. 62, 114 (2012)CrossRefGoogle Scholar
  13. 13.
    M.A. Hakeem, M. Kamil, Appl. Therm. Eng. 112, 1057 (2017)CrossRefGoogle Scholar
  14. 14.
    Z. Tian, B. Gu, L. Yang, F. Liu, Appl. Therm. Eng. 63, 459 (2014)CrossRefGoogle Scholar
  15. 15.
    V. Vapnik, The Nature of Statistical Learning Theory (Springer Science & Business Media, 2013)Google Scholar
  16. 16.
    H.Y. Cheng, C.C. Yu, S.J. Lin, Energy 70, 121 (2014)CrossRefGoogle Scholar
  17. 17.
    Y. Yaslan, B. Bican, Measurement 103, 52 (2017)CrossRefGoogle Scholar
  18. 18.
    J. Antonanzas, R. Urraca, F.J. Martinez-De-Pison, F. Antonanzas-Torres, Energy Convers. Manag. 100, 380 (2015)CrossRefGoogle Scholar
  19. 19.
    K. Mohammadi, S. Shamshirband, M.H. Anisi, K. Amjad Alam, D. Petković, Energy Convers. Manag. 91, 433 (2015)CrossRefGoogle Scholar
  20. 20.
    Y. Oğuz, I. Güney, Turk. J. Electr. Eng. Comput. Sci. 18, 625 (2010)Google Scholar
  21. 21.
    A. Ivakhnenko, IEEE Trans. Syst. Man Cybern. 1, 364 (1971)CrossRefGoogle Scholar
  22. 22.
    M.H. Ahmadi, M.A. Ahmadi, M. Mehrpooya, M.A. Rosen, Sustainability 7, 2243 (2015)CrossRefGoogle Scholar
  23. 23.
    I. Ebtehaj, H. Bonakdari, A.H. Zaji, H. Azimi, F. Khoshbin, Eng. Sci. Technol. 18, 746 (2015)Google Scholar
  24. 24.
    M. Sheikholeslami, F. Bani Sheykholeslami, S. Khoshhal, H. Mola-Abasia, D.D. Ganji, H.B. Rokni, Neural Comput. Appl. 25, 171 (2014)CrossRefGoogle Scholar
  25. 25.
    S.V. Mousavi, M. Sheikholeslami, M. Gorji bandpy, M. Barzegar Gerdroodbary, Chem. Eng. Res. Des. 113, 112 (2016)CrossRefGoogle Scholar
  26. 26.
    M. Goharkhah, M. Ashjaee, M. Shahabadi, Int. J. Therm. Sci. 99, 113 (2016)CrossRefGoogle Scholar
  27. 27.
    M. Goharkhah, A. Salarian, M. Ashjaee, M. Shahabadi, Powder Technol. 274, 258 (2015)CrossRefGoogle Scholar
  28. 28.
    M. Lajvardi, J. Moghimi-Rad, A.G.I. Hadi, T.D. Isfahani, F. Zabihi, J. Sabbaghzadeh, J. Magn. & Magn. Mater. 322, 3508 (2010)ADSCrossRefGoogle Scholar
  29. 29.
    L. Sha, Y. Ju, H. Zhang, Appl. Therm. Eng. 126, 108 (2017)CrossRefGoogle Scholar
  30. 30.
    E. Esmaeili, R. Ghazanfar Chaydareh, S.A. Rounaghi, Appl. Therm. Eng. 110, 1212 (2017)CrossRefGoogle Scholar
  31. 31.
    N. Hatami, A. Kazemnejad Banari, A. Malekzadeh, A.R. Pouranfard, Phys. Lett. A 381, 510 (2017)ADSCrossRefGoogle Scholar
  32. 32.
    S.M. Besarati, P.D. Myers, D.C. Covey, A. Jamali, Cogent Eng. 2, 1056929 (2015)CrossRefGoogle Scholar
  33. 33.
    C. Cortes, V. Vapnik, Mach. Learn. 20, 273 (1995)Google Scholar
  34. 34.
    Vladimir N. Vapnik, Statistical Learning Theory, 1st ed. (Wiley-Interscience, NY, 1998)Google Scholar
  35. 35.
    A. Khosravi, R.N.N. Koury, L. Machado, J.J.G. Pabon, J. Clean. Prod. 176, 63 (2018)CrossRefGoogle Scholar
  36. 36.
    K. Cheng, Z. Lu, Y. Wei, Y. Shi, Y. Zhou, Mech. Syst. Signal Process. 96, 201 (2017)ADSCrossRefGoogle Scholar
  37. 37.
    A.J. Smola, B. Schölkopf, Stat. Comput. 14, 199 (2004)MathSciNetCrossRefGoogle Scholar
  38. 38.
    F. Antonanzas-Torres, R. Urraca, J. Antonanzas, J. Fernandez-Ceniceros, F.J. Martinez-De-Pison, Energy Convers. Manag. 96, 277 (2015)CrossRefGoogle Scholar
  39. 39.
    L. Jinyang, M. Xiaofeng, Eng. Appl. Artif. Intell. 26, 1237 (2013)CrossRefGoogle Scholar
  40. 40.
    Y. Tan, A. Van Cauwenberghe, Eng. Appl. Artif. Intell. 12, 21 (1999)CrossRefGoogle Scholar
  41. 41.
    I. Ceylan, E. Gedik, O. Erkaymaz, A.E. Gürel, Energy Build. 84, 258 (2014)CrossRefGoogle Scholar
  42. 42.
    V.H. Quej, J. Almorox, J.A. Arnaldo, L. Saito, J. Atmos. Sol.-Terrestr. Phys. 155, 62 (2017)ADSCrossRefGoogle Scholar
  43. 43.
    E. Ikonen, K. Najim, U. Kortela, Eng. Appl. Artif. Intell. 13, 705 (2000)CrossRefGoogle Scholar
  44. 44.
    A. Khosravi, R.N.N. Koury, L. Machado, J.J.G. Pabon, Sustain. Energy Technol. Assess. 25, 146 (2018)Google Scholar

Copyright information

© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate Program in Mechanical EngineeringFederal University of Minas Gerais (UFMG)Belo HorizonteBrazil
  2. 2.Department of Bioengineering, Heart Institute (InCor), Medical SchoolUniversity of São PauloSão PauloBrazil

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