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


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.


Convective heat transfer Reynolds number Prandtl number Intelligent models Optimization 



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.


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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

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