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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran

    Alireza Baghban

  2. Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

    Fathollah Pourfayaz, Alibakhsh Kasaeian & Seyed Mohsen Pourkiaei

  3. Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohammad Hossein Ahmadi

  4. Dipartimento di Ingegneria e Architettura, Università degli Studi di Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy

    Giulio Lorenzini

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  1. Alireza Baghban
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  2. Fathollah Pourfayaz
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  5. Seyed Mohsen Pourkiaei
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Correspondence to Fathollah Pourfayaz or Mohammad Hossein Ahmadi.

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Baghban, A., Pourfayaz, F., Ahmadi, M.H. et al. Connectionist intelligent model estimates of convective heat transfer coefficient of nanofluids in circular cross-sectional channels. J Therm Anal Calorim 132, 1213–1239 (2018). https://doi.org/10.1007/s10973-017-6886-z

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