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Free Convection of Hybrid Nanofluids in a C-Shaped Chamber under Variable Heat Flux and Magnetic Field: Simulation, Sensitivity Analysis, and Artificial Neural Networks

Author

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  • Hamed Bagheri

    (Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful 61424-20890, Iran)

  • Mohammadali Behrang

    (Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful 61424-20890, Iran
    Farab Power Generation & Operation Management Co., Tehran 15946-59914, Iran)

  • Ehsanolah Assareh

    (Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful 61424-20890, Iran)

  • Mohsen Izadi

    (Mechanical Engineering Department, Faculty of Engineering, Lorestan University, Khorramabad 68151-44316, Iran)

  • Mikhail A. Sheremet

    (Laboratory on Convective Heat and Mass Transfer, Tomsk State University, Tomsk 634050, Russia)

Abstract

In the present investigation, the free convection energy transport was studied in a C-shaped tilted chamber with the inclination angle α that was filled with the MWCNT (MultiWall Carbon Nanotubes)-Fe 3 O 4 -H 2 O hybrid nanofluid and it is affected by the magnetic field and thermal flux. The control equations were numerically resolved by the finite element method (FEM). Then, using the artificial neural network (ANN) combined with the particles swarm optimization algorithm (PSO), the Nusselt number was predicted, followed by investigating the effect of parameters including the Rayleigh number ( Ra ), the Hartmann number ( Ha ), the nanoparticles concentration ( φ ), the inclination angle of the chamber (α), and the aspect ratio ( AR ) on the heat transfer rate. The results showed the high accuracy of the ANN optimized by the PSO algorithm in the prediction of the Nusselt number such that the mean squared error in the ANN model is 0.35, while in the ANN model, it was optimized using the PSO algorithm (ANN-PSO) is 0.22, suggesting the higher accuracy of the latter. It was also found that, among the studied parameters with an effect on the heat transfer rate, the Rayleigh number and aspect ratio have the greatest impact on the thermal transmission intensification. The obtained data also showed that a growth of the Hartmann number illustrates a reduction of the Nusselt number for high Rayleigh numbers and the heat transfer rate is almost constant for low Rayleigh number values.

Suggested Citation

  • Hamed Bagheri & Mohammadali Behrang & Ehsanolah Assareh & Mohsen Izadi & Mikhail A. Sheremet, 2019. "Free Convection of Hybrid Nanofluids in a C-Shaped Chamber under Variable Heat Flux and Magnetic Field: Simulation, Sensitivity Analysis, and Artificial Neural Networks," Energies, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2807-:d:250427
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    References listed on IDEAS

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    1. Sajid, Muhammad Usman & Ali, Hafiz Muhammad, 2019. "Recent advances in application of nanofluids in heat transfer devices: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 556-592.
    2. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    3. Ranga Babu, J.A. & Kumar, K. Kiran & Srinivasa Rao, S., 2017. "State-of-art review on hybrid nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 551-565.
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    3. Der-Fa Chen & Yi-Cheng Shih & Shih-Cheng Li & Chin-Tung Chen & Jung-Chu Ting, 2020. "Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO," Energies, MDPI, vol. 13(11), pages 1-25, June.

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