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

Hamed Bagheri, Mohammadali Behrang, Ehsanolah Assareh, Mohsen Izadi and Mikhail A. Sheremet
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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
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

Energies, 2019, vol. 12, issue 14, 1-17

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.

Keywords: heat transfer; hybrid nanofluid; Nusselt number; artificial neural network; particles swarm algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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