https://doi.org/10.1140/epjp/s13360-022-03518-5
Regular Article
General correlations for hydrothermal and hydrodynamic features of a nanofluid affected by FHD: a GMDH-type neural network
1
Faculty of Engineering, Islamshahr Branch, Islamic Azad University, Tehran, Iran
2
Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran
a
Alirezamohammadi137329@yahoo.com
Received:
20
February
2022
Accepted:
19
November
2022
Published online:
6
December
2022
The enhancement of the micro-sized heat pipes efficiency is now a critical matter. The optimized micropipes should have both high thermal performance and low-pressure drop characteristics. Employing the external magnetic field can be a novel means to achieve this purpose. In this paper, a numerical analysis has been conducted to investigate the effects of various parameters such as intensity of the magnetic field (Mn), wire distance (a), the concentration of ferrofluid (φ), and Reynolds number (Re) on forced heat convection of a ferrofluid inside a mini pipe with a constant heat flux at low Reynolds numbers. The governing equations are solved based on finite volume methods, via SIMPLEC algorithm. The results depict that the presence of a magnetic field remarkably increases the Nusselt number and the pressure drop inside the pipe by about 351% and 48%, respectively. The outcomes reveal that the presence of a magnetic field can boost the efficiency of the system at lower Reynolds and higher magnetic numbers, 3.99 times more than non-magnetic cases. In addition, the group method of data handling neural network is used to produce correlations of the Nusselt number and pressure drop with the concerned variations. Finally, the Pareto optimization method is employed to extract the optimum case. The result of Pareto shows that the optimized case of this study occurs when the Mn, Re, and φ are 9.4763E07, 366.72, and 0.037, respectively, where the Nusselt number is 23.98 and the non-dimensional pressure drop equals 19.88.
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