Using artificial neural network to optimize the flow and natural heat transfer of a magnetic nanofluid in a square enclosure with a fin on its vertical wall: a lattice Boltzmann simulation

Chen, Haiji; Saleem, S.; Ghaderi, Mohammad Reza

doi:10.1007/s10973-021-10767-6

Cited by 4 publications

(1 citation statement)

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“…In a variety of heat transfer applications, nanofluids have numerous properties that make them potentially useful, such as machining, pharmaceutical procedures, fuel cells, vehicle cooling/thermal control, domestic refrigerators, hybridpowered engines, chillers, microelectronics, lowering boiler flue gas temperature, and heat exchangers [29]. Zubaidi et al [30][31] has a great contribution regarding heat transfer applications of nanofluids. Jou and Tzeng [32] quantitatively investigated the vital convection enhancement of a twodimensional nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of the Optimal Auxiliary Function Method and Numerical Method to Explore the Heat Transfer between Two Parallel Porous Plates of Steady Nanofluids with Brownian and Thermophoretic Influences

Ullah

Khan

Fiza

et al. 2022

Mathematical Problems in Engineering

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In this study, we used the newly established optimal approach, namely, optimal auxilary function method (OAFM) along with the Adams numerical solver technique in order to investigate the heat transfer between two permeable parallel plates of steady nanofluids (HTBTP-SNFs) through Brownian and thermophoretic consequences. The new scheme model (HTBTP-SNFs) in terms of partial differential equations (PDEs) is changed to nonlinear ordinary differential equations (ODEs) by utilizing similarity transformations. The OAFM and Adams numerical methods are used to solve the resulting ODEs with boundary conditions. The OAFM along with convergence and Adams numerical method are studied in detail. The influences of the physical parameters of HTBTP-SNFs model for instance porosity parameter (m), parameter of magnetic (M), parameter of Brownian (Nb), viscosity parameter (R), Schmidt number (Sc), thermophores parameter (Nt), and Prandlt number (Pr) are discussed with the help of tabular data and graphs. The reliability and effectiveness of the technique are achieved by equating the results available in the literature.

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Section: Introductionmentioning

confidence: 99%