Analysis of Heat Transfer Characteristics of MHD Ferrofluid by the Implicit Finite Difference Method at Temperature-Dependent Viscosity Along a Vertical Thin Cylinder

Islam, Md. Mahadul; Hasan, Md Farhad; Molla, Md. Mamun

doi:10.1007/s40997-023-00656-8

Cited by 6 publications

(2 citation statements)

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“…Some of the mathematical parameters need to be defined. For example, ρ n f is the effective density, the heat capacity is (ρC p ) n f , and the expansion of the thermal coefficient of the nanofluid is (ρβ) n f , which is provided in the following ways, as described in [17,33]:…”

Section: Properties Of Nanofluidsmentioning

confidence: 99%

“…Thermosolutal natural convection, heat, and mass transfer must be observed. Magnetohydrodynamics (MHD) is the study of fluid flow and electrically conducting fluid in a heat transfer process and is a strong candidate to serve as the controller [17]. A strong magnetic field works against the heat transfer process and could be used as a restriction if a heat transfer process needs to be stable to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

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Multiple-Relaxation-Time Lattice Boltzmann Simulation of Soret and Dufour Effects on the Thermosolutal Natural Convection of a Nanofluid in a U-Shaped Porous Enclosure

Islam,

Hasan,

Molla

2023

Energies

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This article reports an investigation of the Soret and Dufour effects on the double-diffusive natural convection of Al2O3-H2O nanofluids in a U-shaped porous enclosure. Numerical problems were resolved using the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM). The indented part of the U-shape was cold, and the right and left walls were heated, while the bottom and upper walls were adiabatic. The experimental data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The benchmark results thoroughly validate the graphics process unit (GPU) based in-house compute unified device architecture (CUDA) C/C++ code. Numeral simulations were performed for a variety of dimensionless variables, including the Rayleigh number, (Ra = 104,105,106), the Darcy number, (Da = 10−2,10−3,10−4), the Soret number, (Sr = 0.0,0.1,0.2), the Dufour number, (Df = 0.0,0.1,0.2), the buoyancy ratio, (−2≤Br≤2), the Lewis number, (Le = 1,3,5), the volume fraction, (0≤ϕ≤0.04), and the porosity, ϵ = (0.2−0.8), and the Prandtl number, Pr = 6.2 (water) is fixed to represent the base fluid. The numerical results are presented in terms of streamlines, isotherms, isoconcentrations, temperature, velocity, mean Nusselt number, mean Sherwood number, entropy generation, and statistical analysis using a response surface methodology (RSM). The investigation found that fluid mobility was enhanced as the Ra number and buoyancy force increased. The isoconcentrations and isotherm density close to the heated wall increased when the buoyancy force shifted from a negative magnitude to a positive one. The local Nu increased as the Rayleigh number increased but reduced as the volume fraction augmented. Furthermore, the mean Nu (Nu¯) decreased by 3.12% and 6.81% and the Sh¯ increased by 83.17% and 117.91% with rising Lewis number for (Ra=105 and Da=10−3) and (Ra=106 and Da=10−4), respectively. Finally, the Br and Sr demonstrated positive sensitivity, and the Ra and ϕ showed negative sensitivity only for higher values of ϕ based on the RSM.

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Section: Properties Of Nanofluidsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%