Numerical investigation on natural convection of Al2O3/water nanofluid with variable properties in an annular enclosure under magnetic field

Berrahil, Farid; Filali, Abdelkader; Abid, C.; Benissaad, Smail; Bessaïh, Rachid; Matar, Omar

doi:10.1016/j.icheatmasstransfer.2021.105408

Cited by 24 publications

(8 citation statements)

References 49 publications

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“…Recently, Aly et al 17 investigated the effect of magnetic field on the finned cavity, including a rotating rectangle and reported that mean rates of heat and mass transfer decreased by increasing the Hartmann number. Berrahil et al 18 applied the magnetic field on the Al 2 O 3 /water, natural convection in an annular enclosure and found that the rate of the average Nu number, decrement caused by the magnetic field, is greater as the radius ratio λ decreases. Mourad et al 19 used the uniform magnetic field for the thermal performance of a wavy cavity filled by Fe 3 O 4 -MWCNT hybrid nanofluid and confirmed that Nu improves by Darcy and porosity number, while it reduced by Ha number.…”

Section: Introductionmentioning

confidence: 99%

MHD mixed convection and entropy generation of CNT-water nanofluid in a wavy lid-driven porous enclosure at different boundary conditions

Hamzah

Ali

Hatami

2022

Sci Rep

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In this study, Galerkin Finite Element Method or GFEM is used for the modeling of mixed convection with the entropy generation in wavy lid-driven porous enclosure filled by the CNT-water nanofluid under the magnetic field. Two different cases of boundary conditions for hot and cold walls are considered to study the fluid flow (streamlines) and heat transfer (local and average Nusselt numbers) as well as the entropy generation parameters. Richardson (Ri), Darcy (Da), Hartmann angle (γ), Amplitude (A), Number of peaks (N), Volume fraction (φ), Heat generation factor (λ), Hartmann number (Ha) and Reynolds number (Re) are studied parameters in this study which results indicated that at low Richardson numbers (< 1) increasing the inclined angle of magnetic field, decreases the Nu numbers, but at larger Richardson numbers (> 1) it improves the Nu numbers.

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Section: Introductionmentioning

confidence: 99%

MHD mixed convection and entropy generation of CNT-water nanofluid in a wavy lid-driven porous enclosure at different boundary conditions

Hamzah

Ali

Hatami

2022

Sci Rep

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“…, where tr is the trace operator, T is the temperature, ϕ is the concentration, p is the pressure, E is the electric field, and B is the magnetic field. There are also studies which indicate that some nanofluids can be assumed to behave as Newtonian fluids [8,44,45]. For the sake of simplicity and in order to test our new numerical approach, in this paper, we assume that the stress tensor for the nanofluid is given by the traditional Navier-Stokes fluid model, where:…”

Section: Constitutive Equationsmentioning

confidence: 99%

“…Sankar et al [7] studied the conjugate heat transfer in nanofluid with different nanoparticles in a vertical enclosed annular gap. Berrahil et al [8] reported a numerical investigation of natural convection of a nanofluid in a heated vertical annulus under a uniform magnetic field.…”

Section: Introductionmentioning

confidence: 99%

Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network

et al. 2022

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This study develops a geometry adaptive, physical field predictor for the combined forced and natural convection flow of a nanofluid in horizontal single or double-inner cylinder annular pipes with various inner cylinder sizes and placements based on deep learning. The predictor is built with a convolutional-deconvolutional structure, where the input is the annulus cross-section geometry and the output is the temperature and the Nusselt number for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like data, the convolutional neural network (CNN)-based predictor enables an accurate end-to-end mapping from the geometry input and the desired nanofluid physical field. Taking the computational fluid dynamics (CFD) calculation as the basis of our approach, the obtained results show that the average accuracy of the predicted temperature field and the coefficient of determination R2 are more than 99.9% and 0.998 accurate for single-inner cylinder nanofluid-filled annulus; while for the more complex case of double-inner cylinder, the results are still very close, higher than 99.8% and 0.99, respectively. Furthermore, the predictor takes only 0.038 s for each nanofluid field prediction, four orders of magnitude faster than the numerical simulation. The high accuracy and the fast speed estimation of the proposed predictor show the great potential of this approach to perform efficient inner cylinder configuration design and optimization for nanofluid-filled annulus.

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“…At a 30°i nclination angle, the maximum average Nu and total creation of entropy occur. 9 Moreover, in Berrahil et al, 10 a numerical study of the natural convection of Al 2 O 3 /water nanofluid in a differentially heated vertical annulus under a uniform magnetic field is carried out. Farid Berrahil and others created an in-house Fortran algorithm to solve the equations regulating the magneto-hydrodynamic flow.…”

Section: Introductionmentioning

confidence: 99%

“…A finite volume approach is used to numerically solve the governing equations. Numerous Rayleigh numbers, Hartmann numbers, and nanoparticle volume fractions have been subjected to numerical study in Medebber et al 4 References [5][6][7][8][9][10][11][12] study the Al 2 O 3 /water nano-fluid effect in the following ways. Aghakhani et al 5 investigates the free convection of Al 2 O 3 /water nano-fluid in a slanted container with a curved form fin in the lower section under a magnetic field.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of mixed convection in a lid driven enclosure based on magnetic field presence with nanofluid

Shehata

Dawood

Amer

et al. 2023

Advances in Mechanical Engineering

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Computational analysis is utilized to examine the effects of introducing nanoparticles to a square lid-driven cavity to enhance the hydro-magnet mixed convection. Constant temperatures are imposed along the square container’s vertical edges. Both the top and bottom surfaces are covered with insulation. The lid is thought to move in two ways: increasing or decreasing free convection. In addition, a horizontal magnetic field is applied uniformly. For various Hartmann numbers (Ha) (0:100), Richardson numbers (Ri) (0.001:10), and solid volume fractions (0:0.1), the results are reported. This research is based on a constant Grashof number (Gr) of 104. The effects of parameters, including the Richardson number, Hartmann number, solid volume percentage on the stream, isothermal lines, and local Nusselt numbers (Nu), were investigated numerically. In addition, given various parametric settings, the anticipated results for the average Nusselt (Nuavg) are shown and discussed. For all tested parameters, increasing the magnetic field makes the orientation of the lid more effective on heat and fluid movement. The magnetic field reduces heat and fluid flow. The heat transmission is aided by increasing the solid volume percentage. The effect of nanoparticles on flow and heat transmission is being studied. At Ri = 0.001, the effect of lid orientation is not significant. The highest reduction in heat transfer occurs when adding flow at Ri = 10, = 0, and Ha = 100.

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Numerical investigation on natural convection of Al2O3/water nanofluid with variable properties in an annular enclosure under magnetic field

Cited by 24 publications

References 49 publications

MHD mixed convection and entropy generation of CNT-water nanofluid in a wavy lid-driven porous enclosure at different boundary conditions

MHD mixed convection and entropy generation of CNT-water nanofluid in a wavy lid-driven porous enclosure at different boundary conditions

Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network

Enhancement of mixed convection in a lid driven enclosure based on magnetic field presence with nanofluid

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