A modified two-phase mixture model of nanofluid flow and heat transfer in a 3-D curved microtube

Akbari, Omid Ali; Safaei, Mohammad Reza; Goodarzi, Marjan; Akbar, Noreen Sher; Zarringhalam, Majid; Shabani, Gholamreza Ahmadi Sheikh; Dahari, Mahidzal

doi:10.1016/j.apt.2016.08.002

Cited by 178 publications

(49 citation statements)

References 37 publications

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“…Finite volume method [27,28] and the second-order upwind discretization method with maximum residual of 10 −6 [29,30] was used. For coupling velocity-pressure equations, the SIMPLEC [31,32] algorithm was used. Also, the effect of nanoparticle volume fraction, Reynolds number, and jet number on flow and heat transfer parameters were investigated; and flow parameters, temperature, and streamlines contours are the presented.…”

Section: Numerical Detailsmentioning

confidence: 99%

Heat Transfer of Oil/MWCNT Nanofluid Jet Injection Inside a Rectangular Microchannel

et al. 2019

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In the current study, laminar heat transfer and direct fluid jet injection of oil/MWCNT nanofluid were numerically investigated with a finite volume method. Both slip and no-slip boundary conditions on solid walls were used. The objective of this study was to increase the cooling performance of heated walls inside a rectangular microchannel. Reynolds numbers ranged from 10 to 50; slip coefficients were 0.0, 0.04, and 0.08; and nanoparticle volume fractions were 0-4%. The results showed that using techniques for improving heat transfer, such as fluid jet injection with low temperature and adding nanoparticles to the base fluid, allowed for good results to be obtained. By increasing jet injection, areas with eliminated boundary layers along the fluid direction spread in the domain. Dispersing solid nanoparticles in the base fluid with higher volume fractions resulted in better temperature distribution and Nusselt number. By increasing the nanoparticle volume fraction, the temperature of the heated surface penetrated to the flow centerline and the fluid temperature increased. Jet injection with higher velocity, due to its higher fluid momentum, resulted in higher Nusselt number and affected lateral areas. Fluid velocity was higher in jet areas, which diminished the effect of the boundary layer.

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Section: Numerical Detailsmentioning

confidence: 99%

Heat Transfer of Oil/MWCNT Nanofluid Jet Injection Inside a Rectangular Microchannel

et al. 2019

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“…Chamkha [3] reported the flow of two-phase with hydromagnetic. Akbari et al [4] have presented the mixture model of a two-phase nanofluid. Goodarzi et al [5] examined the two-phase flow for shallow cavity with a mixture model.…”

Section: Introductionmentioning

confidence: 99%

Study of Two-Phase Newtonian Nanofluid Flow Hybrid with Hafnium Particles under the Effects of Slip

et al. 2020

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This paper investigates the role of slip in a two-phase flow of Newtonian fluid. The nano-size Hafnium particles are used in the base fluid. The fluid under consideration is studied for two cases namely (i) fluid phase (ii) phase of particles. Both cases are examined for three types of geometries. The governing equations are simplified in nondimensional form for each phase along with boundary conditions. The resulting equations have been analytically solved to get exact solutions for both fluid and particle phases. Different features of significant physical factors are discussed graphically. The flow patterns have been examined through streamlines.

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“…Improved thermophysical feature of the fluids containing nanostructures makes them as favorable candidates for heat transfer fluid in thermal systems such as heat pipes, solar collectors, heat exchangers, etc. [18][19][20][21][22][23][24]. Elsayed et al [25], carried out a numerical study on heat transfer of turbulent flow inside a helically coiled tube and concluded that using Al 2 O 3 /water instead of water led to 60% increase in heat transfer coefficient.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

et al. 2020

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The existence of solid-phase nanoparticles remarkably improves the thermal conductivity of the fluids. The enhancement in this property of the nanofluids is affected by different items such as the solid-phase volume fraction and dimensions, temperature, etc. In the current paper, three different mathematical models, including polynomial correlation, Multivariate Adaptive Regression Spline (MARS), and Group Method of Data Handling (GMDH), are applied to forecast the thermal conductivity of nanofluids containing MgO particles. The inputs of the model are the base fluid thermal conductivity, volume concentration, and average dimension of solid-phase, and nanofluids’ temperature. Comparing the proposed models revealed higher confidence of GMDH in estimating the thermal conductivity, which is attributed to its complicated structure and more appropriate consideration of the input’s interaction. The values of R-squared for the correlation, MARS, and GMDH are 0.9949, 0.9952, and 0.9991, respectively. In addition, based on the sensitivity analysis, the effect of thermal conductivity of the base fluid on the overall thermal conductivity of nanofluids is more remarkable compared with the other inputs such as volume fraction, temperature, and dimensions of the particles which are used as the inputs of the models.

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A modified two-phase mixture model of nanofluid flow and heat transfer in a 3-D curved microtube

Cited by 178 publications

References 37 publications

Heat Transfer of Oil/MWCNT Nanofluid Jet Injection Inside a Rectangular Microchannel

Heat Transfer of Oil/MWCNT Nanofluid Jet Injection Inside a Rectangular Microchannel

Study of Two-Phase Newtonian Nanofluid Flow Hybrid with Hafnium Particles under the Effects of Slip

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

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