Thermal performance and pressure drop analysis of nanofluids in turbulent forced convective flows

Bayat, Javad; Nikseresht, Amir H.

doi:10.1016/j.ijthermalsci.2012.04.012

Cited by 66 publications

(28 citation statements)

References 40 publications

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“…6 indicates that models A, B, C and E predict a decrease of pumping power with increasing particle loading, while the results of the case D are considerably more optimistic. Similar results were obtained also by Bayat and Nikseresht [49] performing a numerical analysis on alumina-EG/water nanofluid on turbulent flow.…”

Section: Fig 4 Heat Transfer Coefficient Variation For Case Esupporting

confidence: 88%

Simulation of Nanofluids Turbulent Forced Convection at High Reynolds Number: A Comparison Study of Thermophysical Properties Influence on Heat Transfer Enhancement

Minea

2014

Flow Turbulence Combust

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After many years of studying of nanofluids development, their thermophysical properties are not yet known precisely and the judgment of their true potential is difficult. This fact was demonstrated by studying numerically forced convection heat transfer at high Re of a nanofluid consisting of water and Al 2 O 3 in horizontal tubes. Five different models from the literature are used to express the thermophysical properties in terms of particle loading and they led to different results in heat transfer enhancement. In particular, the heat transfer coefficient of water-based Al 2 O 3 nanofluids is increased by 2.33-26.45 % under fixed high Reynolds number compared with that of pure water. As a conclusion, the effect of uncertainties in adopting nanofluids properties was observed.

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Section: Fig 4 Heat Transfer Coefficient Variation For Case Esupporting

confidence: 88%

Simulation of Nanofluids Turbulent Forced Convection at High Reynolds Number: A Comparison Study of Thermophysical Properties Influence on Heat Transfer Enhancement

Minea

2014

Flow Turbulence Combust

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“…19 The flow was axisymmetric, steady, and turbulent through a circular tube which had 1 cm diameter and 1 m length. The finite volume technique was used to discretize a set of coupled non-linear Navier-Stokes differential equations.…”

Section: Numerical Studies In Tubes and Ductsmentioning

confidence: 99%

A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits

Safaei

Shadloo

Goodarzi

et al. 2016

Advances in Mechanical Engineering

106

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Application of nanofluids in heat transfer enhancement is prospective. They are solid/liquid suspensions of higher thermal conductivity and viscosity compared to common working fluids. A number of studies have been performed on the effect of nanofluids in heat transfer to determine the enhancement of properties in addition to rearrangement of flow passage configurations. The principal objective of this study is to elaborate this research based on natural, forced, and the mixed heat transfer characteristics of nanofluids exclusively via convection for single-and two-phase mixture models. In this study, the convection heat transfer to nanofluids has been reviewed in various closed conduits both numerically and experimentally.

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“…The following Eqs. (6)(7)(8)(9)(10)(11)(12)(13)(14) give the thermophysical properties of Al 2 O 3 -water nanofluid.…”

Section: Thermophysical Properties Of Nanofluidsmentioning

confidence: 99%

“…Bayat and Nikheresht [7] studied the effects of nanoparticle concentration (1-10 % Al 2 O 3 ) in EG/water mixture numerically. They examined different turbulence models to attain the best prediction, and finally they suggested the standard k−ε turbulence model as the most accurate model.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of Heat Transfer, Pressure Drop and Wall Shear Stress Characteristics of Al2O3-Water Nanofluid in a Square Duct

Kaya

2015

Arab J Sci Eng

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Turbulent heat transfer, pressure drop and wall shear stress behavior of the nanofluid Al 2 O 3 -water mixture in a square duct under constant wall heat flux are investigated numerically. Single-phase approach is taken into account during the simulations. All of the nanofluid properties depend on the temperature and the nanoparticle volume concentration. The renormalization group theory RNG k − ε model is employed in order to model turbulence. Validation tests of the numerical results are done by using water as the first working fluid. Similar models and methods are chosen for the simulation of nanofluid (Al 2 O 3 -water) flow and heat transfer. A very good agreement is realized with the previous water and nanofluid related theoretical-empirical heat transfer and pressure drop correlations. The rate of heat transfer is increased by the presence of nanofluids when compared to that of water. Increasing Re number and particle's volumetric concentration increases the convection heat transfer coefficient, pressure drop and wall shear stress along the duct. On the other hand, this study confirmed that single-phase model approach is appropriate for the simulation of Al 2 O 3 -water flow and heat transfer. List of Symbolsa P Coefficient of P cell C 1ε Turbulent model constant (=1.42) C 2ε Turbulent model constant (=1.68) C p Constant pressure specific heat (J/kgK) C μ Turbulent model constant (=0.0845) c Constant part of the source term D Hydraulic diameter (m) f Darcy friction factor g Gravitational acceleration (m/s 2 ) h Convection heat transfer coefficient (W/m 2 K) I Turbulent intensity (=u /u) k Turbulent kinetic energy (m 2 /s) L Length of the duct (m) l Turbulent mixing length (m) l ε Length scale of turbulent kinetic energy dissipation (m) l μ Length scale of viscosity (m) N u Nusselt number P Pressure (Pa) Pr Prandtl number q Wall heat flux (W/m 2 ) R Effect of strain in ε equation (kg/ms 4 ) Re Re number S Modulus of mean rate of strain tensor (1/s) T Temperature (K) u Time-averaged mean velocity (m/s) u Instantaneous velocity component (m/s) u Velocity (m/s) t Time (s) u Velocity (m/s) X Distance from the entrance of the duct (m) y * Non-dimensional viscous sublayer thickness y * T Non-dimensional thermal sublayer thickness 123 Arab J Sci Eng Greek Symbols α Inverse effective Pr number (=1/P r ) α ε Inverse effective Pr number for dissipation rate of turbulent kinetic energy (=1/Pr ε ) α k Inverse effective Pr number for turbulent kinetic energy (=1/Pr k ) α t Inverse effective Pr number for turbulent flow (=1/Pr t ) β Model constant (=0.012) P Pressure drop (Pa) ε Turbulent kinetic energy dissipation rate (m 2 /s 3 ) η Rate of strain in turbulent flow (= Sk/ε) τ Wall shear stress (N/m 2 ) η 0 Model constant (=4.38) λ Thermal conductivity (W/mK) μ Molecular viscosity (kg/ms) φ Volumetric concentration of nanoparticles ρ Density (kg/m 3 )

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Thermal performance and pressure drop analysis of nanofluids in turbulent forced convective flows

Cited by 66 publications

References 40 publications

Simulation of Nanofluids Turbulent Forced Convection at High Reynolds Number: A Comparison Study of Thermophysical Properties Influence on Heat Transfer Enhancement

Simulation of Nanofluids Turbulent Forced Convection at High Reynolds Number: A Comparison Study of Thermophysical Properties Influence on Heat Transfer Enhancement

A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits

Numerical Investigation of Heat Transfer, Pressure Drop and Wall Shear Stress Characteristics of Al2O3-Water Nanofluid in a Square Duct

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