Numerical modelling of thermophoresis in water-alumina nanofluid under pool boiling conditions

Gobinath, N.; Venugopal, T.; Palani, Karthikeyan; Samuel, Anand A.

doi:10.1016/j.ijthermalsci.2018.02.025

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…Predicting of bubble departure diameter in the present model was successfully done by comparison of the incorporated UDF by using Eq. (22) for the current extended model and the experimental data of Gerardi et al [37], which are used for silica and graphene nanofluids under atmospheric pressure condition. Figure 6 shows the bubble departure diameter against superheat temperature for the present model compared to experimental data, and the average absolute deviation was about 11% between the current model and the experimental data in the literature.…”

Section: Propertymentioning

confidence: 99%

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Simulation of pool boiling of nanofluids by using Eulerian multiphase model

Kamel

Alagha

Lezsovits

et al. 2019

J Therm Anal Calorim

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In the present work, a new simulation of nanofluid/vapor two-phase flow inside the 2-D rectangular boiling chamber was numerically investigated. The Eulerian–Eulerian approach used to predict the boiling curve and the interaction between two phases. The surface modification during pool boiling of silica nanofluid represented by surface roughness and wettability is put into the account in this simulation. New closure correlations regarding the nucleation sites density and bubble departure diameter during boiling of silica nanofluid were inserted to extend the boiling model in this work. Besides, the bubble waiting time coefficient which involved in quenching heat flux under heat flux partitioning HFP model was corrected to improve the results of this study. The numerical results validated with experimental works in the literature, and they revealed good agreements for both pure water and nanofluids. The results found that when improving the heat flux partitioning model HFP by considering the surface modification of nucleate pool boiling parameters, it will give more mechanistic sights compared to the classical model, which is used for predicting of boiling heat transfer of pure liquid.

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Section: Propertymentioning

confidence: 99%

“…Gobinath et al [22] studied the thermodiffusion (soreteffect) of water-alumina nanofluid at sub-cooled pool boiling conditions numerically. They conducted an experimental investigation of pool boiling on a horizontal tube with various subcooled temperatures for alumina nanofluid to validate their model.…”

Section: Introductionmentioning

confidence: 99%

Simulation of pool boiling of nanofluids by using Eulerian multiphase model

Kamel

Alagha

Lezsovits

et al. 2019

J Therm Anal Calorim

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“…Under the subcooled boiling conditions, the heat transfer rate of nanofluid enhanced with ultrasonic technique while for saturated boiling condition ultrasonic was not much effective at high heat flux conditions. Gobinath et al [15] performed the thermophoresis in the nanofluid to solve the mathematical model conditions. Thermophoretic parameters like thermophoretic mobility and thermophoretic velocity was observed at different temperature value of boiling surface.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Nucleate Boiling Heat Transfer Performance using SiO₂/Water Nanofluid

Singh

Sharma

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Nucleate boiling regime is the most efficient regime that gives the maximum heat flux at the wall superheat range of 5 °C to 30 °C. In this current study, the nucleate pool boiling heat transfer performance of SiO2/water nanofluid are studied numerically. The effect of volume fraction (0.01 – 0.06 vol. %) of nanofluid on heat transfer coefficient and wall superheat are investigated. The variation in heat transfer coefficient (HTC) & wall superheat with different heat flux are investigated. Eulerian multiphase approach with RPI boiling model is used to carry out the simulation. The pool boiling heat transfer performance increased with rise in volume concentration of nanofluid up to an optimum concentration of 0.05 % and then it starts to decrease. A reduction in wall superheat of 6.7 %, 8.43 %, 11.27 % and 9.3 % are obtained for SiO2/water nanofluid with volumetric concentration of 0.01, 0.02, 0.05 and 0.06 vol. % respectively as compared to base fluid. Heat transfer coefficient enhanced by 3.55 %, 7.2 %, 23.87 % and 19.67 % for SiO2/water nanofluid with volumetric concentration of 0.01, 0.02, 0.05 and 0.06 vol. % respectively as compared to base fluid.

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“…They figured out that increasing nanoparticle concentration from 0% to 1% caused vapor volume fraction and heat transfer coefficient to increase. Gobinath et al (2018) performed experiments for different heater conditions and numerically analyzed the thermophoresis of aqueous alumina nanofluid under subcooled pool boiling conditions. They used ANSYS Fluent CFD software in numerical analysis and 2-D steady pressure-based implicit solver with Gauss–Seidel iterative procedure.…”

Section: Introductionmentioning

confidence: 99%

Heat transfer enhancement in pool boiling and condensation using h-BN/DCM and SiO₂/DCM nanofluids: experimental and numerical comparison

Çiftçi

Sözen

2020

HFF

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Purpose The purpose of this study is to experimentally and numerically scrutinize the heat transfer enhancement in pool boiling and condensation by changing the hydrophilicity or hydrophobicity properties of the working fluid, i.e. by use of nanofluid solution. Design/methodology/approach For specifying the effects of nanoparticle concentration on heat transfer properties, two different nanofluid solutions (h-BN/DCM and SiO2/DCM) at three different volumetric concentrations were prepared and tested under different heat flux conditions. Boiling curves, alterations in pressure with heat flux and heat transfer coefficients for both boiling and condensation processes were obtained and viscosity measurements were performed for dichloromethane (DCM) and each working fluid was prepared. In addition, a series of numerical simulations, via computational fluid dynamics approach, was performed for specifying the evaporation–condensation phenomena and temperature and velocity distributions. Findings Nanoparticle addition inside the base fluid increased the thermal characteristics of the base fluid significantly. For the experimental results of h-BN/DCM nanofluid, the increment rate in heat transfer coefficient for saturation boiling, after-saturation boiling and condensation processes was found as 27.59%, 14.44% and 15%, respectively. Originality/value The novelty of this comparison study is that there is no such experimental and numerical comparison study in literature for DCM fluid, which concentrates on thermal performance enhancement and compares the effect of different kinds of nanoparticles on heat transfer characteristics for boiling–condensation processes.

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Numerical modelling of thermophoresis in water-alumina nanofluid under pool boiling conditions

Cited by 15 publications

References 22 publications

Simulation of pool boiling of nanofluids by using Eulerian multiphase model

Simulation of pool boiling of nanofluids by using Eulerian multiphase model

Numerical Simulation of Nucleate Boiling Heat Transfer Performance using SiO₂/Water Nanofluid

Heat transfer enhancement in pool boiling and condensation using h-BN/DCM and SiO₂/DCM nanofluids: experimental and numerical comparison

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Numerical modelling of thermophoresis in water-alumina nanofluid under pool boiling conditions

Cited by 15 publications

References 22 publications

Simulation of pool boiling of nanofluids by using Eulerian multiphase model

Simulation of pool boiling of nanofluids by using Eulerian multiphase model

Numerical Simulation of Nucleate Boiling Heat Transfer Performance using SiO2/Water Nanofluid

Heat transfer enhancement in pool boiling and condensation using h-BN/DCM and SiO2/DCM nanofluids: experimental and numerical comparison

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Numerical Simulation of Nucleate Boiling Heat Transfer Performance using SiO₂/Water Nanofluid

Heat transfer enhancement in pool boiling and condensation using h-BN/DCM and SiO₂/DCM nanofluids: experimental and numerical comparison