Effective viscosity of nanofluids — A modified Krieger–Dougherty model based on particle size distribution (PSD) analysis

Selvakumar, R. Deepak; Dhinakaran, S.

doi:10.1016/j.molliq.2016.10.137

Cited by 53 publications

(13 citation statements)

References 66 publications

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“…As it was discussed in [6,[36][37][38][39], more advanced correlations for viscosity and thermal conductivity of nanofluids besides nanoparticle concentration and temperature-as proposed in this paper, should include at least nanoparticle shape and size [6,[36][37][38][39]. However, in order to establish the correct trend of variation of the thermal conductivity and viscosity of nanofluids with a number of parameters already mentioned, it is necessary to broad our knowledge about the mechanisms leading to the improvement or deterioration of the thermal conductivity and viscosity of nanofluids as it is recently shown by [40,41]. In the literature several mechanisms potentially responsible for thermal conductivity enhancement of nanofluids are proposed.…”

Section: Discussionmentioning

confidence: 91%

A Comparison of Empirical Correlations of Viscosity and Thermal Conductivity of Water-Ethylene Glycol-Al2O3 Nanofluids

2020

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Because of their superb thermal conductivity, nanofluids are seen as new generation of cooling mediums in many engineering applications. It is well established that even a small amount of nanoparticles mixed with a base fluid may result in distinct thermal conductivity enhancement. On the other hand, addition of nanoparticles to the base fluid results in its substantial viscosity increase. Therefore, it is very difficult to evaluate the relative importance of viscosity and thermal conductivity of the nanofluid on convective heat transfer performance. In order to estimate such resultant impact properly, it is necessary to develop reliable correlation equations for predictions of these two thermophysical properties of nanofluids. In this paper, the thermal conductivity and dynamic viscosity of five fluids, i.e., pure water, ethylene glycol (EG) and three mixtures of water and EG with volume ratio of 40:60, 50:50 and 60:40 have been experimentally determined. The aforementioned fluids served as base fluids in nanofluids with Al2O3 nanoparticles at the concentration of 0.01%, 0.1% and 1% by weight. A set of 20 correlations for prediction of thermal conductivity and dynamic viscosity of base fluids and corresponding nanofluids has been developed. Moreover, present results have been confronted with literature data and predictions made by use of carefully selected recognized literature correlations.

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Section: Discussionmentioning

confidence: 91%

A Comparison of Empirical Correlations of Viscosity and Thermal Conductivity of Water-Ethylene Glycol-Al2O3 Nanofluids

2020

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“…Dissolution method + hot plate drying [6], [7], [8], [10], [16], [22], [25], [27] Dissolution method + oven/furnace drying [15], [19], [23], [28], [21] Magnetical stirring in molten state [9] Mechanical stirring in molten state [17], [18] Static mixing in molten state in a furnace [11] Twin screw micro-compounder mixing in molten state [14] In-situ production [12], [13] Dry milling in a ball mill [26] Dry mixing [20], [21] The final particle or cluster size of the nanoparticles dispersed in the molten salt is needed in order to evaluate the efficiency of the production method and its influence on the thermophysical properties. Selvakumar and Dhinkaran [31] developed a model to predict the volume fraction of clusters present in a nanofluid from the particle size distribution measurement. This effective volume fraction can be used to model the rheological behaviour.…”

Section: Methods Referencesmentioning

confidence: 99%

Influence of the production method on the thermophysical properties of high temperature molten salt-based nanofluids

Navarrete

Hernández

Vela

et al. 2020

Journal of Molecular Liquids

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“…In 2017, Selvakumar and Dhinakaran made a modification on the proposed model by Chen et al by introducing the term of interfacial layers surrounding the clusters [1]. This correlation is given as follows:…”

Section: Theoretical Models On Viscosity Of Nanofluidsmentioning

confidence: 99%

Dynamic Viscosity of Graphene- and Ferrous Oxide-Based Nanofluids: Modeling and Experiment

Al-Wadhahi¹,

Vakili‐Nezhaad²,

Ghafri³

2020

Thermophysical Properties of Complex Materials

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This study focused on measuring the viscosity and analyzing the behavior of two types of nanofluids: ferrous oxide-deionized (DI) water nanofluids and graphene-DI water nanofluids at different temperatures and volume fractions. Zeta potential measurement, which was performed to check the stability of the nanofluids, showed stable suspensions. All viscosity measurements were conducted using a capillary viscometer at temperatures ranging between 25 and 65°C. Both types of nanofluids showed increasing viscosity with increasing nanoparticle loading and decreasing viscosity with increasing temperature. Furthermore, experiments on different-sized ferrous oxide-based nanofluids revealed inverse relation between the size of nanoparticles and viscosity. An accurate model was developed based on the Buckingham Pi theorem to fit all factors affecting viscosity in a dimensionless form. These factors are the viscosity of the base fluid, nanoparticles' volume fraction, nanoparticles' size, the temperature of the system, some molecular properties, and zeta potential.

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Effective viscosity of nanofluids — A modified Krieger–Dougherty model based on particle size distribution (PSD) analysis

Cited by 53 publications

References 66 publications

A Comparison of Empirical Correlations of Viscosity and Thermal Conductivity of Water-Ethylene Glycol-Al2O3 Nanofluids

A Comparison of Empirical Correlations of Viscosity and Thermal Conductivity of Water-Ethylene Glycol-Al2O3 Nanofluids

Influence of the production method on the thermophysical properties of high temperature molten salt-based nanofluids

Dynamic Viscosity of Graphene- and Ferrous Oxide-Based Nanofluids: Modeling and Experiment

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