Thermal Conductivity of Nanofluids: Review

Younes, Hammad; Christensen, Greg; Liu, Dong; Hong, Haiping; Ghaferi, Amal Al

doi:10.1166/jon.2015.1151

Cited by 66 publications

(34 citation statements)

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“…[105][106][107][108][109][110][111][112][113] on the thermal conductivity of water based nanofluids, as well as comprehensive reviews such as Refs. [114][115][116][117][118][119][120][121][122][123].…”

Section: Experimental Based Correlationsmentioning

confidence: 99%

Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

et al. 2019

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It has been more than two decades since the discovery of nanofluids-mixtures of common liquids and solid nanoparticles less than 100 nm in size. As a type of colloidal suspension, nanofluids are typically employed as heat transfer fluids due to their favorable thermal and fluid properties. There have been numerous numerical studies of nanofluids in recent years (more than 1000 in both 2016 and 2017, based on Scopus statistics). Due to the small size and large numbers of nanoparticles that interact with the surrounding fluid in nanofluid flows, it has been a major challenge to capture both the macro-scale and the nano-scale effects of these systems without incurring extraordinarily high computational costs.To help understand the state of the art in modeling nanofluids and to discuss the challenges that remain in this field, the present article reviews the latest developments in modeling of nanofluid flows and heat transfer with an emphasis on 3D simulations. In part I, a brief overview of nanofluids (fabrication, applications, and their achievable thermo-physical properties) will be presented first.Next, various forces that exist in particulate flows such as drag, lift (Magnus and Saffman), Brownian, thermophoretic, van der Waals, and electrostatic double layer forces and their significance in nanofluid flows are discussed. Afterwards, the main models used to calculate the thermophysical properties of nanofluids are reviewed. This will be followed with the description of the main physical models presented for nanofluid flows and heat transfer, from single-phase to Eulerian and Lagrangian two-phase models. In part II, various computational fluid dynamics (CFD) techniques will be presented. Next, the latest studies on 3D simulation of nanofluid flow in various regimes and configurations are reviewed. The present review is expected to be helpful for researchers working on numerical simulation of nanofluids and also for scholars who work on experimental aspects of nanofluids to understand the underlying physical phenomena occurring during their experiments.

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Section: Experimental Based Correlationsmentioning

confidence: 99%

Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

et al. 2019

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“…The thermal conductivity of CNTs is more than seven times higher than that of copper, which is well known for having a good thermal conductivity of 385 W/Mk [19][20][21][22]. Because of their remarkable thermal, electrical, mechanical properties, and low density, carbon nanotubes are ideal materials for reinforcement in composites [23][24][25][26][27], sensors [28], and many other applications, such as thermal managements [29][30][31].…”

Section: Carbon Nanotubesmentioning

confidence: 99%

Nanofluids Based on Carbon Nanostructures

Younes¹,

Ghaferi²,

Saadat³

et al. 2016

Advances in Carbon Nanostructures

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A nanofluid consists in a liquid suspension of nanometer-sized particles. These fluids may contain (or not) surface-active agents to aid in the suspension of the particles. Nanometer-sized particles have higher thermal conductivity than the base fluids. Oxides, metals, nitrides, and nonmetals, like carbon nanotubes, can be used as nanoparticles in nanofluids. Water, ethylene glycol, oils, and polymer solutions can be used as base fluids. In this chapter, we summarize the recent studies of using CNTs and graphene to improve the thermal conductivity of nanofluids. Moreover, we refer to the studies about the effect of using magnetic fields on enhancing the thermal conductivity of nanofluids. Too much discrepancy about thermal conductivity of nanofluids can be found in the literature. For carbon nanofluids, unfortunately, no significant improvements on thermal conductivity are observed using low concentrations. Different improvement percentages have been reported. This variation in the thermal conductivity can be attributed to many factors, such as particle size temperature, pH, or zeta potential. We believe that more research efforts need to be made in order to, first, improve the thermal conductivity of nanofluids and, second, assess the effect of the different parameters and conditions on the thermal conductivity of nanofluids.

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“…Theoretically, the electrical current density of metallic SWNTs was found to be 1000 times more than that of metals such as silver and copper [2]. The remarkable structural, thermal, electrical and mechanical properties make CNTs a potential material in many applications such as nanocomposites [3][4][5][6], nanofluids [7][8][9][10] and lubricating technology [11][12][13][14][15][16]. For lubricating technology, this has opened up a realm of research opportunities.…”

Section: Introductionmentioning

confidence: 99%

TC Study of Manufacturable Nano Grease: Evidence of 3D Network Structure

et al. 2018

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Surfactant-free grease made from carbon nanotube and carbon nanofibers has shown improvement in thermal conductivity. Time-dependent thermal conductivity (TC) magnetic study with different (Fe 2 O 3 concentrations and Ni-coated carbon nanotubes) was performed. The results show that under the influence of a strong magnetic field, the TC value remains constant even with different processing times, magnetic fields, and Fe 2 O 3 concentrations. An explanation for these results could be that for a high loading of CNTs ([ 10 wt%), CNTs become associated strongly with each other by van der Waals forces and form a three-dimensional network (3D), which makes magnetic alignment of nanotubes very unlikely. A slight change on the TC of carbon nanofibers grease was noticed, which is probably because of the weak Van der Waals forces among the nanofibers. Mixing the CNFs with SWNTs produced a stable grease as SWNTs help to create the 3D network structure; however, the TC has dropped to 0.37 W/mK which is attributed to the lower TC of the SWNTs grease itself. The scientific merit of this paper is that the results provide further support that the CNTs form a 3D network structure and move forward the R&D work of manufacturable carbon nano grease.

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Thermal Conductivity of Nanofluids: Review

Cited by 66 publications

References 0 publications

Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

Nanofluids Based on Carbon Nanostructures

TC Study of Manufacturable Nano Grease: Evidence of 3D Network Structure

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