A Review of Thermal Conductivity Data, Mechanisms and Models for Nanofluids

Lee, Ji‐Hwan; Lee, Seung–Hyun; Choi, Chul Jin; Jang, Seok Pil; Choi, Seok Jin

doi:10.1260/1759-3093.1.4.269

Cited by 193 publications

(101 citation statements)

References 125 publications

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“…These conflicting data do not allow formulating neither possible mechanisms nor nanofluid thermal conductivity models, though about a dozen of such models are available in literature (see for example [23][24][25][26] and the literature quoted there).…”

Section: Introductionmentioning

confidence: 99%

Thermal conductivity measurements of nanofluids

Pryazhnikov

Минаков

Rudyak

et al. 2017

International Journal of Heat and Mass Transfer

203

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The paper presents the results of systematic measurements of the thermal conductivity coefficient of nanofluids at room temperature. In total, more than fifty various nanofluids based on water, ethylene glycol, and engine oil containing particles of SiO2, Al2O3, TiO2, ZrO2, CuO, and diamond were studied. The nanoparticles volume concentration ranged from 0.25 to 8% and the particles size ranged from 10 to 150 nm. It is shown that the thermal conductivity of nanofluids is not described by the classical theories (Maxwell's and so forth). The nanofluid thermal conductivity coefficient is a complicated function not only of the particle concentration, but also the particles size, their material, and type of base fluid. Measured thermal conductivity coefficients almost always exceed the values calculated by the Maxwell's formula, though nanofluids with sufficiently small particles may have thermal conductivity coefficients even lower than those predicted by the Maxwell theory. However, in all cases, the nanofluid thermal conductivity coefficient enhances with increasing particle size. It is convincingly shown that there is no direct correlation between the thermal conductivity of the nanoparticle material and the thermal conductivity of nanofluid containing these particles. The base liquid also significantly influences the effective thermal conductivity of the nanofluid. It has been confirmed that the lower the thermal conductivity of the base fluid, the higher the relative thermal conductivity coefficient of the nanofluid.

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Section: Introductionmentioning

confidence: 99%

Thermal conductivity measurements of nanofluids

Pryazhnikov

Минаков

Rudyak

et al. 2017

International Journal of Heat and Mass Transfer

203

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“…They have also presented different heat transfer correlations for these fluids as functions of the fluid characteristics and the flow geometry. Lee et al [27] have presented a critical review of the experimental data published for a variety of nanofluids. They have also presented the theoretical models developed for nanofluid thermal conductivity.…”

Section: Natural Convection Of Nanofluidsmentioning

confidence: 99%

Use of Nanofluids for Improved Natural Cooling of Discretely Heated Cavities

Ben‐Mansour

Habib

2013

Advances in Mechanical Engineering

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The present study is aimed at investigating the natural convection heat transfer from discrete heat sources to nanofluids. The behavior of nanofluids was investigated numerically inside a heated cavity to gain insight into convective recirculation and flow processes induced by a nanofluid. A computational model was developed to analyze heat transfer performance of nanofluids inside a cavity taking into account the solid particle dispersion. The model was validated through the comparison with available experimental data, and the results showed good agreement. The influence of the solid volume fraction and the aspect ratios on the flow pattern and heat transfer inside the cavity was investigated. The results show that the intensity of the streamlines increases with the volume fraction. The influence of the loading factor is more distinguished in the vicinity of the upper heaters and, in particular, at the highest heater. The heat transfer increases with the increase in the volume fraction of the nanoparticles in the range of 2 to 10%. As the aspect ratio (AR) increases (channel width is reduced), the streamlines change and the intensity of the streamlines increases slightly, showing flow in the middle of the channel. The stagnation region disappears, and interaction between the two boundary layers at the hot and cold walls is evident.

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“…суспензий частиц размерами от 20 до 100 Å, диспергированных в базовой жидкости. Для нового класса конденсированных сред характерны как аномально высокая теплопроводность [7], так и нелинейные соотношения между теплофизическими свойствами и концентрацией наночастиц, наряду со значительным увеличением критического теплового потока при кипении [8]. Обзор современного положе-ния исследований в области теплопроводности нано-жидкостей приведен в работе [9].…”

Section: Introductionunclassified

Phase equilibria of nitrobenzene – n-heptane system with TiO2 nanoparticle additives

Артеменко¹

2015

EEJET

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A Review of Thermal Conductivity Data, Mechanisms and Models for Nanofluids

Cited by 193 publications

References 125 publications

Thermal conductivity measurements of nanofluids

Thermal conductivity measurements of nanofluids

Use of Nanofluids for Improved Natural Cooling of Discretely Heated Cavities

Phase equilibria of nitrobenzene – n-heptane system with TiO2 nanoparticle additives

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