A new thermal conductivity model for nanofluids

In a quiescent suspension, nanoparticles move randomly and thereby carry relatively large volumes of surrounding liquid with them. This micro-scale interaction may occur between hot and cold regions, resulting in a lower local temperature gradient for a given heat flux compared with the pure liquid case. Thus, as a result of Brownian motion, the effective thermal conductivity, k_eff, which is composed of the particles conventional static part and the Brownian motion part, increases to result in a lower temperature gradient for a given heat flux. To capture these transport phenomena, a new thermal conductivity model for nanofluids has been developed, which takes the effects of particle size, particle volume fraction and temperature dependence as well as properties of base liquid and particle phase into consideration by considering surrounding liquid traveling with randomly moving nanoparticles.The strong dependence of the effective thermal conductivity on temperature and material properties of both particle and carrier fluid was attributed to the long impact range of the interparticle potential, which influences the particle motion. In the new model, the impact of Brownian motion is more effective at higher temperatures, as also observed experimentally. Specifically, the new model was tested with simple thermal conduction cases, and demonstrated that for a given heat flux, the temperature gradient changes significantly due to a variable thermal conductivity which mainly depends on particle volume fraction, particle size, particle material and temperature. To improve the accuracy and versatility of the k_effmodel, more experimental data sets are needed.     

Adjustable thermal conductivity in carbon nanotube nanofluids

Homogeneous and stable nanofluids have been produced by suspending well dispersible multi-walled carbon nanotubes (CNTs) into ethylene glycol base fluid. CNT nanofluids have enhanced thermal conductivity and the enhancement ratios increase with the nanotube loading and the temperature. Thermal conductivity enhancement was adjusted by ball milling and cutting the treated CNTs suspended in the nanofluids to relatively straight CNTs with an appropriate length distribution. Our findings indicate that the straightness ratio, aspect ratio, and aggregation have collective influence on the thermal conductivity of CNT nanofluids.     

A micro-convection model for thermal conductivity of nanofluids

Increase in the specific surface area as well as Brownian motion are supposed to be the most significant reasons for the anomalous enhancement in thermal conductivity of nanofluids. This work presents a semi-empirical approach for the same by emphasizing the above two effects through micro-convection. A new way of modeling thermal conductivity of nanofluids has been explored which is found to agree excellently with a wide range of experimental data obtained by the present authors as well as the data published in literature     

Theory for anisotropic thermal conductivity of magnetic nanofluids

Effective thermal conductivity tensor for magnetic nanofluids containing magnetizable nanoparticles suspended in a base liquid is theoretically investigated with a two-step homogenization method. First, we adopt differential effective medium theory to determine the equivalent thermal conductivity of magnetizable nanoparticle chains. Second, we generalize self-consistent anisotropic effective medium theory to study the effective thermal conductivity tensors of magnetic nanofluids. Numerical results show that the aspect ratio of chain-like aggregated clusters plays an important role in enhancement of anisotropic thermal conductivity. In addition, our theoretical results on the elements of thermal conductivity parallel to the fields Kez and perpendicular to the fields Kex are in good agreement with experimental data. Furthermore, we predict the nonmonotonic dependence of effective thermal conductivity on magnetic field strength, in accordance with experimental reports.     

Computational analysis of factors influencing thermal conductivity of nanofluids

Numerical investigations are conducted to study the effect of factors such as particle clustering and interfacial layer thickness on thermal conductivity of nanofluids. Based on this, parameters including Kapitza radius and fractal and chemical dimension which have received little attention by previous research are rigorously investigated. The degree of thermal enhancement is analyzed for increasing aggregate size, particle concentration, interfacial thermal resistance, and fractal and chemical dimensions. This analysis is conducted for water-based nanofluids of Alumina (Al₂O₃), CuO, and Titania (TiO₂) nanoparticles where the particle concentrations are varied up to 4 vol%. Results from the numerical work are validated using available experimental data. For the case of aggregate size, particle concentration, and interfacial thermal resistance, the aspect ratio (ratio of radius of gyration of aggregate to radius of primary particle, R _g/a) is varied from 2 to 60. It was found that the enhancement decreases with interfacial layer thickness. Also the rate of decrease is more significant after a given aggregate size. For a given interfacial resistance, the enhancement is mostly sensitive to R _g/a < 20 indicated by the steep gradients of data plots. Predicted and experimental data for thermal conductivity enhancement are in good agreement. On the influence of fractal and chemical dimensions (d _l and d _f) of Alumina–water nanofluid, the R _g/a was varied from 2 to 8, d _l from 1.2 to 1.8, and d _f from 1.75 to 2.5. For a given concentration, the enhancement increased with the reduction of d _l or d _f. It appears a distinctive sensitivity of the enhancement to d _f, in particular, in the range 2–2.25, for all values of R _g/a. However, the sensitivity of d _l was largely depended on the value of R _g/a. The information gathered from this study on the sensitivity of thermal conductivity enhancement to aggregate size, particle concentration, interfacial resistance, and fractal and chemical dimensions will be useful in manufacturing highly thermally conductive nanofluids. Further research on the refine cluster evolution dynamics as a function of particle-scale properties is underway.     

Investigation on characteristics of thermal conductivity enhancement of nanofluids

It has been shown that a nanofluid consisting of nanoparticles dispersed in base fluid has much higher effective thermal conductivity than pure fluid. In this study, four kinds of nanofluids such as multiwalled carbon nanotube (MWCNT) in water, CuO in water, SiO₂ in water, and CuO in ethylene glycol, are produced. Their thermal conductivities are measured by a transient hot-wire method. The thermal conductivity enhancement of water-based MWCNT nanofluid is increased up to 11.3% at a volume fraction of 0.01. The measured thermal conductivities of MWCNT nanofluids are higher than those calculated with Hamilton–Crosser model due to neglecting solid–liquid interaction at the interface. The results show that the thermal conductivity enhancement of nanofluids depends on the thermal conductivities of both particles and the base fluid.     

A cell model approach for thermal conductivity of nanofluids

This work presents a cell model for predicting the thermal conductivity of nanofluids. Effects due to the high specific surface area of the mono-dispersed nanoparticles and the micro-convective heat transfer enhancement associated with the Brownian motion of particles are addressed in detail. Novelty of the paper lies in its prediction of the non-linear dependence of thermal conductivity of nanofluids on particle volume fraction at low particle concentrations. The model is found to correctly predict the trends observed in experimental data over a wide range of particle sizes, temperatures and particle concentrations.     

Significant thermal conductivity enhancement for nanofluids containing graphene nanosheets

We developed a facile technique to produce ethylene glycol based nanofluids containing graphene nanosheets. The thermal conductivity of the base fluid was increased significantly by the dispersed graphene: up to 86% increase for 5.0 vol % graphene dispersion. The 2D structure and stiffness of graphene and graphene oxide help to increase the thermal conductivity of the nanofluid. The thermal conductivity of graphene oxide and graphene in the fluid were estimated to be ∼4.9 and 6.8 W/m K, respectively.     

Dispersibility dependence of thermal conductivity of carbon nanotube based nanofluids

A differential effective medium theory together with Brownian motion is used to predict Effective Thermal Conductivity (ETC) of CNT nanofluids. ETC was influenced significantly by Brownian motion and enhancement was higher in dilute nanofluids. A theoretical model employing an effective volume fraction with dispersibility factor agrees well with experimental data.     

Predicting the thermal conductivity enhancement of nanofluids using computational intelligence

Nanofluids, composed of nanoparticles in base liquids, have drawn increasing attention in heat transfer applications due to their anomalously increased thermal conductivity. Pertinent parameters, including the base liquid thermal conductivity, particle thermal conductivity, particle size, particle volume fraction, and temperature, have been shown to have significant but complex effects on thermal performance of nanofluids, which is commonly characterized by the thermal conductivity enhancement, E%. In this work, machine learning is used to develop the Gaussian process regression model to find statistical correlations between E% and aforementioned physical parameters among various types of nanofluids. Nearly 300 nanofluid samples, dispersions of metal and ceramic nanoparticles in water, ethylene glycol, and transformer oil, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of E%.     

Prediction of thermal conductivity and viscosity of nanofluids by molecular dynamics simulation

Limitations of conventional heat transfer fluids in different industries because of their poor thermal conductivity made heat transfer improvement in working fluids was performing, as a new method of advanced heat transfer. Therefore, the dispersion solid particle idea in fluids, which has been started with mili- and micrometer particles, completed by using nanoparticles and today nanofluids have been found to provide a considerable heat transfer and viscosity enhancement in comparison to conventional fluids such as water, ethylene glycol, and engine oil. In this study, molecular dynamics simulation was used to predict thermal conductivity and viscosity of nanofluids. Water was used as a base fluid. The simple point charge-extended (SPC/E) model was used for simulation of water and Ewald sum method for electrostatic interactions. Lennard–Jones potential for Van der Waals interactions, KTS potential for water and SiO₂ and Spor and Heinzinger correlation for water and Pt were used. The results were compared with experimental data. For investigation of the effect of temperature, simulation was done for three temperatures of 20, 30, and 50?C. The results showed that the ratio of thermal conductivity of nanofluid to base fluid and viscosity will decrease as the temperature increases. The effect of the concentration of nanoparticle was studied for three different concentrations, namely, 0.45, 1.85, and 4%. The thermal conductivity of nanofluid increases with increasing the concentration. Moreover, the effect of two nanoparticle sizes (i.e., 5 and 7 nm) on the thermal conductivity of nanofluid was investigated. It was shown that an increase in the size causes a decrease in the thermal conductivity. Finally, by replacing the SiO₂nanoparticle with a Pt nanoparticle in the nanofluid, it was observed that the kind of nanoparticle had not a considerable effect on increasing the thermal conductivity of nanofluid.     

Effect of multipolar interaction on the effective thermal conductivity of nanofluids

Nanofluids or liquids with suspended nanoparticles are likely to be the future heat transfer media, as they exhibit higher thermal conductivity than those of liquids. It has been proposed that nanoparticles are apt to congregate and form clusters, and hence the interaction between nanoparticles becomes important. In this paper, by taking into account the interaction between nearest-neighbour inclusions, we adopt the multiple image method to investigate the effective thermal conductivity of nanofluids. Numerical results show that then the thermal conductivity ratio between the nanoparticles and fluids is large, and the two nanoparticles are close up and even touch, and the point-dipole theory such as Maxwell--Garnett theory becomes rough as many-body interactions are neglected. Our theoretical results on the effective thermal conductivity of CuO/water and Al$_{2}$O$_{3}$/water nanofluids are in good agreement with experimental data.     

Sunlight absorbing potential of carbon nanoball water and ethylene glycol-based nanofluids

Solar thermal collectors are applicable in the water heating or space conditioning systems in which surface-based absorption of incident solar flux cause high heat losses. Therefore, an enhancement in the efficiency of solar harvesting devices is a basic challenge which requires great effort. Adding nanoparticles to the working fluid in direct absorption solar collector, which has been recently proposed, leads to improvement in the working fluid thermal and optical properties such as thermal conductivity and absorption coefficient. This results certainly in collector efficiency enhancement. In this paper, the characteristics of nanofluids consisting carbon nanoball in water- and ethylene glycol-based suspensions in consideration of their use as sunlight absorber fluid in a DASC are investigated. It was found that by using of 300 ppm carbon nanoballs, the extinction coefficient of pure water and ethylene glycol is increased by about 3.9 cm^?1 and 3.4 cm^?1 in average, respectively. With these significantly promising optical properties, a direct absorption solar collector using carbon nanoball-based nanofluids can achieve relatively higher efficiencies, compared with a conventional solar collector.     

The effect of particle size on the thermal conductivity of alumina nanofluids

We present new data for the thermal conductivity enhancement in seven nanofluids containing 8–282 nm diameter alumina nanoparticles in water or ethylene glycol. Our results show that the thermal conductivity enhancement in these nanofluids decreases as the particle size decreases below about 50 nm. This finding is consistent with a decrease in the thermal conductivity of alumina nanoparticles with decreasing particle size, which can be attributed to phonon scattering at the solid–liquid interface. The limiting value of the enhancement for nanofluids containing large particles is greater than that predicted by the Maxwell equation, but is predicted well by the volume fraction weighted geometric mean of the bulk thermal conductivities of the solid and liquid. This observation was used to develop a simple relationship for the thermal conductivity of alumina nanofluids in both water and ethylene glycol.     

水基ZnO纳米流体电导和热导性能研究

利用水热法生成了形状规则、粒径均匀的球形ZnO纳米颗粒, 并超声分散于水中, 制备得到稳定的水基ZnO纳米流体. 实验测量水基ZnO纳米流体在体积分数和温度变化时的电导率, 并测试室温下水基ZnO纳米流体在不同体积分数下的热导率. 实验结果表明, ZnO纳米颗粒的添加较大地提高了基液(纯水)的热导率和电导率, 水基ZnO纳米流体的电导率随纳米颗粒体积分数增加呈非线性增加关系, 而电导率随温度变化呈现出拟线性关系; 纳米流体的热导率与纳米颗粒体积分数增加呈近似线性增加关系. 本文在经典Maxwell热导模型和布朗动力学理论的基础上, 同时考虑了吸附层、团聚体和布朗运动等因素对热导率的影响, 提出了热导率修正模型.将修正模型预测值与实验值对比, 结果表明修正模型可以较为准确地计算出纳米流体的热导率. 关键词： 水热法 电导率 热导率 热导模型     

Influence of the particle size distribution on the thermal conductivity of nanofluids

In a previous study, we have obtained an equation to predict the thermal conductivity of nanofluids containing nanoparticles with conductive interface. The model is maximal particle packing dependent. In this study, the maximal packing is obtained as a function of the particle size distribution, which is the Gamma distribution. The thermal conductivity enhancement depends on the averaged particle size. Discussion concerning the influence of the suspension pH on the particle packing is made. The proposed model is evaluated using number of sets from the published experimental data to the thermal conductivity enhancement for different nanofluids.     

Enhanced thermal conductivity in nanofluids under the action of oscillating force fields

The thermal conductivity of nanoparticles colloidal suspensions, submitted to the action of an external force field has been calculated by nonequilibrium molecular dynamics simulations. For driven forces in the radio frequency and microwave ranges, we show that the thermal conductivity of nanofluids can be strongly enhanced without cluster formation.     

Application of SAXS to the study of particle-size-dependent thermal conductivity in silica nanofluids

Knowledge of the size and distribution of nanoparticles in solution is critical to understanding the observed enhancements in thermal conductivity and heat transfer of nanofluids. We have applied small-angle X-ray scattering (SAXS) to the characterization of SiO₂ nanoparticles (10–30 nm) uniformly dispersed in a water-based fluid using the Advanced Photon Source at Argonne National Laboratory. Size distributions for the suspended nanoparticles were derived by fitting experimental data to an established model. Thermal conductivity of the SiO₂ nanofluids was also measured, and the relation between the average particle size and the thermal conductivity enhancement was established. The experimental data contradict models based on fluid interfacial layers or Brownian motion but support the concept of thermal resistance at the liquid–particle interface.     

Comments on the effect of liquid layering on the thermal conductivity of nanofluids

This article provides critical examinations of two mathematical models that have been developed in recent years to describe the impact of nano-layering on the enhancement of the effective thermal conductivity of nanofluids. Discrepancy between the two models is found to be an artefact of an incorrect derivation used in one of the models. With correct formulation, both models predict effective thermal conductivity enhancements that are not significantly greater than those predicted by classical Maxwell theory. This study indicates that nano-layering by itself is unable to account for the effective thermal conductivity enhancements observed in nanofluids.     

A thermal conductivity model for nanofluids including effect of the temperature-dependent interfacial layer

The interfacial layer of nanoparticles has been recently shown to have an effect on the thermal conductivity of nanofluids. There is, however, still no thermal conductivity model that includes the effects of temperature and nanoparticle size variations on the thickness and consequently on the thermal conductivity of the interfacial layer. In the present work, the stationary model developed by Leong et al. (J Nanopart Res 8:245–254, 2006) is initially modified to include the thermal dispersion effect due to the Brownian motion of nanoparticles. This model is called the ‘Leong et al.’s dynamic model’. However, the Leong et al.’s dynamic model over-predicts the thermal conductivity of nanofluids in the case of the flowing fluid. This suggests that the enhancement in the thermal conductivity of the flowing nanofluids due to the increase in temperature does not come from the thermal dispersion effect. It is more likely that the enhancement in heat transfer of the flowing nanofluids comes from the temperature-dependent interfacial layer effect. Therefore, the Leong et al.’s stationary model is again modified to include the effect of temperature variation on the thermal conductivity of the interfacial layer for different sizes of nanoparticles. This present model is then evaluated and compared with the other thermal conductivity models for the turbulent convective heat transfer in nanofluids along a uniformly heated tube. The results show that the present model is more general than the other models in the sense that it can predict both the temperature and the volume fraction dependence of the thermal conductivity of nanofluids for both non-flowing and flowing fluids. Also, it is found to be more accurate than the other models due to the inclusion of the effect of the temperature-dependent interfacial layer. In conclusion, the present model can accurately predict the changes in thermal conductivity of nanofluids due to the changes in volume fraction and temperature for various nanoparticle sizes.