Modeling of thermal conductivity of nanofluids by modifying Maxwell’s equation using cell model approach

Nanofluid is an innovative heat transfer fluid with superior potential for enhancing the heat transfer performance of conventional fluids. Though many attempts have been made to investigate the abnormal high thermal conductivity of nanofluids, the existing models cannot precisely predict the same. An attempt has been made to develop a model for predicting the thermal conductivity of different types of nanofluids. The model presented here is derived based on the fact that thermal conductivity of nanofluids depends on thermal conductivity of particle and fluid as well as micro-convective heat transfer due to Brownian motion of nanoparticles. Novelty of the article lies in giving a unique equation which predicts thermal conductivity of nanofluids for different concentrations and particle sizes which also correctly predicts the trends observed in experimental data over a wide range of particle sizes, temperatures, and particle concentrations.     

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.     

Thermal conductance of nanofluids: is the controversy over?

Over the last decade nanofluids (colloidal suspensions of solid nanoparticles) sparked excitement as well as controversy. In particular, a number of researches reported dramatic increases of thermal conductivity with small nanoparticle loading, while others showed moderate increases consistent with the effective medium theories on well-dispersed conductive spheres. Accordingly, the mechanism of thermal conductivity enhancement is a hotly debated topic. We present a critical analysis of the experimental data in terms of the potential mechanisms and show that, by accounting for linear particle aggregation, the well established effective medium theories for composite materials are capable of explaining the vast majority of the reported data without resorting to novel mechanisms such as Brownian motion induced nanoconvection, liquid layering at the interface, or near-field radiation. However, particle aggregation required to significantly enhance thermal conductivity, also increases fluid viscosity rendering the benefit of nanofluids to flow based cooling applications questionable.     

Investigation of thermal transport in colloidal silica dispersions (nanofluids)

Thermal conductivity enhancement in colloidal silica dispersions (nanofluids) is investigated experimentally using a novel optical technique. The effects of nanoparticle size, concentration, and state of aggregation are examined. New data on well dispersed systems are compared to published data obtained using the more conventional transient hot-wire technique and good agreement was found. Experimental results are also compared with model predictions for relative thermal conductivity based on effective medium theory. For systems composed of larger diameter nanoparticles (~30 nm), good agreement was found between the measured thermal conductivity enhancement and that predicted by the classical Maxwell-Garnett model. For systems composed of smaller nanoparticles (∼10 and 20 nm), thermal conductivity enhancement was reduced by as much as 10%, presumably because interfacial thermal resistance effects become important. Measurements on two systems that were induced to form gels exhibited an increase in thermal conductivity of approximately 5% relative to the well-dispersed systems. The observed increase in thermal conductivity is larger than that predicted by a recently proposed model for aggregated nanofluids.     

The role of interfacial layers in the enhanced thermal conductivity of nanofluids: A renovated Hamilton–Crosser model

We previously developed a renovated Maxwell model for the effective thermal conductivity of nanofluids and determined that the solid/liquid interfacial layers play an important role in the enhanced thermal conductivity of nanofluids. However, this renovated Maxwell model is limited to suspensions with spherical particles. Here, we extend the Hamilton--Crosser model for suspensions of nonspherical particles to include the effect of a solid/liquid interface. The solid/liquid interface is described as a confocal ellipsoid with a solid particle. The new model for the three-phase suspensions is mathematically expressed in terms of the equivalent thermal conductivity and equivalent volume fraction of anisotropic complex ellipsoids, as well as an empirical shape factor. With a generalized empirical shape factor, the renovated Hamilton--Crosser model correctly predicts the magnitude of the thermal conductivity of nanotube-in-oil nanofluids. At present, this new model is not able to predict the nonlinear behavior of the nanofluid thermal conductivity.     

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.     

纳米流体热导率和粘度的分子动力学模拟计算

本文采用分子动力学(MD)模拟来计算纳米流体比较重要的热物性:热导率和粘度,与已有实验结果比较符合 较好,为进一步研究纳米流体传热效率提供了依据。     

Physics and mechanics of heat exchange processes in nanofluid flows

Nanofluids present a new type of dispersed fluids consisting of a carrier fluid and solid nanoparticles. Unusual properties of nanofluids, particularly high thermal conductivity, make them eminently suitable for many thermophysical applications, e.g., for cooling of equipment, designing of new heat energy transportation and production systems and so on. This requires a systematic study of heat exchange properties of nanofluids. The present paper contains the measurement results for the heat transfer coefficient of the laminar and turbulent flow of nanofluids on the basis of distilled water with silica, alumina and copper oxide particles in a minichannel with circular cross section. The maximum volume concentration of particles did not exceed 2%. The dependence of the heat transfer coefficient on the concentration and size of nanoparticles was studied. It is shown that the use of nanofluids allows a significant increase in the heat transfer coefficient as compared to that for water. However, the obtained result strongly depends on the regime of flow. The excess of the heat transfer coefficient in the laminar flow is only due to an increase in the thermal conductivity coefficient of nanofluid, while in the turbulent flow the obtained effect is due to the ratio between the viscosity and thermal conductivity of nanofluid. The viscosity and thermal conductivity of nanofluids depend on the volume concentration of nanoparticles as well as on their size and material and are not described by classical theories. That is why the literature data are diverse and contradictory; they do not actually take into account the influence of the mentioned factors (size and material of nanoparticles). It has been shown experimentally and by a molecular dynamics method that the nanofluid viscosity increases while the thermal conductivity decreases with the decreasing dispersed particle size. It is found experimentally for the first time that the nanofluid viscosity coefficient depends on the particle material. The higher is the density of particles, the higher is the thermal conductivity coefficient of nanofluid.     

Temperature Dependence of Thermal Conductivity of Nanofluids

Mechanism of thermal conductivity of nanofluids is analysed and calculated, including Brownian motion effects, particle agglomeration and viscosity, together influenced by temperature. The results show that only Brown- Jan motion as reported is not enough to describe the temperature dependence of the thermal conductivity of nanofluids. The change of particle agglomeration and viscosity with temperature are also important factors. As temperature increases, the reduction of the particle surface energy would decrease the agglomeration of nanopartieles, and the reduction of viscosity would improve the Brownish motion. The results egree well with the experimental data reported.     

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.     

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.     

Brownian dynamic simulation for the prediction of effective thermal conductivity of nanofluid

Nanofluid is a colloidal solution of nanosized solid particles in liquids. Nanofluids show anomalously high thermal conductivity in comparison to the base fluid, a fact that has drawn the interest of lots of research groups. Thermal conductivity of nanofluids depends on factors such as the nature of base fluid and nanoparticle, particle concentration, temperature of the fluid and size of the particles. Also, the nanofluids show significant change in properties such as viscosity and specific heat in comparison to the base fluid. Hence, a theoretical model becomes important in order to optimize the nanofluid dispersion (with respect to particle size, volume fraction, temperature, etc.) for its performance. As molecular dynamic simulation is computationally expensive, here the technique of Brownian dynamic simulation coupled with the Green Kubo model has been used in order to compute the thermal conductivity of nanofluids. The simulations were performed for different concentration ranging from 0.5 to 3 vol%, particle size ranging from 15 to 150 nm and temperature ranging from 290 to 320 K. The results were compared with the available experimental data, and they were found to be in close agreement. The model also brings to light important physical aspect like the role of Brownian motion in the thermal conductivity enhancement of nanofluids.     

Analytical prediction of forced convective heat transfer of fluids embedded with nanostructured materials (nanofluids)

Nanofluids are a new class of heat transfer fluids developed by suspending nanosized solid particles in liquids. Larger thermal conductivity of solid particles compared to the base fluid such as water, ethylene glycol, engine oil etc. significantly enhances their thermal properties. Several phenomenological models have been proposed to explain the anomalous heat transfer enhancement in nanofluids. This paper presents a systematic literature survey to exploit the characteristics of nanofluids, viz., thermal conductivity, specific heat and other thermal properties. An empirical correlation for the thermal conductivity of Al₂O₃ + water and Cu + water nanofluids, considering the effects of temperature, volume fraction and size of the nanoparticle is developed and presented. A correlation for the evaluation of Nusselt number is also developed and presented and compared in graphical form. This enhanced thermophysical and heat transfer characteristics make fluids embedded with nanomaterials as excellent candidates for future applications.      

Effect of Clusters on Thermal Conductivity in Nanofluids

We propose a new model for the effective thermal conductivities of nanottuids, which is derived from the fact that nanoparticles and clusters coexist in the fluids. The effects of the compactness and the perfectness of the contact between nanoparticles in clusters on the effective thermal conductivity of nanofluids are analysed. The proposed model indicates that the effective thermal conductivity of nanofluids decreases with the increasing concentration of clusters. The model predictions are compared with and are in good agreement with the available experimental data.     

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 model for the thermal conductivity of nanofluids – the effect of interfacial layer

A model for predicting the effective thermal conductivity of nanofluids is proposed. It has been documented that the interfacial layer at the solid (particle)/liquid interface and particle size is one of the major mechanisms for enhancing the thermal conductivity of nanofluids. Comparing with other classical models, the proposed model takes into account some additional effects including volume fraction, thickness, thermal conductivity of the interfacial layer and particle size. The proposed model is found to be better than the existing models since the predicted effective thermal conductivity of different types of nanofluids are closer to the experimental results.     

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.     

Particle agglomeration and properties of nanofluids

The present study demonstrates the importance of actual agglomerated particle size in the nanofluid and its effect on the fluid properties. The current work deals with 5 to 100 nm nanoparticles dispersed in fluids that resulted in 200 to 800 nm agglomerates. Particle size distributions for a range of nanofluids are measured by dynamic light scattering (DLS). Wet scanning electron microscopy method is used to visualize agglomerated particles in the dispersed state and to confirm particle size measurements by DLS. Our results show that a combination of base fluid chemistry and nanoparticle type is very important to create stable nanofluids. Several nanofluids resulted in stable state without any stabilizers, but in the long term had agglomerations of 250 % over a 2 month period. The effects of agglomeration on the thermal and rheological properties are presented for several types of nanoparticle and base fluid chemistries. Despite using nanodiamond particles with high thermal conductivity and a very sensitive laser flash thermal conductivity measurement technique, no anomalous increases of thermal conductivity was measured. The thermal conductivity increases of nanofluid with the particle concentration are as those predicted by Maxwell and Bruggeman models. The level of agglomeration of nanoparticles hardly influenced the thermal conductivity of the nanofluid. The viscosity of nanofluids increased strongly as the concentration of particle is increased; it displays shear thinning and is a strong function of the level of agglomeration. The viscosity increase is significantly above of that predicted by the Einstein model even for very small concentration of nanoparticles.     

Effect of Nanoparticles on Thermal Properties Enhancement in Different Oils – A Review

The development of stable dispersion of nanoparticles in different oils is gaining momentum for close circuit applications as most of the mineral oils used are not very good thermal conductors. The enhancement of thermal conductivity with optimum enhancement of viscosity of oil with nanoparticles poses a serious challenge for use of such fluids in cooling. Transformer oil, mineral oil, silicon oil, hydrocarbon fuels, biodiesel, and some organic solutions have been used as the base fluids for studying the effect of nanoparticles for improving thermal efficiency. Innovative heat transfer fluids are produced by suspending metallic or nonmetallic nanometer-sized solid particles. Although a large number of sources are available on water-based nanofluids, the number of such reports on oil-based nanofluids is rather limited. The aim of this article is to summarize recent developments on the preparation methods of nanofluids based on oil, its stability, thermal conductivity enhancement, nanoparticle effect on viscosity, heat transfer characteristics, breakdown voltage, dielectric properties, and applications of such nanofluids.