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.     

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.     

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.     

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.     

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.     

Fabrication, characterization and measurement of thermal conductivity of Fe3O4 nanofluids

Magnetite Fe₃O₄ nanoparticles were synthesized by a co-precipitation method at different pH values. The products were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and transmission electronic microscopy. Their magnetic properties were evaluated on a vibrating sample magnetometer. The results show that the shape of the particles is cubic and they are superparamagnetic at room temperature. Magnetic nanofluids were prepared by dispersing the Fe₃O₄ nanoparticles in water as a base fluid in the presence of tetramethyl ammonium hydroxide as a dispersant. The thermal conductivity of the nanofluids was measured as a function of volume fraction and temperature. The results show that the thermal conductivity ratio of the nanofluids increases with increase in temperature and volume fraction. The highest enhancement of thermal conductivity was 11.5% in the nanofluid of 3 vol% of nanoparticles at 40 °C. The experimental results were also compared with the theoretical models.     

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     

An experimental correlation approach for predicting thermal conductivity of water-EG based nanofluids of zinc oxide

In this study, the effects of temperature (20 °C<T<50 °C) and volume fracti°n (0<φ<4%) on the thermal conductivity of zinc oxide/ethylene glycol-water nanofluid have been presented. Nanofluid samples were prepared by a two-step method and thermal conductivity measurements were performed by a KD2 pro instrument. Results showed that the thermal conductivity increases uniformly with increasing solid volume fraction and temperature. The results also revealed that the thermal conductivity of nanofluids significantly increases with increasing solid volume fraction at higher temperatures. Moreover, it can be seen that for more concentrated samples, the effect of temperature was more tangible. Experimental thermal conductivity enhancement of the nanofluid in comparison with the Maxwell model indicated that Maxwell model was unable to predict the thermal conductivity of the present nanofluid. Therefore, a new correlation was presented for predicting the thermal conductivity of ZnO/EG-water nanofluid.     

抛物槽式太阳能集热器内合成油基纳米流体传热特性的数值模拟

对Al₂O₃-合成油纳米流体在槽式太阳能集热管内的传热特性进行流体动力学数值模拟,重点考察纳米流体导热系数模型的影响。通过与管内Nusselt数半经验模型的预测结果对比,表明使用考虑布朗运动的纳米流体导热系数模型可较好地预测集热管内传热特性。研究表明纳米颗粒与流体基液的相对运动具有促进集热管内传热的作用。最后,定量研究纳米颗粒添加量对提高基础流体平均传热系数的影响,显示纳米流体在太阳能集热器中具有巨大应用潜力。     

Overview of Hybrid Nanofluids Development and Benefits

Conventional fluids have poor heat transfer properties, but their vast applications in power generation, chemical processes, heating and cooling processes, electronics and other microsized applications make the reprocessing of those thermofluids to have better heat transfer properties quite essential. Recently, it has been shown that the addition of solid nanoparticles to various fluids can increase the thermal conductivity and can influence the viscosity of the suspensions by tens of percent. Thermophysical properties of nanofluids were shown dependent on the particle material, shape, size, concentration, the type of the base fluid, and other additives. In spite of some inconsistency in the reported results and insufficient understanding of the mechanism of the heat transfer in nanofluids, it has been emerged as a promising heat transfer fluid. In the continuation of nanofluids research, the researchers have also tried to use hybrid nanofluid recently, which is engineered by suspending dissimilar nanoparticles either in mixture or composite form. The idea of using hybrid nanofluids is to further improve the heat transfer and pressure drop characteristics by trade-off between advantages and disadvantages of individual suspension, attributed to good aspect ratio, better thermal network and synergistic effect of nanomaterials. As a conclusion, the hybrid nanofluids containing composite nanoparticles yield significant enhancement of thermal conductivity. However, the long-term stability, production process, selection of suitable nanomaterials combination to get synergistic effect and cost of nanofluids may be major challenges behind the practical applications.     

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.     

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.     

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%.     

Experimental data on the dependence of the viscosity of water- and ethylene glycol-based nanofluids on the size and material of particles

The viscosities of all nanofluids considered are shown to be dependent on the size of nanoparticles. It has been established that the greater the nanofluid viscosity, the smaller the size of particles. The measurements carried out in this study make it possible for the first time to fix experimentally the fact of the dependence of the viscosity of nanofluids on the particle material.     

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.     

Convective Heat Transfer of a Cu/Water Nanofluid Flowing Through a Circular Tube

Abstract

Fluids in which nanometer-sized solid particles are suspended are called nanofluids. These fluids can be employed to increase the heat transfer rate in various applications. In this study, the convective heat transfer for Cu/water nanofluid through a circular tube was experimentally investigated. The flow was laminar, and constant wall temperature was used as thermal boundary condition. The Nusselt number of nanofluids for different nanoparticle concentrations, as well as various Peclet numbers, was obtained. Also, the rheological properties of the nanofluid for different volume fractions of nanoparticles were measured and compared with theoretical models. The results show that the heat transfer coefficient is enhanced by increasing the nanoparticle concentrations as well as the Peclet number.     

Influence of pH on Nanofluids' Viscosity and Thermal Conductivity

Aiming at the dispersion stability of nanopartieles regarded as the guide of heat transfer enhancement, we investigate the viscosity and the thermal conductivity of Cu and Al2O3 nanoparticles in water under different pH values. The results show that there exists an optimal pH value for the lowest viscosity and the highest thermal conductivity, and that at the optimal pH value the nanofluids containing a small amount of nanoparticles have noticeably higher thermal conductivity than that of the base fluid without nanoparticles. For the two nanofluids the enhancements of thermal conductivity are observed up to 13% （Al2O3-water） or 15% （Cu-water） at 0.4 wt%, respectively. Therefore, adjusting the pH values is suggested to improve the stability and the thermal conductivity for practical applications of nanofluid.     

Dependence of the viscosity of nanofluids on nanoparticle size and material

The viscosity of nanofluids as a function of nanoparticle size and material is modeled and analyzed. Dependences of the viscosity of nanofluids based on liquid argon with aluminum and lithium nanoparticles are obtained. The nanoparticle size ranges from 1 to 4 nm. The volume concentration of nanoparticles is varied from 1% to 12%. It is shown that the viscosity of the nanofluid increases with decreasing nanoparticle size and, in addition, depends on the nanoparticle material.     

纳米颗粒聚集形态对纳米流体导热系数的影响

纳米流体中悬浮的纳米颗粒可以增强其导热性能已经得到广泛认可,然而纳米流体颗粒增强传热的机理目前尚不清楚.研究表明,纳米颗粒的聚集是纳米流体导热系数增大的重要机制,而且纳米颗粒聚集的形态对纳米流体的导热系数有重要影响,但是目前的导热系数模型大多是建立在Maxwell有效介质理论的"静态"和"均匀分散"假设基础上.本文用平衡分子动力学模拟Cu-Ar纳米流体,采用Green-Kubo公式计算导热系数,采用Schmidt-Ott关系式计算不同聚集形态下的分形维数.对比导热系数与分形维数可以发现:在相同体积分数下,较低的分形维数会有更高的导热系数,分析了分形维数与导热系数的定量关系.此外,通过径向分布函数可以看出纳米颗粒紧密聚集与松散聚集的差异,基液分子在纳米颗粒附近的纳米薄层中处于动态平衡状态.研究结果有助于理解纳米颗粒聚集形态对导热系数的影响机理.