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.     

颗粒表面吸附层对纳米流体导热系数贡献的分子动力学研究

本文采用平衡分子动力学方法研究了纳米流体中固体颗粒周围的吸附层厚度及结构,并通过Green-Kubo原理计算了吸附层内导热系数的分布。研究表明,纳米颗粒周围存在0.5 nm厚的吸附层,层内原子数密度提高了50%~60%,导热系数提高了200%~300%。分析发现,吸附层导热系数的提高是由于原子数密度及吸附层内原子排列有序程度的提高引起的。该研究结果有望为建立合理的预测纳米流体的导热系数模型提供关键参数及理论指导。     

一种纳米流体热导率通用模型

本文提出预测纳米流体热导率的通用模型,该模型可以预测含球形纳米颗粒的纳米流体或含纳米管的纳米流体的热导率。模型首先计算纳米粉体团聚体的热导率与团聚体在流体中的体积分数,进而计算得到纳米流体的热导率。将通用模型的预测结果、已有纳米流体热导率模型的预测结果与实验数据进行比较,结果表明97%的通用模型预测结果与实验数据的误差在10%以内,平均误差为2.8%。通用模型的精度超过已有的纳米流体热导率模型,且具有很好的通用性。     

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。     

纳米流体导热系数的分子动力学模拟

纳米流体的导热系数相对于基础流体而言有了显著提高,然而这种现象却无法用现有理论进行解释。应用平衡分子动力学方法对纳米流体的导热系数进行了模拟,并研究了纳米颗粒表面类似于固体的液体吸附层的存在和特性以及其对纳米流体导热能力的影响,在此基础上进一步对纳米流体导热性能显著提高的微观机理进行了探讨。模拟结果表明由于固体分子较强的吸引力,一部分液体分子被吸附在固体颗粒表面形成一个薄层,薄层内分子的排列结构不同于纯液体的分子排列结构,导热性能也优于基础流体,从而使得纳米流体的导热系数有了显著提高。最后对纳米颗粒表面液体薄层的厚度进行了定量计算。     

纳米流体介质导热机理初探

纳米流体导热行为具有许多奇异的特性，结合纳米流体的特点和微尺度传热学原理，研究了 热流在纳米颗粒内波动式及非限域的热传导特性、纳米颗粒在悬浮液内的布朗运动、颗粒- 液体界面上液膜层原子的有规则排列、以及纳米颗粒的团簇形成及移动等四方面因素对纳米 流体导热系数的影响. 关键词： 纳米流体 导热     

纳米流体对流换热系数增大机理

纳米流体流动换热能力优于传统流体介质.研究了纳米流体热物性的提升和热散射对其对流换热系数的影响.结果表明,纳米颗粒的加入,优化了介质的热物性,增大了导热系数,强化了纳米流体内颗粒、流体以及流道管壁碰撞和相互作用,同时加强了流体的混合脉动和湍流,从而增大了对流换热系数. 关键词： 纳米流体 换热系数 热散射     

羧基化碳纳米管/水纳米流体核沸腾传热研究

将羧基基团引入多壁碳纳米管,改善了碳纳米管在水中的分散性及稳定性。同时研究了不同质量浓度纳米流体的导热系数、加热表面颗粒沉积、接触角变化对核沸腾传热性能的影响。结果表明;羧基化碳纳米流体可强化核沸腾传热。在测试浓度范围内,强化率在低热通时,随着热通量的增加急剧增大,高热通时,趋于稳定;当质量比ω为0.10%,功率为210.6 kW.m~(-2)时,强化率达到最大为138.3%;流体的导热系数随着质量浓度的增大而增大,0.15%浓度导热系数是纯水的1.18倍。分析认为纳米流体表面张力,纳米颗粒沉积,纳米颗粒扰动和导热系数的变化均是影响水基羧基化碳纳米流体沸腾的因素。结论由0.05%的纳米流体沸腾过程高速成像得到验证。     

方腔内Cu/Al2O3水混合纳米流体自然对流的格子Boltzmann模拟

纳米流体作为一种较高的导热介质,广泛应用于各个传热领域.鉴于纳米颗粒导热系数和成本之间的矛盾,本文提出了一种混合纳米流体.为了研究混合纳米流体颗粒间相互作用机理和自然对流换热特性,在考虑颗粒间相互作用力的基础上,利用多尺度技术推导了纳米流体流场和温度场的格子Boltzmann方程,通过耦合流动和温度场的演化方程,建立了Cu/Al2O3水混合纳米流体的格子Boltzmann模型,研究了混合纳米流体颗粒间的相互作用机理和纳米颗粒在腔体内的分布.发现在颗粒间相互作用力中,布朗力远远大于其他作用力,温差驱动力和布朗力对纳米颗粒的分布影响最大.分析了纳米颗粒组分、瑞利数对自然对流换热的影响,对比了混合纳米流体(Cu/Al2O3-水)与单一金属颗粒纳米流体(Al2O3-水)的自然对流换热特性,发现混合纳米流体具有更强的换热特性.     

方腔内Cu/Al2O3水混合纳米流体自然对流的格子Boltzmann模拟

纳米流体作为一种较高的导热介质, 广泛应用于各个传热领域. 鉴于纳米颗粒导热系数和成本之间的矛盾, 本文提出了一种混合纳米流体. 为了研究混合纳米流体颗粒间相互作用机理和自然对流换热特性, 在考虑颗粒间相互作用力的基础上, 利用多尺度技术推导了纳米流体流场和温度场的格子Boltzmann方程, 通过耦合流动和温度场的演化方程, 建立了Cu/Al₂O₃水混合纳米流体的格子Boltzmann模型, 研究了混合纳米流体颗粒间的相互作用机理和纳米颗粒在腔体内的分布. 发现在颗粒间相互作用力中, 布朗力远远大于其他作用力, 温差驱动力和布朗力对纳米颗粒的分布影响最大. 分析了纳米颗粒组分、瑞利数对自然对流换热的影响, 对比了混合纳米流体(Cu/Al₂O₃-水)与单一金属颗粒纳米流体(Al₂O₃-水)的自然对流换热特性, 发现混合纳米流体具有更强的换热特性.     

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.     

Modified Maxwell model for predicting thermal conductivity of nanocomposites considering aggregation

The effect of nanoparticle aggregation on the thermal conductivity of nanocomposites or nanofluids is typically nonnegligible. A universal model(Maxwell model) including nanoparticle aggregation is modified in order to predict the thermal conductivity of nanocomposites more accurately. The predicted thermal conductivities of silica and titania nanoparticle powders are compared first with that measured by a hot-wire method and then with those in previous experimental works.The results show that there is good agreement between our model and experiments, and that nanoparticle aggregation in a nanocomposite enhances the thermal conductivity greatly and should not be ignored. Because it considers the effect of aggregation, our model is expected to yield precise predictions of the thermal conductivity of composites.     

Molecular dynamics simulation of thermal conductivity of Cu–Ar nanofluid using EAM potential for Cu–Cu interactions

Mechanism of heat conduction in copper-argon nanofluids is studied by molecular dynamics simulation and the thermal conductivity was obtained using the Green–Kubo method. While the interatomic potential between argon atoms is described using the well-known Lennard–Jones (L–J) potential, a more accurate embedded atom method (EAM) potential is used in describing the interatomic interaction between copper atoms. It is found that the heat current autocorrelation function obtained using L–J potential to describe the copper-copper interatomic interaction fluctuates periodically due to periodic oscillation of the instantaneous microscopic heat fluxes. Thermal conductivities of nanofluids using EAM potentials were calculated with different volume fractions but the same nanoparticle size. The results show that thermal conductivity of nanofluids are almost a linear function of the volume fraction and slightly higher than the results predicted by the conventional effective media theory for a well-dispersed solution. A solid-like base fluid liquid layer with a thickness of 0.6 nm was found in the simulation and this layer is believed to account for the small discrepancy between the results of MD simulation and the conventional effective media theory.     

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.     

Modeling of nanoparticles’ aggregation and sedimentation in nanofluid

The aggregation and sedimentation of nanoparticles in nanofluid have significant influences on the stability and applicability of nanofluids. The objective of this study is to propose a model to predict the nanoparticles’ aggregation and sedimentation characteristics. The characteristics are evaluated by the concentration of nanoparticles in nanofluid at different time. The concentration of nanoparticles can be calculated according to the speed and location of each nanoparticle. Then, the speed and location of each nanoparticle can be yielded when the forces on each nanoparticle are determined. For the forces on nanoparticles are related to the space structure of nanoparticle clusters, the clusters’ space structures are simulated. Case study shows that the mean deviation of predicted nanoparticle concentration from experimental data for Fullerence + H₂O, Fullerence + Oil and CuO + Oil nanofluids are 25%, 16% and 13%, respectively. The model can provide quantitative prediction of the aggregation and sedimentation characteristics of nanoparticles in nanofluid.     

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.     

Thermal conductivity of nanofluids and size distribution of nanoparticles by Monte Carlo simulations

Nanofluids, a class of solid–liquid suspensions, have received an increasing attention and studied intensively because of their anomalously high thermal conductivites at low nanoparticle concentration. Based on the fractal character of nanoparticles in nanofluids, the probability model for nanoparticle’s sizes and the effective thermal conductivity model are derived, in which the effect of the microconvection due to the Brownian motion of nanoparticles in the fluids is taken into account. The proposed model is expressed as a function of the thermal conductivities of the base fluid and the nanoparticles, the volume fraction, fractal dimension for particles, the size of nanoparticles, and the temperature, as well as random number. This model has the characters of both analytical and numerical solutions. The Monte Carlo simulations combined with the fractal geometry theory are performed. The predictions by the present Monte Carlo simulations are shown in good accord with the existing experimental data.     

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.     

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.     

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.