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.     

Effect of Brownian motion on flow and heat transfer of nanofluids over a backward-facing step with and without adiabatic square cylinder

A mathematical model to predict large enhancement of thermal conductivity of nanofluids by considering the Brownian motion is proposed. The effect of the Brownian motion on the flow and heat transfer characteristics is examined. The computations were done for various types of nanoparticles such as CuO, Al₂O₃, and ZnO dispersed in a base fluid (water), volume fraction of nanoparticles ? in the range of 1 % to 6 % at a fixed Reynolds number Re = 450 and nanoparticle diameter d_np = 30 nm. Our results demonstrate that Brownian motion could be an important factor that enhances the thermal conductivity of nanofluids. Nanofluid of Al₂O₃ is observed to have the highest Nusselt number Nu among other nanofluids types, while nanofluid of ZnO nanoparticles has the lowest Nu. Effects of the square cylinder on heat transfer characteristics are significant with considering Brownian motion. Enhancement in the maximum value of Nu of 29 % and 26 % are obtained at the lower and the upper walls of the channel, respectively, by considering the Brownian effects, with square cylinder, compared with that in the case without considering the Brownian motion. On the other hand, results show a marked improvement in heat transfer compared to the base fluid, this improvement is more pronounced on the upper wall for higher ?.     

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...     

Diffusion of Overheated and Overcooled Particles as a Mechanism of Thermal Conductivity in Nanofluids

JETP Letters - A new mechanism of heat transfer in nanofluids is proposed on the basis of two physical principles: Brownian motion of particles in a fluid and thermal resistance of a...     

纳米悬浮液的有效导热系数

文中以有效介质近似理论为基础,考虑了纳米颗粒在基液中强烈的B rown ian运动对强化传热的作用和纳米颗粒的表面吸附液体层、纳米颗粒的粒径和体积分数对纳米悬浮液有效导热系数的影响,建立了预测纳米悬浮液有效导热系数的模型,通过对纳米CuO-去离子水溶液的验证,发现该模型比几种经典模型具有更高的精度,因此具有一定的参考价值。     

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     

纳米流体的聚集结构和导热系数模拟

本文根据布朗运动理论模拟纳米粒子在流体中的聚集过程，运用分形理论描述纳米粒子团的结构．考虑纳米粒子的运动传热，建立纳米流体的导热系数模型，理论预测值与实验结果显现了良好的一致性。     

纳米流体对流换热机理分析

考虑在纳米流体中纳米颗粒做布朗运动引起的对流换热, 基于纳米颗粒在纳米流体中遵循分形分布, 本文得到纳米流体对流换热的机理模型. 本解析模型没有增加新的经验常数, 从该模型发现纳米流体池沸腾热流密度是温度、纳米颗粒的平均直径、 纳米颗粒的浓度、纳米颗粒的分形维数、沸腾表面活化穴的分形维数、基本液体的物理特性的函数. 对不同的纳米颗粒浓度和不同的纳米颗粒平均直径与不同的实验数据进行了比较, 模型预测的结果与实验结果相吻合. 所得的解析模型可以更深刻地揭示纳米流体对流换热的物理机理.     

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.     

Subcooled pool boiling heat transfer in fractal nanofluids:A novel analytical model

A novel analytical model to determine the heat flux of subcooled pool boiling in fractal nanofluids is developed. The model considers the fractal character of nanofluids in terms of the fractal dimension of nanoparticles and the fractal dimen- sion of active cavities on the heated surfaces; it also takes into account the effect of the Brownian motion of nanoparticles, which has no empirical constant but has parameters with physical meanings. The proposed model is expressed as a function of the subcooling of fluids and the wall superheat. The fractal analytical model is verified by a reasonable agreement with the experimental data and the results obtained from existing models.     

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.     

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     

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.     

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.     

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.     

A fractal model for heat transfer of nanofluids by convection in a pool

Based on the fractal distribution of nanoparticles, a fractal model for heat transfer of nanofluids is presented in the Letter. Considering heat convection between nanoparticles and liquids due to the Brownian motion of nanoparticles in fluids, the formula of calculating heat flux of nanofluids by convection is given. The proposed model is expressed as a function of the average size of nanoparticle, concentration of nanoparticle, fractal dimension of nanoparticle, temperature and properties of fluids. It is shown that the fractal model is effectual according to a good agreement between the model predictions and experimental data.     

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.     

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.     

Thermal conductivity of nanoscale colloidal solutions (nanofluids)

Researchers have been perplexed for the past five years with the unusually high thermal conductivity (k) of nanoparticle-laden colloidal solutions (nanofluids). Although various mechanisms and models have been proposed in the literature to explain the high k of these nanofluids, no concrete conclusions have been reached. Through an order-of-magnitude analysis of various possible mechanisms, we show that convection caused by the Brownian movement of these nanoparticles is primarily responsible for the enhancement in k of these colloidal nanofluids.     

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.