金刚石/环氧树脂复合物热导率的分子动力学模拟

提高环氧树脂热界面材料热导率对解决5G等微电子芯片高热流密度散热问题具有重要意义.采用非平衡态分子动力学方法,重点研究了纳米金刚石填料的不同填充方式对环氧树脂基复合物热导率的影响.结果表明,单颗粒填充方式下,复合物热导率随金刚石尺寸的增大而增大,大尺寸金刚石填料可以降低复合物的自由体积分数,对热导率的提升效果更显著;多颗粒填充方式下,复合物热导率随颗粒数的增多呈先增大后减小的趋势,增加颗粒数可以减小复合物的自由体积分数,但具有更大的比表面积及界面热阻,其对热导率的削弱作用更为显著.此外,同一质量分数下,增大纳米金刚石颗粒尺寸比增加颗粒数对复合物热导率的提升效果更为显著.本文研究对具有高热导率的纳米金刚石/环氧树脂复合物热界面材料的设计和制备具有指导意义.     

碳纳米流体强化传热研究

在去离子水中分别添加了单壁、多壁碳纳米管材料,通过配比一定质量的亲水性分散剂,经超声波振荡试制了水基碳纳米流体。测试分析了不同碳纳米管材料质量分数下的纳米流体热导率和动力粘度等重要热物性参数。测试结果表明:碳纳米管粒子能够强化基液工质导热性能,随着其质量分数的增加,水基单壁、多壁碳纳米流体热导率均明显提高;单壁碳纳米流体粘度显著增加,多壁碳纳米流体粘度无明显变化,多壁碳管更适用于纳米流体强化传热。     

碳纳米流体强化传热研究

在去离子水中分别添加了单壁、多壁碳纳米管材料,通过配比一定质量的亲水性分散剂,经超声波振荡试制了水基碳纳米流体。测试分析了不同碳纳米管材料质量分数下的纳米流体热导率和动力粘度等重要热物性参数。测试结果表明:碳纳米管粒子能够强化基液工质导热性能,随着其质量分数的增加,水基单壁、多壁碳纳米流体热导率均明显提高;单壁碳纳米流体粘度显著增加,多壁碳纳米流体粘度无明显变化,多壁碳管更适用于纳米流体强化传热。     

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。     

La1-xCaxMnO3材料的热导与电导

实验研究了两个典型掺杂的La1-xCaxMnO3(x=0.3和x=0.6)样品的热导率与电导率随温度的变化关系,测量温区为77－300K。样品的热导率与电导率的温度曲率之间有着很大的相似性：在高温部分,两个样品的电导率均随温度降低而下降,热导率也呈现类似的温度关系;在低温部分,两个样品的电导率呈现相反的温度关系,热导率的变化也截然不同。而且,在La1-xCaxMnO3多晶材料中,电导率很小,使得电子直接贡献的热导部分微乎其微。分析指出,La1-xCaxMnO3巨磁阻材料中由Jahn-Teller效应引起的晶格畸变,受到电子状态的直接影响和制约,从而对热导产生不同的作用。     

Ag-ZnO纳米复合热电材料的制备及其性能研究

ZnO是一类具有潜力的热电材料, 但其较大声子热导率影响了热电性能的进一步提高. 纳米复合是降低热导率的有效途径. 本文以醋酸盐为前驱体, 溶胶-凝胶法制备了Ag-ZnO纳米复合热电材料. 扫描电镜照片显示ZnO颗粒呈现多孔结构, Ag纳米颗粒分布于ZnO的晶粒之间. Ag-ZnO纳米复合材料的电导率比未复合ZnO材料高出100倍以上, 而热导率是未复合ZnO材料的1/2. 同时, 随着Ag添加量的增加, 赛贝克系数的绝对值逐渐减小. 综合以上原因, 添加7.5%mol Ag的Ag-ZnO纳米复合材料在700 K时的热电优值达到0.062, 是未复合ZnO材料的约25倍. 在ZnO基体中添加导电金属颗粒有利于产生导电逾渗通道, 提高材料体系的电导率, 但同时导致赛贝克系数的绝对值减小. 总热导率的差异来源于声子热导率的差异. 位于ZnO晶界的纳米Ag颗粒, 有利于降低声子热导率. 关键词： 热电材料 ZnO 纳米复合 热导率     

铝纳米颗粒的热物性及相变行为的分子动力学模拟

采用分子动力学方法模拟了纳米金属铝在粒径为0.8-3.2 nm 时的熔点、密度和声子热导率的变化, 研究了粒径为1.6 nm的铝纳米颗粒的密度、比热和声子热导率随温度的变化. 采用原子嵌入势较好地模拟了纳米金属铝的热物性及相变行为, 根据能量-温度曲线和比热容-温度曲线对铝纳米颗粒的相变温度进行了研究, 并利用表面能理论、尺寸效应理论对铝纳米颗粒熔点的变化进行了分析. 随着纳米粒径的不断增大, 铝纳米颗粒的熔点呈递增状态, 当粒径在2.2-3.2 nm时, 熔点的增幅减缓, 但仍处于递增趋势. 随着纳米粒径的增大, 铝纳米颗粒的密度呈单调递减, 热导率则呈线性单调递增, 且热导率的变化情况符合声子理论. 随着温度的升高, 粒径为1.6 nm的铝纳米颗粒的密度、热导率均减小. 该模拟从微观原子角度对纳米材料的热物性进行了研究, 对设计基于铝纳米颗粒的相变材料具有指导意义.     

纳米尺度下流体剪切流动的分子动力学模拟

利用分子动力学方法模拟流体在两无限大平板间剪切流动过程。研究通道内加入不同体积分数纳米颗粒、体积分数相同纳米颗粒数目不同以及剪切速度对流体密度、速度以及界面滑移的影响。结果表明:近壁区流体数密度呈衰减振荡分布,由近壁区到主流区振幅逐渐减小,颗粒和流体的整体数密度在中心主流区呈抛物线分布。流体剪切应变率随颗粒体积分数的增加逐渐减小,同时剪切应变率和滑移速度之间呈近似线性分布。体积分数相同颗粒数目不同,颗粒在运动过程中呈线,性排列时,剪切应变率最大。随剪切速度的增加,流体滑移速度和滑移长度随之增大,但滑移长度增加量相对较小。     

SiO_2-导热油纳米流体的黏度研究

实验制备了稳定性良好的SiO_2-导热油纳米流体,通过旋转黏度计对纳米流体的黏度进行了测量,研究了旋转黏度计转速、纳米颗粒体积分数以及温度对纳米流体黏度的影响。结果表明随着纳米SiO_2体积分数增加,纳米流体黏度迅速增加,且逐渐从牛顿流体转变为非牛顿流体;而随着温度的升高,纳米流体黏度迅速减小。通过对实验数据的分析,提出了SiO_2-导热油纳米流体黏度计算经验公式。     

基于量子修正的石墨烯纳米带热导率分子动力学表征方法

本文提出了基于量子修正的非平衡态分子动力学模型,可用于石墨烯纳米带热导率的表征.利用该模型对不同温度下,不同手性及宽度的石墨烯纳米带热导率进行了研究,结果发现:相较于经典分子动力学模型给出的热导率随温度升高而单调下降的结论,在低于Debye温度的情况下,量子修正模型的计算结果出现了反常现象.本文研究还发现,石墨烯纳米带的热导率呈现出明显的边缘效应及尺度效应:锯齿型石墨烯纳米带的热导率明显高于扶手椅型石墨烯纳米带;全温段的热导率及热导率在低温段随温度变化的斜率均随宽度的增加而增大.最后,文章用Boltzmann声子散射理论对低温段的温度效应及尺度效应进行了阐释,其理论分析结果说明文章所建模型适合在全温段范围内对不同宽度和不同手性的热导率进行精确计算,可为石墨烯纳米带在传热散热领域的应用提供理论计算和分析依据.     

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...     

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.     

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.     

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.     

A study on the DC-electrical and thermal conductivities of epoxy/ZnO composites doped with carbon black

Thermo-electrical characterizations of hybrid polymer composites, made of epoxy matrix filled with various zinc oxide (ZnO) concentrations (0, 4.9, 9.9, 14.9, and 19.9 wt%), and reinforced with conductive carbon black (CB) nanoparticles (0.1 wt%), have been investigated as a function of ZnO concentration and temperature. Both the measured DC-electrical and thermal conductivities showed ZnO concentration and temperature dependencies. Increasing the temperature and filler concentrations were reflected in a negative temperature coefficient of resistivity and enhancement of the electrical conductivity as well. The observed increase in the DC conductivity and decrease in the determined activation energy were explained based on the concept of existing paths and connections between the ZnO particles and the conductive CB nanoparticles. Alteration of ZnO concentration with a fixed content of CB nanoparticles and/or temperature was found to be crucial in the thermal conductivity behavior. The addition of CB nanoparticles to the epoxy/ZnO matrix was found to enhance the electrical conduction resulting from the electronic and impurity contributions. Also, the thermal conductivity enhancement was mostly attributed to the heat transferred by phonons and electrons hopping to higher energy levels throughout the thermal processes. Scanning electron microscopy and energy-dispersive spectroscopy were used to observe the morphology and elements’ distribution in the composites. The observed thermal conductivity behavior was found to correlate well with that of the DC-electrical conductivity as a function of the ZnO content. The overall enhancements in both the measured DC- and thermal conductivities of the prepared hybrid composites are mainly produced through mutual interactions between the filling conductive particles and also from electrons tunneling in the composite's bulk as well.     

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。     

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.     

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 ?.     

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.