纳米流体导热系数实验研究进展

导热系数是反映介质换热能力的主要参数,具有重要的理论和应用意义。详细介绍国内外纳米流体制备及其导热系数的研究进展情况,对比和总结国内外研究者的研究结果。表明:纳米流体能显著提高基液的导热系数,但不同的研究者对于纳米流体导热系数的研究得出的结果存在一定差异,有待于进一步的深入研究。     

瞬态热丝法测量纳米流体的导热系数

推导了用双铂丝测量流体导热系数的瞬态热丝法的实验关联式,并据此设计实验装置,分析误差因素,给出了系统误差修正系数.用该装置测量了几种纳米流体的导热系数并作了相关分析.     

基于粒子吸附层的纳米流体有效导热系数模型

基于纳米粒子表面吸附层的分析,构建包含纳米粒子、分散剂层、类固相层和分散基液的导热单元,利用最小热阻法建立纳米流体的有效导热系数模型,推导出其表达式,并分别讨论纳米粒子粒径、分散剂层和类固相层对纳米流体的有效导热系数的影响。结果表明,考虑类固相层的影响得到的纳米流体的有效导热系数比不考虑其影响得到的数值大;添加分散剂后,纳米流体的有效导热系数随着分散剂导热系数的增大而增大,当纳米粒子较小时分散剂在纳米粒子表面的吸附层对纳米流体导热系数的影响更大。     

固相纳米复合材料的导热系数预测

研究了分散剂和纳米颗粒对固相纳米复合材料导热系数的影响。假设纳米颗粒在纳米流体中均匀分布,构建了一个考虑纳米颗粒尺度和分散介质影响的物理分析模型;并由此利用最小热阻力法则和比等效导热系数相等法则,建立了一个固相纳米复合材料的导热系数理论模型。计算结果表明,颗粒的体积分数和导热系数、以及分散剂导热系数的增大,都会引起复合材料导热系数的增大。     

影响纳米流体导热系数实验测试的因素

导热系数是衡量纳米流体强化换热的最重要的参数,但在不同学者的研究中,对于同一种纳米流体所测得的导热系数却有很大差别。本文针对影响纳米流体导热系数实验测量的因素进行研究,在相同的实验条件下,分别运用Hotdisk导热仪和闪光法导热仪对水基二氧化钛纳米流体的导热系数进行了测量。实验结果表明,用Hotdisk法的测量结果比用闪光法测得的高21%～34%。通过计算分析发现:自然对流是引起纳米流体导热系数测量结果多变的重要原因之一。     

基于RBF径向基神经网络的摩擦学模型

建立了润滑油摩擦学特性影响规律的径向基神经网络模型,可以较准确地计算和预测润滑油摩擦系数与负荷及相对滑动速度之间的关系.试验结果表明这种网络具有很好的准确度和预测性,为摩擦学设计和程序化计算和分析提供了一种方便且有效的工具.     

采用3ω法测量多壁纳米碳管悬浮液的导热系数

为了减小瞬态热线法测量纳米流体导热系数时易受电磁干扰和自然对流等因素的影响,更好地探究新型换热工质的强化传热机理,研制了3ω法实验台,采用锁相放大器测得交流加热铂丝的3倍频电压响应,拟合算出待测液体的导热系数.先通过对常规液体蒸馏水、乙二醇以及酒精溶液的测量,验证了实验台的精度和可靠性.然后采用两步法合成了稳定性较高的多壁纳米碳管悬浮液,测得其各体积分数和各温度下的导热系数.实验结果表明,3ω法具有较好的电磁兼容性,测量时温升不超过0.5K,可以有效地减小对流传热和辐射传热的影响,且可以通过1ω电压来判断纳米流体的稳定性;纳米碳管悬浮液的导热系数比基液和Hamilton-Crosser预测值明显提高,并且分别随纳米碳管含量的增加和温度升高而加大.     

基于BP神经网络模型的MEMS加速度计误差补偿方法

现阶段普遍采用多元线性回归对加速度计误差建模,并利用最小二乘法对模型参数辨识,但其对加速度计精度提高有限,因此该文提出一种基于BP神经网络模型的MEMS加速度计误差补偿方法。该方法利用BP神经网络建立加速度计误差模型,通过多位置翻滚进行实验数据测量,并对模型进行训练,最后利用训练好的模型对加速度计误差进行补偿。比较多元线性回归和BP神经网络建模对加速计误差补偿结果,其标准偏差分别为0.001 9 g和0.000 16 g。结果表明误差下降一个数量级,说明BP神经网络能有效地补偿加速度计误差。     

径向基函数神经网络在地下水位预报中的应用

本文在介绍径向基函数神经网络原理的基础上,研究径向基函数神经网络模型在地下水位预报中的应用,以吉林西部地区为例,应用其1990-2012年的月平均地下水位数据,建立径向基函数神经网络模型。为进一步证明预报结果的准确性,把预报结果与自回归模型的预报结果进行比较。结果表明:径向基函数神经网络模型能很好地进行地下水位预报,同自回归模型相比,径向基函数神经网络模型预报的精度更高,预报结果更具有准确性。     

二元混合表面活性剂对纳米流体热物性影响

采用两步法制备了添加二元混合表面活性剂的氧化锌纳米流体,纳米颗粒的体积浓度为0.398%～2.292%。XRD、TEM对氧化锌纳米粒子进行表征,吸光度法和沉降法分析了纳米流体稳定性,之后研究15～55℃时,制备流体的导热与黏度,并与添加单一表面活性剂的纳米流体进行对比。实验结果显示,添加SDS/CTAB纳米流体稳定性更优。纳米流体导热系数随温度及纳米粒子体积浓度的增加而增大,55℃,2.292%的纳米流体热导率增强最大,提高了38%,且热导率增强作用明显优于单一表面活性剂的添加。纳米流体黏度随温度及纳米粒子体积浓度的增加而逐渐降低,在55℃,2.292%时拥有最小黏度0.645 mPa·s。与单一表面活性剂相比,SDS/CTAB的添加能有效降低纳米流体黏度,对减小纳米流体黏度有积极作用。     

基于径向基函数神经网络的CFRP切削力预测

碳纤维增强树脂基复合材料(CFRP)加工中基体相极易因切削力过大而破坏,并迅速扩展至加工表面以下而形成损伤。为了准确预测其切削力并加以控制,基于实验切削力数据建立了人工神经网络切削力模型,预测了不同纤维角度、切削深度和刀具角度下加工CFRP的切削力变化规律,并完成了不同刀具角度及切削参数下典型纤维角度CFRP单向板的直角切削实验,对预测模型进行验证,其预测精度可达85%以上。结合成屑过程在线显微观测结果可知:纤维角度是影响CFRP切削力的主要因素, 0°~135°范围内,切屑形成方式为切断型和开裂后弯断型;切削力随纤维角度增大呈先减小后增大的趋势, 135°时最大,随切削深度增加,切削力总体呈增大趋势。      

A model of thermal conductivity of nanofluids with interfacial shells

We derive an expression for the effective thermal conductivity of nanofluids with interfacial shells. Comparing with conventional models, the expression is not only depended on the thermal conductivity of the solid and liquid and their relative volume fraction, but also depended on the particle size and interfacial properties. The theoretical results on the effective thermal conductivity of CuO/water and CuO/ethylene glycol nanofluids are in good agreement with the experimental data.     

基于RBF神经网络的添加剂改性炭材料高温黏结剂的性能预报模型

在炭材料黏结剂添加剂改性实验数据的基础上，将神经网络方法用于研究添加剂配方和热处理温度对黏结强度的影响关系，建立了添加剂改性炭材料黏结剂的RBF（Radial Basis Function径向基函数）神经网络性能预报模型，并与BP（Back-Propagation逆传播）人工神经网络进行了预报精度和训练过程比较。结果表明：上述两种模型对于黏结强度的预报平均相对误差分别为0．0127和0．0600，且BP人工神经网络易陷入局部最小。因此，RBF神经网络模型的预报能力较好，得出了具有较精确黏结性能的添加剂配方和热处理数据。可望在炭材料黏结剂改性中的多变量、非线性体系中提高实验工作效率，为炭材料黏结剂提供一条有应用前景的理论设计途径。     

基于BP神经网络的导线脱冰跳跃高度预测模型

冰区导线脱冰振动会引起绝缘间隙减小,严重时甚至导致闪络和跳闸等电气事故.首先利用数值方法模拟得到各种参数条件下导线的脱冰动力响应,获得导线的最大脱冰跳跃高度.进而基于数值模拟结果和BP神经网络构建导线脱冰跳跃高度预测模型,将线路的导线分裂数、导线型号、档距、高差等结构参数以及初始应力、覆冰厚度和脱冰率等载荷参数作为输入...     

Predicting the effective thermal conductivity of carbon nanotube based nanofluids

Adding a small volume fraction of carbon nanotubes (CNTs) to a liquid enhances the thermal conductivity significantly. Recent experimental findings report an anomalously wide range of enhancement values that continue to perplex the research community and remain unexplained. In this paper we present a theoretical model based on three-dimensional CNT chain formation (percolation) in the base liquid and the corresponding thermal resistance network. The model considers random CNT orientation and CNT-CNT interaction forming the percolating chain. Predictions are in good agreement with almost all available experimental data. Results show that the enhancement critically depends on the CNT geometry (length), volume fraction, thermal conductivity of the base liquid and the nanofluid (CNT-liquid suspension) preparation technique. Based on the physical mechanism of heat conduction in the nanofluid, we introduce a new dimensionless parameter that alone characterizes the nanofluid thermal conductivity with reasonable accuracy (～ ± 5%).     

基于改进粒子群算法的径向基人工神经网络淀粉基发泡复合材料性能预测

以乙烯-醋酸乙烯酯(EVA)和淀粉质量比、甘油质量分数和NaHCO_3质量分数为输入,以拉伸强度和回弹率为输出,建立基于种群熵多样性评估和收敛、发散策略的粒子群改进算法的径向基人工神经网络(RBF ANN)的淀粉基发泡复合材料性能预测模型。结果表明,该模型的预测效果较好,预测均方差和相关系数分别为0.0160和0.9890。预测发现,淀粉基发泡复合材料的拉伸强度随甘油含量的增加而缓慢降低,随NaHCO_3含量的增加先减少后增加;回弹率随甘油含量的增加而递增,随NaHCO_3含量的增加而先增加后减少。     

A hybrid radial boundary node method based on radial basis point interpolation

The hybrid boundary node method (HBNM) retains the meshless attribute of the moving least squares (MLS) approximation and the reduced dimensionality advantages of the boundary element method. However, the HBNM inherits the deficiency of the MLS approximation, in which shape functions lack the delta function property. Thus in the HBNM, boundary conditions are implemented after they are transformed into their approximations on the boundary nodes with the MLS scheme.This paper combines the hybrid displacement variational formulation and the radial basis point interpolation to develop a direct boundary-type meshless method, the hybrid radial boundary node method (HRBNM) for two-dimensional potential problems. The HRBNM is truly meshless, i.e. absolutely no elements are required either for interpolation or for integration. The radial basis point interpolation is used to construct shape functions with delta function property. So unlike the HBNM, the HRBNM is a direct numerical method in which the basic unknown quantity is the real solution of nodal variables, and boundary conditions can be applied directly and easily, which leads to greater computational precision. Some selected numerical tests illustrate the efficiency of the method proposed.     

Effective thermal conductivity and rheological characteristics of ethylene glycol-based nanofluids with single-walled carbon nanohorn inclusions

In this study, we report the effective thermal conductivity and rheological behavior of ethylene glycol with single-walled carbon nanohorn inclusions. The thermal conductivity and viscosity was found to increase with respect to nanohorn loading. Maximum thermal conductivity enhancement of ～11% at a nanohorn loading of 1.5 vol% was obtained in this study. The viscosity of nanofluids increase with respect to nanohorn loading and decreases with respect to shear rate which indicates the non-Newtonian shear thinning behavior at higher nanohorn loading. Finally, the effectiveness of nanofluids was calculated for laminar and turbulent regions to predict the heat transfer performance and favorability of these nanofluids. The present nanofluids are favorable upto 0.1 vol% in the laminar region. However, these nanofluids are not favorable for turbulent region and loadings beyond 0.1 vol% due to higher viscosity enhancement.     

基于人工神经网络的NiZn铁氧体结构不敏感性能的预测模型

为了系统研究配方对铁氧体电磁性能的影响,制备了一系列Mn2 、Ge4 和Si4 替代的NiZn铁氧体材料,建立了铁氧体配方与结构不敏感性能之间的人工神经网络预测模型.利用所建立的模型研究了ZnO对NiZn铁氧体3个结构不敏感性能居里温度、磁饱和强度及介电常数的影响规律,以及多个组分的交互作用.结果表明:模型的预测结果与实验结果吻合良好,二者的相对误差较小.ZnO含量的增加会导致铁氧体居里温度下降,但会提高饱和磁化强度和介电常数.NiO和ZnO的交互作用对铁氧体的结构不敏感性能影响明显.利用模型得到的铁氧体性能-成分等值线图对寻找最佳配方有较高参考价值.     

A molecular dynamics-stochastic model for thermal conductivity of nanofluids and its experimental validation

A model to predict the enhanced thermal conductivity of water based copper nanofluid on the basis of molecular dynamics simulation coupled with stochastic simulation shows for the first time that the temperature of a copper nanoparticle colliding with a heat source can rise rapidly within the short collision period (e.g., 10-50 ps) estimated by impact dynamics due to phonon transfer. Thereafter the particles undergo Brownian movement in the base fluid and transfer the excess heat in about 2 to 3 ms to the surrounding fluid resulting in an appreciable enhancement of the thermal conductivity of the fluid. Microconvection has minor contribution to the enhanced thermal conductivity of nanofluids. The predicted thermal conductivity of nanofluid and its variation with the volume fraction of the nanoparticles agree well with the present experiments, as well as, with the data reported in the literature.