Thermal conductivity of multiphase particulate composite materials

Hollow particle filled composites, called syntactic foams, are used in weight sensitive structural applications in this paper. In this paper, homogenization techniques are used to derive estimates for thermal conductivity of hollow particle filled composites. The microstructure is modeled as a three-phase system consisting of an air void, a shell surrounding the air void, and a matrix material. The model is applicable to composites containing coated solid particles in a matrix material and can be further expanded to include additional coating layers. The model is successful in predicting thermal conductivity of composites containing up to 52% particles by volume. Theoretical results for thermal conductivity are validated with the results obtained from finite element analysis and are found to be in close agreement with them. A simplified approximation of the theoretical model applicable to thin shells is also validated and found to be in good agreement with the corresponding finite element results. The model is applicable to a wide variety of particulate composite materials and will help in tailoring the properties of particulate composites as per the requirements of the application.     

Thermal conductivity of inhomogeneous materials

The effective thermal conductivity is calculated from the rate of entropy production per unit volume. Thermal conductivity and the temperature field are expressed in terms of Fourier components and these are related. The rate of entropy production is then obtained in terms of the volume-averaged thermal conductivity and the Fourier components of thermal conductivity. A simple expression for the effective thermal conductivity is found. In the case of striations it leads to well-known results. The formalism is applied to solids with inhomogeneously distributed solutes. It is shown that the thermal conductivity is less than the volume-averaged thermal conductivity and that homogenization by diffusion increases the thermal conductivity. Similar results would apply to the electrical conductivity of inhomogeneous alloys.     

Thermal conductivity of moist porous materials

We propose both a model and a method of calculating the effective thermal conductivity of moist porous materials made of a three-component structure with interpenetrating components.Translated from Inzhenerno-Fizi-cheskii Zhurnal, Vol. 56, No. 2, pp. 281–291, February, 1989.     

Thermal conductivity analysis and applications of nanocellulose materials

Abstract

In this review, we summarize the recent progress in thermal conductivity analysis of nanocellulose materials called cellulose nanopapers, and compare them with polymeric materials, including neat polymers, composites, and traditional paper. It is important to individually measure the in-plane and through-plane heat-conducting properties of two-dimensional planar materials, so steady-state and non-equilibrium methods, in particular the laser spot periodic heating radiation thermometry method, are reviewed. The structural dependency of cellulose nanopaper on thermal conduction is described in terms of the crystallite size effect, fibre orientation, and interfacial thermal resistance between fibres and small pores. The novel applications of cellulose as thermally conductive transparent materials and thermal-guiding materials are also discussed.     

Thermal conductivity and quasihomogeneity criterion of disperse materials

We suggest a method of numerical calculation of the effective thermal conductivity and the time for establishing quasihomogeneity of disperse materials. The method is based on the principle of generalized conductivity realizable within the framework of a nonstationary thermal problem. Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 71, No. 3, pp. 441–446, May–June, 1998.     

Contribution of crystalline lattice defects to thermal conductivity of porous materials

The relationship of pore thermal resistance and effective thermal conductivity of porous media to the processes of crystalline lattice defect formation and motion is demonstrated.Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 44, No. 6, pp. 965–969, June, 1983.     

Thermal resistance of contacts coated with low conductivity materials

Results are presented of an experimental study of thermal resistance in contacts with a ceramic coating in a vacuum and a gaseous medium. Thermal resistance of contact is determined as a function of coating thickness and thermal conductivity.     

热线法测量保温材料的导热系数

本文在分析热线法测量导热系数原理的基础上，应用热线法测定了几种保温材料不同温度下的导热系数。并通过Origin 7．0对测量结果进行了数据线性拟合，可快捷、精确的获得测量结果。     

固体材料低温热导率测试系统

研制出工程材料低温热导率测试系统.测试采用稳态纵向热流法,高真空绝热恒温器,并利用热开关装置,解决了传统测试方法降温速率慢的问题.通过与标准样品比对,证明测试误差在5%以内.利用该测试系统研究了不锈钢、钛合金及镁合金的低温热导率.     

纳米铜/石蜡复合相变蓄热材料的导热性能研究

采用HotDisk热分析仪测试了Cu/石蜡体系在不同纳米颗粒质量分数、温度和热循环次数下的导热系数。研究表明,Cu/石蜡体系的固、液态导热系数随纳米Cu颗粒含量的增加呈非线性增加;温度变化对相变材料导热系数的影响并不明显,但当温度升高至相变温度区间时,相变材料的导热系数急剧增加;复合材料在经历100次热循环后,材料的导热系数值仍较稳定。     

Thermal decomposition and electrical conductivity of oxide cathode emission materials

Thermal decomposition and electrical conductivity of oxide cathode emission materials used for cathode ray tubes (CRTs) have been studied under different heat treatment conditions for commercial sprayed cathode systems based on barium-strontium carbonate precursors. Conversion of the carbonate precursor commenced at temperatures above approximately 700 K in vacuum, evidenced by increases in conductivity, however, the rate of the conversion reaction increased dramatically as the temperature was increased. The corresponding chemical and microstructural changes have also been investigated by thermo-gravimetric analysis (TGA) and scanning electron microscopy (SEM), with multiple decomposition stages identified corresponding to the conversion of the carbonate precursor and separate activation steps associated with the reaction of barium oxide with the Mg and Al activating agents in the nickel cathode substrate. © 2000 Kluwer Academic Publishers     

Thermal flicker noise in dissipative pre-melting processes in crystalline materials

An analysis is made of thermal fluctuations on the premelting exothermics of materials with different types of chemical bond (KCl, Ge, Sb, and Cu). Statistical and spectral parameters of the thermal fluctuations are introduced. It is shown that for these materials under certain conditions the thermal fluctuations may be identified as two-level thermal flicker noise. Pis’ma Zh. Tekh. Fiz. 24, 24–27 (July 26, 1998)     

Thermal treatment and crystalline structure in dental cast ceramic materials

The thermal treatment, known as ceramming, of cast glass ceramics was undertaken to clarify the crystalline microstructures that were observed at several constant temperatures. Differential thermal analysis when raising the test temperature indicated that the samples kept at 800, 890 and 980 °C had different characteristic temperatures. X-ray diffraction analysis and the Fourier transform-infrared method showed the findings of diopside, -tricalcium phosphate and hydroxyapatite. These results suggest that the thermal ceramming temperature has to be selected for the conversion of amorphous glass ceramic materials in dental applications.On leave from Hiroshima University.     

Thermal conductivity of porous materials as a function of moisture content

Results are presented of studies of the thermal conductivity of porous materials (including cermets, fireclay ceramics, and glass bead models) as a function of moisture content.Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 21, No. 2, pp. 296–300, August, 1971.     

Thermal conductivity imaging at micrometre-scale resolution for combinatorial studies of materials

Combinatorial methods offer an efficient approach for the development of new materials. Methods for generating combinatorial samples of materials, and methods for characterizing local composition and structure by electron microprobe analysis and electron-backscatter diffraction are relatively well developed. But a key component for combinatorial studies of materials is high-spatial-resolution measurements of the property of interest, for example, the magnetic, optical, electrical, mechanical or thermal properties of each phase, composition or processing condition. Advances in the experimental methods used for mapping these properties will have a significant impact on materials science and engineering. Here we show how time-domain thermoreflectance can be used to image the thermal conductivity of the cross-section of a Nb-Ti-Cr-Si diffusion multiple, and thereby demonstrate rapid and quantitative measurements of thermal transport properties for combinatorial studies of materials. The lateral spatial resolution of the technique is 3.4 microm, and the time required to measure a 100 x 100 pixel image is approximately 1 h. The thermal conductivity of TiCr(2) decreases by a factor of two in crossing from the near-stoichiometric side of the phase to the Ti-rich side; and the conductivity of (Ti,Nb)(3)Si shows a strong dependence on crystalline orientation.     

Thermal conductivity of liquid crystalline epoxy/BN filler composites having ordered network structure

BN filler was added to a liquid crystalline (LC) epoxy resin to obtain a high thermal conductive material. The LC epoxy/BN composites, which were cured at different temperatures, formed an isotropic or LC polydomain phase structure. The relationship between the network orientation containing mesogenic groups and the dispersibility of the BN filler was discussed. As a result, the thermal conductivity of the LC polydomain system was drastically enhanced even at a relatively low volume fraction of BN (30 vol%), regardless of the fact that both the LC and isotropic phase systems consisted of the same resin and filler content combination. This result is due to the formation of thermal conductive paths by the BN filler by exclusion of the BN filler from the LC domain formed during the curing process in the composite having the LC polydomain matrix.     

Thermal diffusivity and conductivity in low-conducting materials: A new technique

A comparative method is presented, suitable to measure both thermal diffusivity and conductivity of low-conducting solids. The repeatibility of the measurements of thermal conductivity is 3%, whereas for diffusivity is 6%. Data for some low-conducting materials are given, consistent with those reported in the literature.     

Effective thermal conductivity of loose particulate systems

The effective thermal conductivity for several loose particulate insulation systems has been measured in the temperature range from 273 K–900 K and the results compared to those predicted from three different models. The measured thermal conductivities increase with temperature. This is accounted for in terms of increased conduction by the fluid (air) and the radiative heat transfer through the media although the latter mode of heat transfer is relatively suppressed in materials containing finer particles. The model due to Zumbrunnen et al. [1] was found to predict values that closely agreed with the experimental values.     

Thermal conductivity of methane

This paper reports thermal conductivity data for methane measured in the temperature range 120–400 K and pressure range 25–700 bar with a maximum uncertainty of ± 1%. A simple correlation of these data accurate to within about 3% is obtained and used to prepare a table of recommended values.Nomenclature a _k ,b _ij ,b _k Parameters of the regression model, k= 0 to n; i =0 to m; j =0 to n - P Pressure (MPa or bar) - Q _kl Heat flux per unit length (mW · m^–1) - t time (s) - T Temperature (K) - T _cr Critical temperature (K) - T _r reduced temperature (= T/T _cr) - T _w Temperature rise of wire between times t ₁ and t ₂ (deg K) - T ^* Reduced temperature difference (TT _cr)/T _cr - Thermal conductivity (mW · m^–1 · K^–1) - ₁ Thermal conductivity at 1 bar (mW · m^–1 · K^–1) - _bg Background thermal conductivity (mW · m^–1 · K^–1) - _cr Anomalous thermal conductivity (mW · m^–1 · K^–1) - _e Excess thermal conductivity (mW · m^–1 · K^–1) - Density (g · cm^–3) - _cr Critical density (g · cm^–3) - _r Reduced density (= / _cr) - ^* Reduced density difference (– _cr )/ _cr