A reconstruction of Maxwell model for effective thermal conductivity of composite materials

2016 ◽  
Vol 102 ◽  
pp. 972-979 ◽  
Author(s):  
J.Z. Xu ◽  
B.Z. Gao ◽  
F.Y. Kang
Keyword(s):  
Thermal Conductivity ◽  
Composite Materials ◽  
Effective Thermal Conductivity ◽  
Maxwell Model

Enhanced Effective Thermal Conductivity of Composite Materials by Incorporating Constructal Fillers

2021 ◽  
Vol 42 (7) ◽  
Author(s):  
Xiaojian Wang ◽  
Xiaohu Niu ◽  
Wensheng Kang ◽  
Xiaoxue Wang ◽  
Liangbi Wang
Keyword(s):  
Thermal Conductivity ◽  
Composite Materials ◽  
Effective Thermal Conductivity

The Effect of Nanoparticle Coating on the Thermal Conductivity of Nanofluids

2009 ◽  
Vol 1207 ◽  
Author(s):  
Michael John Fornasiero ◽  
Diana-Andra Borca-Tasciuc
Keyword(s):  
Thermal Conductivity ◽  
Iron Oxide ◽  
Effective Thermal Conductivity ◽  
Colloidal Suspensions ◽  
Maxwell Model ◽  
Carrier Fluid ◽  
Coated Nanoparticles ◽  
Transient Hot Wire Technique ◽  
Potential Applications ◽  
Iron Oxide Core

AbstractNanofluids are engineered colloidal suspensions of nanometer-sized particles in a carrier fluid and are receiving significant attention because of their potential applications in heat transfer area. Theoretical investigations have shown that the enhanced thermal conductivity observed in nanofluids is due to nanoparticle clustering and networking. This provides a low resistance path to the heat flowing through the fluid. However, the surface coating of the nanoparticles, which is often used to provide stable dispersion over the long term, may act as a thermal barrier, reducing the effective thermal conductivity of the nanofluid. Moreover, nanofluids with the same type of nanoparticles may exhibit different effective thermal conductivities, depending upon the thermal properties and thickness of the coating. In this context, thermal conductivity characterization of well dispersed iron oxide nanoparticles with two different surface coatings was carried out employing the transient hot wire technique. The diameter of the iron oxide core was 35 nm and the coatings used were aminosilane and carboxymethyl-dextran (CMX) of 7nm in thickness. Preliminary results suggest that effective thermal conductivity of CMX coated nanoparticle suspensions is slightly higher than that of aminosilane coated nanoparticles. In both cases, the effective thermal conductivity is higher than that predicted by the Maxwell model for composite media.

Modeling Heat Conduction Across Thermal Interface Materials With Micro-Particles

2008 ◽  
Author(s):  
C. Channy Wong
Keyword(s):  
Thermal Conductivity ◽  
Heat Conduction ◽  
Composite Materials ◽  
Effective Thermal Conductivity ◽  
Thermal Interface Materials ◽  
Conduction Path ◽  
Micro Particles ◽  
Mechanical And Electrical Properties ◽  
Interface Materials ◽  
Composite Polymeric Material

Different types of fillers with high electrical and thermal conductivities, e.g. graphite and alumina, have been added to adhesive polymers to create composite materials with improved mechanical and electrical properties. Previous modeling efforts have found that it is relatively difficult to predict the effective thermal conductivity of a composite polymeric material when incorporated with large volume content of fillers. We have performed comprehensive computational analysis that models the thermal contacts between fillers. This unique setup can capture the critical heat conduction path to obtain the effective thermal conductivity of the composite materials. Results of these predictions and its comparison with experimental data will be presented in this paper.

scholarly journals Improved Heat Transfer in Guar Gel Composites Reinforced with Randomly Distributed Glass Microspheres

2021 ◽  
Author(s):  
Ruifeng CAO ◽  
Taotao WANG ◽  
Yuxuan ZHANG ◽  
Hui WANG
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Particle Size ◽  
Composite Materials ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Glass Microspheres ◽  
Particle Volume ◽  
Number Of Particles

Improved heat transfer in composites consisting of guar gel matrix and randomly distributed glass microspheres is extensively studied to predict the effective thermal conductivity of composites using the finite element method. In the study, the proper and probabilistic three-dimensional random distribution of microspheres in the continuous matrix is automatically generated by a simple and efficient random sequential adsorption algorithm which is developed by considering the correlation of three factors including particle size, number of particles, and particle volume fraction controlling the geometric configuration of random packing. Then the dependences of the effective thermal conductivity of composite materials on some important factors are investigated numerically, including the particle volume fraction, the particle spatial distribution, the number of particles, the nonuniformity of particle size, the particle dispersion morphology and the thermal conductivity contrast between particle and matrix. The related numerical results are compared with theoretical predictions and available experimental results to assess the validity of the numerical model. These results can provide good guidance for the design of advanced microsphere reinforced composite materials.

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