Modeling Thermal Conductivity of Aligned CNT-Matrix Composites With Increasing Volume Fraction

2016 ◽  
Author(s):  
Diana Grandio ◽  
Drazen Fabris
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Particle Interaction ◽  
Longitudinal Direction ◽  
Near Neighbor ◽  
Correction Function ◽  
Matrix Composites ◽  
Particle Volume ◽  
Volume Fractions

In prior work an effective medium approach (EMA) has been developed to evaluate composite physical properties such as thermal conductivity, dielectric function or elastic modulus (C.-W. Nan, Prog. Mat. Sci. V. 37, 1993). This model combined with the Kapitza interface resistance can predict the effective thermal conductivity of randomly dispersed long fibers for a very low volume fraction (f < 0.01). The present study compares finite-element (FEA) computations and the EMA model for CNT-matrix compositions with low to moderate volume fractions, 0.001 to 0.02. The FEA results obtained show that the EMA model underestimates the effective thermal conductivity of the composite when the particles are very close to each other, even for small particle volume fractions. For aligned fibers the Kaptiza resistance cannot be neglected in the longitudinal direction. This paper proposes a general correction function for the dependence on particle to particle interaction based on the near neighbor distances and the number of near neighbors. This correction function reduces the EMA under prediction to within several percent (< 5%) in most cases.

Hierarchical Modeling and Trade-Off Studies in Design of Thermal Interface Materials

2005 ◽  
Author(s):  
X. Zhang ◽  
S. Kanuparthi ◽  
G. Subbarayan ◽  
B. Sammakia ◽  
S. Tonapi
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Thermal Interface Materials ◽  
Particle Volume ◽  
Heat Spreader ◽  
Thermal Interface ◽  
Volume Fractions ◽  
Interface Materials

Particle laden polymer composites are widely used as thermal interface materials in the electronics cooling industry. The projected small chip-sizes and high power applications in the near future demand higher values of effective thermal conductivity of the thermal interface materials (TIMs) used between the chip and the heat-spreader and the heat-spreader and heat-sink. However, over two decades of research has not yielded materials with significantly improved effective thermal conductivities. A critical need in developing these TIMs is apriori modeling using fundamental physical principles to predict the effect of particle volume fraction and arrangements on effective behavior. Such a model will enable one to optimize the structure and arrangement of the material. The existing analytical descriptions of thermal transport in particulate systems under predict (as compared to the experimentally observed values) the effective thermal conductivity since these models do not accurately account for the effect of inter-particle interactions, especially when particle volume fractions approach the percolation limits of approximately 60%. Most existing theories are observed to be accurate when filler material volume fractions are less than 30–35%. In this paper, we present a hierarchical, meshless, computational procedure for creating complex microstructures, explicitly analyzing their effective thermal behavior, and mathematically optimizing particle sizes and arrangements. A newly developed object-oriented symbolic, java language framework termed jNURBS implementing the developed procedure is used to generate and analyze representative random microstructures of the TIMs.

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.

Asymptotic Solutions of Effective Thermal Conductivity of Particle-Laden Polymers

2005 ◽  
Author(s):  
P. Karayacoubian ◽  
M. Bahrami ◽  
J. R. Culham
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Analytical Model ◽  
Volume Fraction ◽  
Effective Medium Theory ◽  
Full Range ◽  
Packed Bed ◽  
Asymptotic Solutions ◽  
Particle Volume ◽  
The Matrix

A general predictive model for the effective thermal conductivity of mixtures is developed. In the limit of very small particle volume fractions, a limiting case is approached where the effective medium theory of Maxwell holds. At higher solid fractions, an analytical model for the conductivity of a packed bed of spheres is developed. These two limiting asymptotic solutions are then combined using a blending procedure. The result is a semi-analytical model that is valid over the full range of solid fractions. The model shows that in addition to the conductivities of the particle/matrix and the solid fraction, the degree of wetting of the particles by the matrix is an important parameter in estimating the effective thermal conductivity of the mixture. In addition, the effect of entrapped air is captured through the definition of an effective volume fraction in Maxwell’s model. The model shows good agreement with experimental data.

The Influence of Multiple Inclusions on the Cauchy Stress of a Spherical Particle-Reinforced Composite Under Uniaxial Loading

2014 ◽  
Author(s):  
Ke Niu ◽  
Armin Abedini ◽  
Zengtao Chen
Keyword(s):  
Spherical Particle ◽  
Single Particle ◽  
Volume Fraction ◽  
Unit Cell ◽  
Spherical Particles ◽  
Uniaxial Tensile ◽  
Cauchy Stress ◽  
Particle Volume ◽  
Volume Fractions ◽  
Unit Cells

This paper investigates the influence of multiple inclusions on the Cauchy stress of a spherical particle-reinforced metal matrix composite (MMC) under uniaxial tensile loading condition. The approach of three-dimensional cubic multi-particle unit cell is used to investigate the 15 non-overlapping identical spherical particles which are randomly distributed in the unit cell. The coordinates of the center of each particle are calculated by using the Random Sequential Adsorption algorithm (RSA) to ensure its periodicity. The models with reinforcement volume fractions of 10%, 15%, 20% and 25% are evaluated by using the finite element method. The behaviour of Cauchy stress for each model is analyzed at a far-field strain of 5%. For each reinforcement volume fraction, four models with different particle spatial distributions are evaluated and averaged to achieve a more accurate result. At the same time, single-particle unit cell and analytical model were developed. The stress-strain curves of multi-particle unit cells are compared with single-particle unit cells and the tangent homogenization model coupled with the Mori-Tanaka method. Only little scatters were found between unit cells with the same particle volume fractions. Multi-particle unit cells predict higher response than single particle unit cells. As the volume fraction of reinforcements increases, the Cauchy stress of MMCs increases.

Figure of Merit-Based Optimization Approach of Phase Change Material-based Composites for Portable Electronics Using Simplified Model

2021 ◽  
Author(s):  
Ayushman Singh ◽  
Srikanth Rangarajan ◽  
Leila Choobineh ◽  
Bahgat Sammakia
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Phase Change ◽  
Effective Thermal Conductivity ◽  
Phase Change Material ◽  
Figure Of Merit ◽  
Volume Fraction ◽  
Simplified Model ◽  
Transient Temperature ◽  
Change Material

Abstract This work presents an approach to optimally designing a composite with thermal conductivity enhancers (TCEs) infiltrated with phase change material (PCM) based on figure of merit (FOM) for thermal management of portable electronic devices. The FOM defines the balance between effective thermal conductivity and energy storage capacity. In present study, TCEs are in the form of a honeycomb structure. TCEs are often used in conjunction with PCM to enhance the conductivity of the composite medium. Under constrained composite volume, the higher volume fraction of TCEs improves the effective thermal conductivity of the composite, while it reduces the amount of latent heat storage simultaneously. The present work arrives at the optimal design of composite for electronic cooling by maximizing the FOM to resolve the stated trade-off. In this study, the total volume of the composite and the interfacial heat transfer area between the PCM and TCE are constrained for all design points. A benchmarked two-dimensional direct CFD model was employed to investigate the thermal performance of the PCM and TCE composite. Furthermore, assuming conduction-dominated heat transfer in the composite, a simplified effective numerical model that solves the single energy equation with the effective properties of the PCM and TCE has been developed. The effective thermal conductivity of the composite is obtained by minimizing the error between the transient temperature gradient of direct and simplified model by iteratively varying the effective thermal conductivity. The FOM is maximized to find the optimal volume fraction for the present design.

MICROSTRUCTURE AND PROPERTIES OF SiC PARTICLES REINFORCED COPPER BASED ALLOY COMPOSITE

2013 ◽  
Vol 27 (19) ◽  
pp. 1341025 ◽  
Author(s):  
YU HONG ◽  
XIAOLI CHEN ◽  
WENFANG WANG ◽  
YUCHENG WU
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Electrical Conductivity ◽  
Relative Density ◽  
Volume Fraction ◽  
Copper Matrix ◽  
Matrix Composites ◽  
Wear Properties ◽  
Sic Particles ◽  
Copper Based Alloy

Copper-matrix composites reinforced with SiC particles are prepared by mechanical alloying. The microstructure characteristics, relative density, hardness, tensile strength, electrical conductivity, thermal conductivity and wear properties of the composites are investigated in this paper. The results indicate that the relative density, macro-hardness and mechanical properties of composites are improved by modifying the surface of SiC particles with Cu and Ni . The electrical conductivity and thermal conductivity of composites, however, are not obviously improved. For a given volume fraction of SiC , the Cu / SiC ( Ni ) has higher mechanical properties than Cu / SiC ( Cu ). The wear resistance of the composites are improved by the addition of SiC . The composites with optimized interface have lower wear rate.

Numerical Simulation of Thermal Conductivity of Nanofluids

2010 ◽  
Author(s):  
Jing Fan ◽  
Liqiu Wang
Keyword(s):  
Thermal Conductivity ◽  
Heat Conduction ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Interfacial Thermal Resistance ◽  
Thermal Conductivity Ratio ◽  
Radius Of Gyration ◽  
Particle Volume ◽  
Geometrical Structures ◽  
Conductivity Enhancement

The recent first-principle model shows a dual-phase-lagging heat conduction in nanofluids at the macroscale. The macroscopic heat-conduction behavior and the thermal conductivity of nanofluids are determined by their molecular physics and microscale physics. We examine numerically effects of particle-fluid thermal conductivity ratio, particle volume fraction, shape, aggregation, and size distribution on macroscale thermal properties for nine types of nanofluids, without considering the interfacial thermal resistance and dynamic processes on particle-fluid interfaces and particle-particle contacting surfaces. The particle radius of gyration and non-dimensional particle-fluid interfacial area in the unit cell are two very important parameters in characterizing the effect of particles’ geometrical structures on thermal conductivity of nanofluids. Nanofluids containing cross-particle networks have conductivity which practically reaches the Hashin-Shtrikman bounds. Moreover, particle aggregation influences the effective thermal conductivity only when the distance between particles is less than the particle dimension. Uniformly-sized particles are desirable for the conductivity enhancement, although to a limited extent.

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