scholarly journals USING BEM TO PREDICT THE EFFECTIVE THERMAL CONDUCTIVITY FOR HETEROGENEOUS MATERIALS

2015 ◽  
Vol 14 (1) ◽  
pp. 09
Author(s):  
M. B. A. M. Oberg ◽  
C. T. M. Anflor ◽  
J. N. V. Goulart
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Effective Properties ◽  
Two Dimensions ◽  
Volume Element ◽  
Numerical Implementation ◽  
State Potential ◽  
Boundary Elements Method ◽  
Steady State Potential

This work presents a study on the effective thermal conductivity in material with heterogeneous composition in two dimensions. The Boundary Elements Method (BEM) is used to solve the steady state potential equations. The sub regions technique was implemented in order to take into account the effects of these inclusions inside the domain. In the numerical implementation, the inclusions are randomly generated in a Representative Volume Element (RVE) domain. The Average Field Theory is used to predict the effective properties (macroscopic) of the material with heterogeneous composition. The material is characterized by a specified volume fraction as well as the inclusion’s size. The samples are composed of square domains with defined number of randomly distributed inclusions and submitted to a condition of unidirectional heat conduction. Each set of samples is analyzed several times in order to guarantee statistical stability of the result.

Effective Thermal Conductivity of Lithium Ion Battery Electrodes Employing Fully Resolved Simulations for Use in Volume Averaged Models

2013 ◽  
Author(s):  
Ajay Vadakkepatt ◽  
Bradley L. Trembacki ◽  
Sanjay R. Mathur ◽  
Jayathi Y. Murthy
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Effective Properties ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Volume Averaging ◽  
Porous Electrodes ◽  
Multiphase Materials ◽  
Averaged Models

Simulations of lithium ion batteries on a cell level are usually performed with volume averaging methods that employ effective transport properties. Bruggeman’s model, which is widely used to determine these effective properties, is solely based on the volume fraction of these porous electrodes. However, other factors like the topology and microstructure of electrodes also play a crucial role in determining effective properties. In this paper, a general derivation of the effective thermal conductivity of multiphase materials, which can be correlated with these factors, is derived using the volume averaging technique. For demonstration, three-dimensional microstructures of various porous materials are reconstructed from scanned images. These images are used to generate fully-resolved finite volume meshes representing the various constituents. The resulting mesh is then employed for numerical analysis of thermal transport, results from which are used for correlating the effective thermal conductivity with various parameters describing the microstructure. It is shown that commonly used power law exponents in the Bruggeman model for effective thermal conductivity must be recalibrated to fit the effective thermal conductivity computed from these detailed simulations.

Two-dimensional lattice Boltzmann modeling for effective thermal conductivity in carbon black filled composites

2017 ◽  
Vol 52 (15) ◽  
pp. 2047-2053 ◽  
Author(s):  
Yong-Jun Kim ◽  
Yu-Fei Tan ◽  
Sok Kim
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Carbon Black ◽  
Effective Thermal Conductivity ◽  
Lattice Boltzmann ◽  
Volume Fraction ◽  
Effective Properties ◽  
Lattice Boltzmann Model ◽  
Two Dimensional ◽  
Boltzmann Model

Polymer composites filled with thermally conductive particles are widely used in thermo-electronic industry, and the prediction of effective properties is still important for design and use of composites. Thus, we propose a lattice Boltzmann model to predict the effective thermal conductivity of composites filled with carbon black. First, a method for reconstructing numerical material having filler distribution characteristic similar to that of actual material is introduced, and the process for obtaining the phase function and the volume fraction of grain filler is described. The energy transport governing equation is then solved through the two-dimensional discrete structure by using a lattice Boltzmann model. The effective thermal conductivity of two-phase composite is expressed by the conductivity of each phase and the temperature distribution in discrete rectangle. The resultant prediction is compared with theoretical and experimental data and indicates good agreement with experimental data.

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.

Thermal Conductivity of Metal-Oxide Nanofluids: Particle Size Dependence and Effect of Laser Irradiation

2006 ◽  
Vol 129 (3) ◽  
pp. 298-307 ◽  
Author(s):  
Sang Hyun Kim ◽  
Sun Rock Choi ◽  
Dongsik Kim
Keyword(s):  
Thermal Conductivity ◽  
Particle Size ◽  
Effective Thermal Conductivity ◽  
Laser Irradiation ◽  
Volume Fraction ◽  
Suspended Particle ◽  
Titanium Dioxide Nanoparticles ◽  
Size Change ◽  
Conductivity Enhancement ◽  
Wide Range

The thermal conductivity of water- and ethylene glycol-based nanofluids containing alumina, zinc-oxide, and titanium-dioxide nanoparticles is measured using the transient hot-wire method. Measurements are performed by varying the particle size and volume fraction, providing a set of consistent experimental data over a wide range of colloidal conditions. Emphasis is placed on the effect of the suspended particle size on the effective thermal conductivity. Also, the effect of laser-pulse irradiation, i.e., the particle size change by laser ablation, is examined for ZnO nanofluids. The results show that the thermal-conductivity enhancement ratio relative to the base fluid increases linearly with decreasing the particle size but no existing empirical or theoretical correlation can explain the behavior. It is also demonstrated that high-power laser irradiation can lead to substantial enhancement in the effective thermal conductivity although only a small fraction of the particles are fragmented.

An Analytical Modeling for Effective Thermal Conductivity of Multi-Phase Transversely Isotropic Fiberous Composites Using Generalized Self-Consistent Method

2012 ◽  
Vol 249-250 ◽  
pp. 904-909 ◽  
Author(s):  
Syed Aadil Hassan ◽  
Hassaan Ahmed ◽  
Asif Israr
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Composite System ◽  
Volume Fraction ◽  
Transversely Isotropic ◽  
Balance Principle ◽  
Isotropic Composite ◽  
Self Consistent ◽  
Consistent Method ◽  
Multi Phase

In this paper a theoretical relationship for the effective thermal conductivity of a multiphase transversely isotropic composite system is obtained. The Generalized Self-Consistent Method and simple energy balance principle is employed to derive a more appropriate model. In the derivation, it is assumed that the orientation of fiber within the transversely isotropic composite system is unidirectional and surrounded by two different phases of porous and matrix phase. A combined effect of these three different phases on the effective thermal conductivity of the composite system in transverse direction is studied. The effect of the interfacial contact conductance between the fibers and porous medium is also considered. Results of effective thermal conductivity are plotted against volume fraction and conductance which shows extremely good agreement.

Determining the Effective Thermal Conductivity of a Nanofluid Using Brownian Dynamics Simulation

2003 ◽  
Author(s):  
P. Bhattacharya ◽  
S. K. Saha ◽  
A. Yadav ◽  
P. E. Phelan ◽  
R. S. Prasher
Keyword(s):  
Thermal Conductivity ◽  
Brownian Motion ◽  
Effective Thermal Conductivity ◽  
Brownian Dynamics ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Solid Particles ◽  
Dynamics Simulation ◽  
Fluid Viscosity ◽  
Brownian Dynamics Simulation

A nanofluid is a fluid containing suspended solid particles, with sizes of the order of nanometers. Normally the fluid has a low thermal conductivity compared to the suspended particles. Therefore introduction of these particles into the fluid increases the effective thermal conductivity of the system. It is of interest to predict the effective thermal conductivity of such a nanofluid under different conditions like varying particle volume fraction, varying particle size, changing fluid conductivity or changing fluid viscosity, especially since only limited experimental data are available. Also, some controversy exists about the role of Brownian motion in enhancing the nanofluid’s thermal conductivity. We have developed a novel technique to compute the effective thermal conductivity of a nanofluid using Brownian dynamics simulation, which has the advantage of being computationally less expensive than molecular dynamics. We obtain the contribution of the nanoparticles towards the effective thermal conductivity using the equilibrium Green-Kubo method. Then we combine that with the thermal conductivity of the base fluid to obtain the effective thermal conductivity of the nanofluid, and thus are able to show that the Brownian motion contributes greatly to the thermal conductivity.

Design of High Thermal Conductivity Particle Filled Polymer Using Effective Thermal Conductivity Models

2012 ◽  
Vol 714 ◽  
pp. 21-24 ◽  
Author(s):  
B. Garnier ◽  
F. Danes
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Thermal Contact Resistance ◽  
Volume Fraction ◽  
Thermal Contact ◽  
Analytical Models ◽  
Finite Element Models ◽  
Inner Diameter ◽  
Conductive Particles ◽  
Conductivity Models

The context of this work is the enhancement of the thermal conductivity of polymer by adding conductive particles. It will be shown how we can use effective thermal conductivity models to investigate effect of various factors such as the volume fraction of filler, matrix thermal conductivity, thermal contact resistance, and inner diameter for hollow particles. Analytical models for lower bounds and finite element models will be discussed. It is shown that one can get some insights from effective thermal conductivity models for the tailoring of conductive composite, therefore reducing the amount of experimental work.

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