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.

Brownian dynamics simulation to determine the effective thermal conductivity of nanofluids

2004 ◽  
Vol 95 (11) ◽  
pp. 6492-6494 ◽  
Author(s):  
P. Bhattacharya ◽  
S. K. Saha ◽  
A. Yadav ◽  
P. E. Phelan ◽  
R. S. Prasher
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Brownian Dynamics ◽  
Dynamics Simulation ◽  
Brownian Dynamics Simulation

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.

Molecular Dynamics Simulation of the Thermal Conductivity of Ar-Al Nanofluid Using Simplified Model

2009 ◽  
Author(s):  
Jie Liu ◽  
Wen-Qiang Lu
Keyword(s):  
Thermal Conductivity ◽  
Molecular Dynamics ◽  
Brownian Dynamics ◽  
Base Fluid ◽  
Numerical Results ◽  
Solid Particles ◽  
Dynamics Simulation ◽  
Simplified Model ◽  
Heat Current

Nanofluid is a colloidal solution of nano-sized solid particles in liquids. Ar-Al nanofluid is a promising heat transport fluid in the fields of low-temperature engineering. A simplified model based on the equilibrium molecular dynamics (EMD) simulation is constructed to calculate the thermal conductivity of argon suspension containing aluminum nanoparticles. The numerical method is verified by comparing the numerical results with the existing numerical results and the experimental data of the base fluid. The influence of various nanoparticle loadings is obtained and the results show that the thermal conductivity with 1% nanoparticle loading enhances up to 31% compared with the base fluid. The heat current autocorrelation functions converge well for the basefluid and nanofluid. Furthermore, interesting distinct oscillations are obtained especially at higher nanoparticle loading. The significant role of the interaction between the fluid atoms and the solid nanoparticle rather than Brownian dynamics motion of the nanoparticle in yielding the high thermal conductivity of nanofluid is numerically revealed.

Multi-Block Copolymers in Selective Solvent: A Brownian Dynamics Simulation

2004 ◽  
Vol 856 ◽  
Author(s):  
Yongsheng Liu ◽  
Huifen Nie ◽  
Rama Bansil ◽  
Zhenli Zhang ◽  
Sharon Glotzer
Keyword(s):  
Brownian Dynamics ◽  
Volume Fraction ◽  
Dynamics Simulation ◽  
Multiblock Copolymers ◽  
Brownian Dynamics Simulation ◽  
Selective Solvent ◽  
Bcc Lattice ◽  
Dynamics Simulations ◽  
Brownian Dynamics Simulations ◽  
Poor Solvent

ABSTRACTWe performed Brownian Dynamics simulations of multiblock copolymers of A and B polymers in a solvent selective for the A block at a volume fraction of 20%. Tri-, penta- and heptabocks were simulated. Fourier transformation reveals micellar clusters arranged in a BCC lattice, in agreement with scattering experiments. The clusters were analyzed using a percolation approach and we observed larger clusters when the outermost block was in the poor solvent condition. The ratio of number of loops to bridges decreases as the number of blocks in the copolymer increases, as does the polydispersity. Increased penalty of looping as the number of blocks increases leads to a larger number of smaller clusters with more bridges.

Parametric Analysis of Effective Thermal Conductivity Models for Nanofluids

2012 ◽  
Author(s):  
Mohsen Sharifpur ◽  
Tshimanga Ntumba ◽  
Josua P. Meyer
Keyword(s):  
Thermal Conductivity ◽  
Particle Size ◽  
Brownian Motion ◽  
Effective Thermal Conductivity ◽  
Parametric Analysis ◽  
Base Fluid ◽  
Volume Fraction ◽  
Hybrid Models ◽  
The Impact ◽  
Conductivity Models

There is a lack of reported research on comprehensive hybrid models for the effective thermal conductivity of nanofluids that takes into consideration all major mechanisms and parameters. The major mechanisms are the nanolayer, Brownian motion and clustering. The recognized important parameters can be the volume fraction of the nanoparticles, temperature, particle size, thermal conductivity of the nanolayer, thermal conductivity of the base fluid, PH of the nanofluid, and the thermal conductivity of the nanoparticle. Therefore, in this work, a parametric analysis of effective thermal conductivity models for nanofluids was done. The impact of the measurable parameters, like volume fraction of the nanoparticles, temperature and the particle size for the more sited models, were analyzed by using alumina-water nanofluid. The result of this investigation identifies the lack of a hybrid equation for the effective thermal conductivity of nanofluids and, consequently, more research is required in this field.

Simultaneous Measurement of the Effective Thermal Conductivity and Effective Thermal Diffusivity of Nanofluids

2008 ◽  
Author(s):  
S. M. Sohel Murshed ◽  
Kai Choong Leong ◽  
Chun Yang
Keyword(s):  
Thermal Conductivity ◽  
Thermal Diffusivity ◽  
Effective Thermal Conductivity ◽  
Simultaneous Measurement ◽  
Volume Fraction ◽  
Particle Volume Fraction ◽  
Maximum Increase ◽  
Effective Thermal Diffusivity ◽  
Thermal Diffusivities ◽  
Volumetric Loading

A transient double hot-wire technique was developed for precise and simultaneous measurement of the effective thermal conductivity and effective thermal diffusivity of nanofluids. The measured effective thermal conductivities and effective thermal diffusivities of nanofluids were found to be higher than those of base fluids and they increase significantly with increasing volume fraction of nanoparticles. The increments of the thermal diffusivities were found to be slightly larger compared to the thermal conductivity values. For example, at 5% volumetric loading of TiO2 nanoparticles of 15 nm and 10 × 40 nm in ethylene glycol, the maximum increase in effective thermal conductivity was found to be 17% and 20%, whereas the maximum increase in effective thermal diffusivity was 25% and 29%, respectively. Besides particle volume fraction, particle material, particle size and the nature of the base fluid were found to have influence on the effective thermal conductivity and diffusivity of nanofluids. Based on the calibration results obtained for the base fluids the measurement error was estimated to be within 1.2 to 2%.

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.

Prediction of effective thermal conductivity of nanofluids by modifying renovated Maxwell model

2016 ◽  
Vol 30 (3) ◽  
pp. 289-301 ◽  
Author(s):  
Deepti Chauhan ◽  
Nilima Singhvi
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Base Fluid ◽  
Volume Fraction ◽  
Maxwell Model ◽  
Theoretical Models ◽  
Solid Particles ◽  
Nanoparticle Volume Fraction ◽  
Suspended Nanoparticles

Nanofluids, which are formed by suspending nanoparticles into conventional fluids, exhibit anomalously high thermal conductivity. Renovated Maxwell model was developed by Choi in which the presence of very thin nanolayer surrounding the solid particles was considered, which can measurably increase the effective thermal conductivity of nanofluids. A new model is proposed by introducing a fitting parameter χ in the renovated Maxwell model, which accounts for nanolayer, nonuniform sizes of filler nanoparticles together with aggregation. The model shows that the effective thermal conductivity of nanofluids is a function of the thickness of the nanolayer, the nanoparticle size, the nanoparticle volume fraction and the thermal conductivities of suspended nanoparticles, nanolayer and base fluid. The validation of the model is done by applying the results obtained by the experiments on nanofluids, other theoretical models, and artificial neural network technique. The uncertainty of the present measurements is estimated to be within 5% for the effective thermal conductivity.

Study of the Effective Thermal Conductivity of Nanofluids

2005 ◽  
Author(s):  
Ratnesh K. Shukla ◽  
Vijay K. Dhir
Keyword(s):  
Thermal Conductivity ◽  
Particle Size ◽  
Brownian Motion ◽  
Effective Thermal Conductivity ◽  
Volume Fraction ◽  
Experimental Results ◽  
Homogeneous Liquid ◽  
Size Dependent ◽  
Water Based ◽  
Base Liquid

Nanofluids, that is liquids containing nanometer sized metallic or non-metallic solid nanoparticles, show an increase in thermal conductivity compared to that of the base liquid. In this paper a model for thermal conductivity of nanofluids based on the theory of Brownian motion of particles in a homogeneous liquid combined with the macroscopic Hamilton-Crosser model is presented. The model is shown to predict a temperature and particle size dependent thermal conductivity. Comparison between the predicted and experimental results show that the model is able to accurately predict the temperature and volume fraction dependence of the thermal conductivity of water based alumina and gold nanofluids.

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