Curvature Effect on the Thermal Conductivity of Nanowires

2008 ◽  
Author(s):  
Liang-Chun Liu ◽  
Mei-Jiau Huang ◽  
Ronggui Yang
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo ◽  
Effective Thermal Conductivity ◽  
Silicon Nanowires ◽  
Phonon Transport ◽  
Curvature Effect ◽  
Transport Efficiency ◽  
Directional Preference ◽  
Monte Carlo Simulator ◽  
Thermal Conductivities

Directional preference of the ballistic phonon transport plays an important role in the effective thermal conductivity of nanostructures. Curved nanowires can have very different thermal conductivities from straight ones. In this work, a Monte-Carlo simulator is developed and used to investigate the curvature effect on the phonon transport in silicon nanowires. The results show that the curvature of geometry does not alter the phonon transport efficiency in large wires but decreases the effective thermal conductivity in their nano-sized counterparts.

scholarly journals Thermal transport in nanocrystalline Si and SiGe by ab initio based Monte Carlo simulation

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lina Yang ◽  
Austin J. Minnich
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo ◽  
Ab Initio ◽  
Grain Boundaries ◽  
Thermal Transport ◽  
Phonon Scattering ◽  
Phonon Dispersion ◽  
Computational Study ◽  
Phonon Transport ◽  
Nanocrystalline Si

Abstract Nanocrystalline thermoelectric materials based on Si have long been of interest because Si is earth-abundant, inexpensive, and non-toxic. However, a poor understanding of phonon grain boundary scattering and its effect on thermal conductivity has impeded efforts to improve the thermoelectric figure of merit. Here, we report an ab-initio based computational study of thermal transport in nanocrystalline Si-based materials using a variance-reduced Monte Carlo method with the full phonon dispersion and intrinsic lifetimes from first-principles as input. By fitting the transmission profile of grain boundaries, we obtain excellent agreement with experimental thermal conductivity of nanocrystalline Si [Wang et al. Nano Letters 11, 2206 (2011)]. Based on these calculations, we examine phonon transport in nanocrystalline SiGe alloys with ab-initio electron-phonon scattering rates. Our calculations show that low energy phonons still transport substantial amounts of heat in these materials, despite scattering by electron-phonon interactions, due to the high transmission of phonons at grain boundaries, and thus improvements in ZT are still possible by disrupting these modes. This work demonstrates the important insights into phonon transport that can be obtained using ab-initio based Monte Carlo simulations in complex nanostructured materials.

Qualitative Equivalence Between Electrical Percolation Threshold and Effective Thermal Conductivity in Polymer/Carbon Nanocomposites

2011 ◽  
Vol 134 (1) ◽  
Author(s):  
Majid Baniassadi ◽  
Akbar Ghazavizadeh ◽  
Yves Rémond ◽  
Said Ahzi ◽  
David Ruch ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Percolation Threshold ◽  
Effective Thermal Conductivity ◽  
Electrical Percolation ◽  
Aspect Ratios ◽  
Wide Range ◽  
Statistical Continuum ◽  
Electrical Percolation Threshold

In this study, a qualitative equivalence between the electrical percolation threshold and the effective thermal conductivity of composites filled with cylindrical nanofillers has been recognized. The two properties are qualitatively compared on a wide range of aspect ratios, from thin nanoplatelets to long nanotubes. Statistical continuum theory of strong-contrast is utilized to estimate the thermal conductivity of this type of heterogeneous medium, while the percolation threshold is simultaneously evaluated using the Monte Carlo simulations. Statistical two-point probability distribution functions are used as microstructure descriptors for implementing the statistical continuum approach. Monte Carlo simulations are carried out for calculating the two-point correlation functions of computer generated microstructures. Finally, the similarities between the effective conductivity properties and percolation threshold are discussed.

scholarly journals Calculations of the Effective Thermal Conductivity in a Model of Syntactic Metallic Hollow Sphere Structures Using a Lattice Monte Carlo Method

2008 ◽  
Vol 273-276 ◽  
pp. 216-221 ◽  
Author(s):  
Thomas Fiedler ◽  
Andreas Öchsner ◽  
Irina V. Belova ◽  
Graeme E. Murch
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Effective Thermal Conductivity ◽  
Hollow Sphere ◽  
Cutting Planes ◽  
Two Dimensional ◽  
Adhesively Bonded ◽  
Lattice Monte Carlo ◽  
The Matrix

In this paper, a Lattice Monte Carlo method is used to determine the effective thermal conductivity in two dimensional models of adhesively bonded metallic hollow sphere structures (MHSS). In contrast to earlier approaches, more realistic distributions of spheres without the simplification of cubic symmetric arrangements are considered in this study. For the Monte Carlo analyses, two-dimensional periodic lattices representing different cutting planes through MHSS are generated. Therefore, an algorithm is used which sequentially fills the lattice by adding cut spherical shells and inclusions in the matrix. Another focus of this work is the analysis of the influence of different geometric circle distributions on the effective thermal conductivity. The findings of the random arrangements are also compared to a regular primitive cubic arrangement and with a Maxwell-type approach.

Monte Carlo Modeling of Phonon Transport Using Scattering Phase Functions

2008 ◽  
Author(s):  
Neil Zuckerman ◽  
Jennifer R. Lukes
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo ◽  
Numerical Methods ◽  
Ballistic Transport ◽  
Simulation Method ◽  
Phonon Transport ◽  
Nanometer Scale ◽  
Boltzmann Transport ◽  
Length Scales ◽  
Scale Structures

The calculation of heat transport in nonmetallic materials at small length scales is important in the design of thermoelectric and electronic materials. New designs with quantum dot superlattices (QDS) and other nanometer-scale structures can change the thermal conductivity in ways that are difficult to model and predict. The Boltzmann Transport Equation can describe the propagation of energy via mechanical vibrations in an analytical fashion but remains difficult to solve for the problems of interest. Numerical methods for simulation of propagation and scattering of high frequency vibrational quanta (phonons) in nanometer-scale structures have been developed but are either impractical at micron length scales, or cannot truly capture the details of interactions with nanometer-scale inclusions. Monte Carlo (MC) models of phonon transport have been developed and demonstrated based on similar numerical methods used for description of electron transport [1-4]. This simulation method allows computation of thermal conductivity in materials with length scales LX in the range of 10 nm to 10 μm. At low temperatures the model approaches a ballistic transport simulation and may function for even larger length scales.

Micro- and Nanoscale Phonon Heat Transport in Silicon

Volume 4 ◽  
2004 ◽  
Author(s):  
Y. Ju
Keyword(s):  
Thermal Conductivity ◽  
Silicon Nanowires ◽  
Phonon Dispersion ◽  
Transport Model ◽  
Phonon Transport ◽  
Conductivity Data ◽  
Phonon Modes ◽  
Conductivity Model ◽  
Thermal Conductivity Data ◽  
Thermal Conductivity Model

Micro- and nanoscale energy transport in semiconductors is one of the critical research areas for emerging nano-electronics. Key features of phonon dispersion curves are re-examined, which motivates the use of phonon density of states obtained from ab initio calculations as a basis for constructing a semi-phenomenological thermal conductivity model. Thermal conductivity data on silicon nanowires are analyzed to identify dominant phonon modes. The consistency of the present thermal conductivity model is examined by comparing its prediction with the thermal conductivity data from bulk germanium samples with controlled amount of point defects. The thermal conductivity modeling study provides input parameters for a two-fluid phonon transport model for silicon and related semiconductors, which can play an important role in computer aided design of nanoelectronic devices and simulation of ultra-fast phenomena.

scholarly journals Investigation of Effective Thermal Conductivity for Ordered and Randomly Packed Bed with Thermal Resistance Network Method

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1666 ◽  
Author(s):  
Jian Yang ◽  
Yingxue Hu ◽  
Qiuwang Wang
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Thermal Resistance ◽  
Effective Thermal Conductivity ◽  
Packed Bed ◽  
Random Packing ◽  
Packing Factor ◽  
Resistance Network ◽  
Network Method ◽  
Thermal Conductivities

In the present paper, the effective thermal conductivities of Li4SiO4-packed beds with both ordered and random packing structures were investigated using thermal resistance network methods based on both an Ohm’s law model and a Kirchhoff’s law model. The calculation results were also validated and compared with the numerical and experimental results. Firstly, it is proved that the thermal resistance network method based on the Kirchhoff’s law model proposed in the present study is reliable and accurate for prediction of effective thermal conductivities in a Li4SiO4-packed bed, while the results calculated with the Ohm’s law model underestimate both ordered and random packings. Therefore, when establishing a thermal resistance network, the thermal resistances should be connected along the main heat transfer direction and other heat transfer directions as well in the packing unit. Otherwise, both the total heat flux and effective thermal conductivity in the packing unit will be underestimated. Secondly, it is found that the effect of the packing factor is remarkable. The effective thermal conductivity of a packed bed would increase as the packing factor increases. Compared with random packing at similar packing factor, the effective thermal conductivity of packed bed would be further improved with an ordered packing method.

Numerical determination of pressure-dependent effective thermal conductivity in Berea sandstone

2021 ◽  
Author(s):  
Mirko Siegert ◽  
Marcel Gurris ◽  
Erik Hans Saenger
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Numerical Model ◽  
Effective Thermal Conductivity ◽  
Rock Sample ◽  
Rock Physics ◽  
Contact Phase ◽  
Data Set ◽  
Computed Tomography Images ◽  
Thermal Conductivities

<p>Within the scope of the present work, the pressure-dependent effective thermal conductivity of rock samples is simulated. Our workflow can be assigned to the field of digital rock physics. In a first step, a 3D micro-CT scan of a rock sample is taken. Subsequently, the resulting greyscale images are analysed and segmented depending on the occurring phases. Based on this data set, a computational mesh is created and the corresponding thermal conductivities are assigned to each phase. Finally the numerical simulations can be carried out.<br>For the representation of the pressure dependency we use the approach proposed by Saenger [1]. By making use of the watershed algorithm, boundaries between the individual grains of the rock sample are detected and assigned to an artificial contact phase. In the course of several simulations, the thermal conductivity of the contact phase is continuously increased. Starting with the thermal conductivity of the pore phase and ending with the thermal conductivity of the grain phase. A linear correlation is used to match the thermal conductivity of the contact phase with the pressure of a given experimental data set. This enables a direct comparison between simulation and measurement.<br>In a further step, the numerical model is calibrated to optimise the agreement between experimental data and simulation results. In particular, starting from two calibration points of the experimental data set, an adjustment of the thermal conductivities in the numerical model is carried out. While the thermal conductivity of the pore phase is held constant during the whole calibration process, thermal conductivities of the grain and contact phase are adjusted.</p><p>References<br>[1] Saenger et al. 2016. Analysis of high-resolution X-ray computed tomography images of Bentheim sandstone under elevated confining pressures. Geophysical Prospecting, 64(4), 848–859.</p><p> </p>

Measurements of the Intrinsic Thermal Conductivity of Individual Multi-Wall Carbon Nanotubes

2011 ◽  
Author(s):  
Juekuan Yang ◽  
Scott W. Waltermire ◽  
Yang Yang ◽  
Deyu Li ◽  
Yunfei Chen
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Heat Source ◽  
Thermal Resistance ◽  
Effective Thermal Conductivity ◽  
Experimental Studies ◽  
Contact Thermal Resistance ◽  
Multi Wall Carbon Nanotubes ◽  
The Past ◽  
Thermal Conductivities

Thermal transport through carbon nanotubes (CNTs) attracted a lot of attention over the past decade. Several experimental studies have been carried out to determine the thermal conductivities of CNTs [1–3]. However, the measurements are based on an individual CNT sample between two suspended membranes and the results actually include both the intrinsic thermal resistance of the CNT and the contact thermal resistance between the CNT and the two suspended membranes that serve as a heat source and a heat sink. Hence, the effective thermal conductivity extracted from these measurements should be lower than the intrinsic thermal conductivities of the CNTs measured. To minimize the contact thermal resistance, electron beam induce deposition (EBID) of different metals has been used to increase the contact area between the CNT and the heat source and sink [3,4]. However, it is still not clear how effective this treatment is and to what level the effective thermal conductivity obtained after the EBID treatment reflects the intrinsic one.

Sign in / Sign up

Export Citation Format

Share Document