2015
DOI: 10.4208/cicp.2014.m360
|View full text |Cite
|
Sign up to set email alerts
|

Lattice Boltzmann Modeling of Thermal Conduction in Composites with Thermal Contact Resistance

Abstract: Abstract. The effective thermal conductivity of composite materials with thermal contact resistance at interfaces is studied by lattice Boltzmann modeling in this work. We modified the non-dimensional partial bounce-back scheme, proposed by Han et al. [Int. J. Thermal Sci., 2008. 47: 1276-1283, to introduce a real thermal contact resistance at interfaces into the thermal lattice Boltzmann framework by re-deriving the redistribution function of heat at the phase interfaces for a corrected dimensional formulatio… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 24 publications
(7 citation statements)
references
References 43 publications
(77 reference statements)
0
7
0
Order By: Relevance
“…Based on the solution of the evolution variable at mesoscale, the macroscopic local concentration and mass diffusive flux can be calculated through statistical process [32]: Once the mass diffusion is solved, the effective diffusion coefficient can be obtained based on the Fick's law [23]:…”
Section: Lattice Boltzmann Methodsmentioning
confidence: 99%
“…Based on the solution of the evolution variable at mesoscale, the macroscopic local concentration and mass diffusive flux can be calculated through statistical process [32]: Once the mass diffusion is solved, the effective diffusion coefficient can be obtained based on the Fick's law [23]:…”
Section: Lattice Boltzmann Methodsmentioning
confidence: 99%
“…Recognition of the significance of the sub-resolution pore space has prompted a sizeable number of researchers in the last couple of years to investigate ways to take this pore space into account explicitly in Lattice-Boltzmann models of water movement in soils, following Gao and Sharma (1994) and Freed (1998) . The resulting “Gray” or “Partial-Bounce-Back” (PBB) Lattice-Boltzmann models consider that each voxel in the original, grayscale CT images has a given probability of penetration by water or solutes, and therefore a complementary probability that water or solute particles that penetrate the voxel eventually bounce back to their previous positions (e.g., Sukop and Thorne, 2006 ; Chen and Zhu, 2008 ; Han et al, 2008 ; Walsh et al, 2009 ; Jones and Feng, 2011 ; El Ganaoui et al, 2012 ; Gottardi et al, 2013 ; Walsh and Saar, 2013 ; Zalzale et al, 2013 ; Chen et al, 2014 ; Li et al, 2014 ; Yoshida and Hayashi, 2014 ; Ginzburg et al, 2015 ; Xie et al, 2015 ; Yehya et al, 2015 ; Apourvari and Arns, 2016 ; Bultreys et al, 2016 ; McDonald and Turner, 2016 ; Pereira, 2016 ; Zhang et al, 2016 ). In all this work, considerable advances have been made recently and a number of technical issues have been clarified ( Ginzburg, 2016 ), yet a major experimental hurdle related to the evaluation of the penetrability of sub-resolution pores, which at this point remains an arbitrary parameter in the models.…”
Section: Progress On the Physical Frontmentioning
confidence: 99%
“…Taking into account that this method has got excellent numerical stability and constitutive versatility, it can play an essential role as a simulation tool for modeling of different phenomena and processes. Recently, LBM has been used for different issues, for example: modeling and simulation of liquid jet breakup [15], modeling dendritic solidification under forced and natural convection [16], modeling of thermal conduction in composites with thermal contact resistance [17], modeling of transport phenomena in fuel cells and flow batteries [18], modeling of the additive layer manufacturing [19]. Lattice Boltzmann method consider flows to be composed of a collection of pseudo-particles that are represented by a velocity distribution function, so it can be applied for modeling of different variants of flows, as well as phase transformations in flows.…”
Section: Hybrid Lbm and Ca Modelheat Flow Modulementioning
confidence: 99%