A spatially-varying relaxation parameter Lattice Boltzmann Method (SVRP-LBM) for predicting the effective thermal conductivity of composite material

Xinyuan Ke, Yu Duan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Functional filler-reinforced composite materials play critical roles in thermal management in various engineering applications. In this study, an in-house coded spatially-varying relaxation parameter Lattice Boltzmann Method (SVRP-LBM) solver has been developed for predicting the effective thermal conductivity (ETC) of simulated composite materials. A randomly dispersed filler generator (RDFG) incorporating Monte Carlo random sampling method has been developed for reconstructing the microstructure of composite materials. The artificial composite materials with functional fillers of different geometries and particle size are studied. The SVRP-LBM is validated against FVM perditions and theoretical models. The spatially-varying relaxation parameters method has been used to reflect materials with different thermophysical properties, including the interfacial contact resistance between the matrix-filler interfaces. It is demonstrated that the lowest relaxation parameters should be around 1.0 in order to achieve a higher accuracy of LBM predictions. The effects of filler geometry and particle sizes on the ETC are also assessed. The shape and orientation of the anisotropic filler have strong effects on the ETC. After the geometry of the filler in the numerical models being adjusted accordingly to the real fillers, the predictions show good agreement with experimental data. All in all, the SVRP-LBM solver has shown good capability and accuracy for predicting the ETC of composite material.

Original languageEnglish
Article number109080
JournalComputational Materials Science
Volume169
Early online date27 Jun 2019
DOIs
Publication statusPublished - 1 Nov 2019

Keywords

  • Composite materials
  • Effective thermal conductivity
  • Lattice Boltzmann
  • Numerical prediction
  • Spatially-varying relaxation parameters

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

Cite this

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abstract = "Functional filler-reinforced composite materials play critical roles in thermal management in various engineering applications. In this study, an in-house coded spatially-varying relaxation parameter Lattice Boltzmann Method (SVRP-LBM) solver has been developed for predicting the effective thermal conductivity (ETC) of simulated composite materials. A randomly dispersed filler generator (RDFG) incorporating Monte Carlo random sampling method has been developed for reconstructing the microstructure of composite materials. The artificial composite materials with functional fillers of different geometries and particle size are studied. The SVRP-LBM is validated against FVM perditions and theoretical models. The spatially-varying relaxation parameters method has been used to reflect materials with different thermophysical properties, including the interfacial contact resistance between the matrix-filler interfaces. It is demonstrated that the lowest relaxation parameters should be around 1.0 in order to achieve a higher accuracy of LBM predictions. The effects of filler geometry and particle sizes on the ETC are also assessed. The shape and orientation of the anisotropic filler have strong effects on the ETC. After the geometry of the filler in the numerical models being adjusted accordingly to the real fillers, the predictions show good agreement with experimental data. All in all, the SVRP-LBM solver has shown good capability and accuracy for predicting the ETC of composite material.",
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AB - Functional filler-reinforced composite materials play critical roles in thermal management in various engineering applications. In this study, an in-house coded spatially-varying relaxation parameter Lattice Boltzmann Method (SVRP-LBM) solver has been developed for predicting the effective thermal conductivity (ETC) of simulated composite materials. A randomly dispersed filler generator (RDFG) incorporating Monte Carlo random sampling method has been developed for reconstructing the microstructure of composite materials. The artificial composite materials with functional fillers of different geometries and particle size are studied. The SVRP-LBM is validated against FVM perditions and theoretical models. The spatially-varying relaxation parameters method has been used to reflect materials with different thermophysical properties, including the interfacial contact resistance between the matrix-filler interfaces. It is demonstrated that the lowest relaxation parameters should be around 1.0 in order to achieve a higher accuracy of LBM predictions. The effects of filler geometry and particle sizes on the ETC are also assessed. The shape and orientation of the anisotropic filler have strong effects on the ETC. After the geometry of the filler in the numerical models being adjusted accordingly to the real fillers, the predictions show good agreement with experimental data. All in all, the SVRP-LBM solver has shown good capability and accuracy for predicting the ETC of composite material.

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