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Modeling Thermal Conductivity of Aligned CNT-Matrix Composites With Increasing Volume Fraction

[+] Author Affiliations
Diana Grandio, Drazen Fabris

Santa Clara University, Santa Clara, CA

Paper No. HT2016-7145, pp. V001T04A001; 10 pages
doi:10.1115/HT2016-7145
From:
  • ASME 2016 Heat Transfer Summer Conference collocated with the ASME 2016 Fluids Engineering Division Summer Meeting and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels
  • Volume 1: Heat Transfer in Energy Systems; Thermophysical Properties; Theory and Fundamentals in Heat Transfer; Nanoscale Thermal Transport; Heat Transfer in Equipment; Heat Transfer in Fire and Combustion; Transport Processes in Fuel Cells and Heat Pipes; Boiling and Condensation in Macro, Micro and Nanosystems
  • Washington, DC, USA, July 10–14, 2016
  • Conference Sponsors: Heat Transfer Division
  • ISBN: 978-0-7918-5032-9
  • Copyright © 2016 by ASME

abstract

In prior work an effective medium approach (EMA) has been developed to evaluate composite physical properties such as thermal conductivity, dielectric function or elastic modulus (C.-W. Nan, Prog. Mat. Sci. V. 37, 1993). This model combined with the Kapitza interface resistance can predict the effective thermal conductivity of randomly dispersed long fibers for a very low volume fraction (f < 0.01). The present study compares finite-element (FEA) computations and the EMA model for CNT-matrix compositions with low to moderate volume fractions, 0.001 to 0.02. The FEA results obtained show that the EMA model underestimates the effective thermal conductivity of the composite when the particles are very close to each other, even for small particle volume fractions. For aligned fibers the Kaptiza resistance cannot be neglected in the longitudinal direction. This paper proposes a general correction function for the dependence on particle to particle interaction based on the near neighbor distances and the number of near neighbors. This correction function reduces the EMA under prediction to within several percent (< 5%) in most cases.

Copyright © 2016 by ASME

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