Numerical investigation of heat transfer in 2D particulate composites: effects of filler concentration and geometric disorder
- 1Department of Physics, Odessa State Academy of Civil Engineering and Architecture, Didrikhsona 4, Odessa, 65029, Ukraine
Abstract
Predicting the effective thermal conductivity of particulate composites is challenging due to the interplay between filler concentration, geometric disorder, and property contrast. This study utilizes a parametric stochastic framework and a perturbed lattice algorithm to investigate 2D microstructures with controlled spatial perturbation ( s ) and size polydispersity ( dr ). Steady-state heat conduction is solved via finite element analysis, while ensemble averaging ensures statistical reliability. Results reveal that conductivity enhancement saturates at a volume fraction of ϕ ≈ 0.11 without a percolation threshold. Notably, mean conductivity exhibits a non-monotonic dependence on spatial disorder, peaking at s ≈ 0.4, which also corresponds to maximum statistical variability. At high phase contrast, properties converge to the lower Hashin-Shtrikman bound, confirming that performance is governed by matrix-phase constrictions rather than intrinsic filler conductivity. These findings demonstrate that matrix morphology imposes a fundamental limit on heat transport in isolated dispersions, suggesting that significant enhancement requires engineered percolating networks.
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BibTeX
@article{pysarenko2026numerical,
title = {{Numerical investigation of heat transfer in 2D particulate composites: effects of filler concentration and geometric disorder}},
author = {Alexander Pysarenko and Artem Chystiakov},
journal = {International Journal of Thermofluids},
year = {2026},
month = oct,
doi = {10.1016/j.ijft.2026.101718},
url = {https://doi.org/10.1016/j.ijft.2026.101718},
note = {CC BY},
}
RIS
TY - JOUR
TI - Numerical investigation of heat transfer in 2D particulate composites: effects of filler concentration and geometric disorder
AU - Alexander Pysarenko
AU - Artem Chystiakov
PY - 2026
DA - 2026/10/30
JO - International Journal of Thermofluids
DO - 10.1016/j.ijft.2026.101718
UR - https://doi.org/10.1016/j.ijft.2026.101718
AB - Predicting the effective thermal conductivity of particulate composites is challenging due to the interplay between filler concentration, geometric disorder, and property contrast. This study utilizes a parametric stochastic framework and a perturbed lattice algorithm to investigate 2D microstructures with controlled spatial perturbation ( s ) and size polydispersity ( dr ). Steady-state heat conduction is solved via finite element analysis, while ensemble averaging ensures statistical reliability. Results reveal that conductivity enhancement saturates at a volume fraction of ϕ ≈ 0.11 without a percolation threshold. Notably, mean conductivity exhibits a non-monotonic dependence on spatial disorder, peaking at s ≈ 0.4, which also corresponds to maximum statistical variability. At high phase contrast, properties converge to the lower Hashin-Shtrikman bound, confirming that performance is governed by matrix-phase constrictions rather than intrinsic filler conductivity. These findings demonstrate that matrix morphology imposes a fundamental limit on heat transport in isolated dispersions, suggesting that significant enhancement requires engineered percolating networks.
SN - 2666-2027
ER -