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Fractal-like tree networks reducing the thermal conductivity
1Department of Physics and Center for Computational Science and Engineering, National University of Singapore, Singapore 117542. yuboming2003@yahoo.com.cn
Summary
This study shows that H-shaped fractal-like tree networks embedded in composites significantly lower thermal conductivity. Network structure, branch length, diameter ratio, and density are key factors influencing this reduction.
Area of Science:
- Materials Science
- Physics
- Composite Materials
Background:
- Understanding thermal conductivity in composite materials is crucial for designing advanced materials.
- Fractal-like structures offer unique properties for manipulating material characteristics.
Purpose of the Study:
- To investigate the effective thermal conductivity of composites containing self-similar H-shaped fractal-like tree networks.
- To determine the relationship between network structure and thermal conductivity.
Main Methods:
- Analysis of effective thermal conductivity in composites with embedded H-shaped fractal-like tree networks.
- Evaluation of parameters such as branch length, diameter ratio (beta), and network density.
Main Results:
- Effective thermal conductivity decreases with longer branches, smaller diameter ratios (beta), and denser networks.
- The thermal conductivity of these fractal-like networks deviates from Murray's law.
- Embedded networks can reduce the composite's thermal conductivity by several orders of magnitude.
Conclusions:
- The structure of embedded fractal-like tree networks significantly impacts composite thermal conductivity.
- These networks offer a method to substantially decrease thermal conductivity compared to traditional structures.
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