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Thermal Conductivity Prediction of Metal Matrix Particulate Composites: Theoretical Methodology and Application

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Abstract

To make more accurate predictions of the effective thermal conductivity (ETC) of the composites, a systematic method for predicting the effective thermal conductivity of metal matrix particle composites with arbitrarily shaped particles was proposed, and the geometry of random particles with controlled shape characteristics is reconstructed. In addition, the geometric vertices of the reconstructed particles are used to characterize the morphology of inclusions with complex profile in two-dimensional isotropic elasticity, and its explicit expression for the Eshelby tensor are explored. Moreover, the material mismatch between the particles and the matrix phase is simulate using a continuously distributed source field based on the Eshelby's equivalent inclusion method. The relationship between micro-structure and effective performance is established. Finally, the effective thermal conductivity of CuCr alloys was predicted using the ETC prediction model. Through the comparison of the numerical simulations, experiments, and calculations, the results show that the ETC model has reliable predictive capability.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture & Technology, Xi’an, 710055, China

    Zhen Chen, Fazhan Wang & Wenbo Yu

  2. College of Humanities, University of Exeter, Exeter, EX4 4QJ, UK

    Yixuan Wang

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  1. Zhen Chen
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  2. Fazhan Wang
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  3. Wenbo Yu
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Contributions

ZC and FW wrote the main manuscript text. WY prepared Table 1 and 2, Figs. 1, 2, 3, 4, 5 and 9. YW prepared Table 1 and Figs. 6, 7, 8, 10, 11. All author reviewed the manuscript.

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Correspondence to Fazhan Wang.

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Chen, Z., Wang, F., Yu, W. et al. Thermal Conductivity Prediction of Metal Matrix Particulate Composites: Theoretical Methodology and Application. Int J Thermophys 44, 94 (2023). https://doi.org/10.1007/s10765-023-03204-3

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  • DOI: https://doi.org/10.1007/s10765-023-03204-3

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