A robust lattice Boltzmann scheme for high-throughput predicting effective thermal conductivity of reinforced composites

Mingshan Yang, Xiangyu Li and Weiqiu Chen

Applied Energy, 2024, vol. 371, issue C, No S0306261924011097

Abstract: Accurately predicting effective thermal conductivity is of great importance for the design and performance evaluation of emerging composites. In this paper, an efficient and implementation-friendly lattice Boltzmann (LB) scheme for predicting the effective thermal conductivity of 3D complex structures is proposed. The key innovation is that the optimum convergence parameter of the 3D thermal LB method is found, which enables the LB equation to converge to steady heat conduction equation with the fastest speed and without losing any accuracy. To deal with the thermal contact resistance between different components, an interface treatment scheme is derived. In comparison with the existing schemes, the present scheme enjoys several hundred times higher computational efficiency. By virtue of this LB scheme, the effective thermal conductivity of the reinforced composites with different dimensional fillers are systematically calculated, and a comprehensive machine learning model is developed. This work provides a powerful numerical tool for high-throughput simulations of the 3D representative volume elements with high thermal conductivity ratios and large grid numbers. It may facilitate the application of data-driven techniques in study of the thermal transport properties of emerging composite materials and structures.

Keywords: High-throughput simulations; Lattice Boltzmann method; Phase change composites; Machine learning prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123726

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