The homogenization-based data-driven approach to predict uniaxial effective conductivity of low thermal composite materials
Email:
viettb@utc.edu.vn
Keywords:
homogenization, conductivity, machine learning, self-consistent, finite element
Abstract
Predicting the thermal conductivity coefficient of longitudinal fiber-reinforced materials is an important and significant problem due to its wide practical application in the structure of these materials. To achieve this factor, we redefine the classical homogenization formula based on a generalized self-consistent model with a free parameter. And then, we build a database of 1700 data points using the periodic homogenization finite element method, with varying contrast ratios of the inclusion phase to the matrix phase conductivity ranging from 1 to 0, and varying volume fractions of the inclusion phase from 0 to the maximum packing density. The combination of the dataset and the generalized self-consistent model allows us to determine the relationship between the free parameter and the material inputs. By combining the approximation function with the original analytical model, we obtain a simple, explicit, and easily applicable hybrid homogenization model that provides excellent predictive capabilities for computational resultsReferences
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[5]. A. Kalamkarov, I. Andrianov, V. Danishevskyy, Asymptotic Homogenization of Composite Materials and Structures, Applied Mechanics Reviews, 62 (2009). https://doi.org/10.1115/1.3090830
[6]. R. Ewing, O. Iliev, R. Lazarov, I. Rybak, J. Willems, A Simplified Method for Upscaling Composite Materials with High Contrast of the Conductivity, SIAM J. Sci. Comput., 31 (2009) 2568–2586. https://doi.org/10.1137/080731906
[7]. M. Bornert, C. Stolz, A. Zaoui, Morphologically representative pattern-based bounding in elasticity, Journal of the Mechanics and Physics of Solids, 44 (1996) 307–331. https://doi.org/10.1016/0022-5096(95)00083-6
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[9]. A. Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd edition, O’Reilly Media, (2017).
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Received
03/07/2023
Revised
25/08/2023
Accepted
30/08/2023
Published
15/10/2023
Type
Research Article
How to Cite
Lê Cảnh, H., Lê Bá, A., Bùi Thị, L., & Trần Bảo, V. (1697302800). The homogenization-based data-driven approach to predict uniaxial effective conductivity of low thermal composite materials. Transport and Communications Science Journal, 74(8), 934-945. https://doi.org/10.47869/tcsj.74.8.7
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