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Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method

Author

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  • Wenzhi Xu

    (Key Laboratory of Ministry of Education for Coastal Disaster and Protection, Hohai University, Nanjing 210098, China
    Center for Numerical Simulation Software in Engineering and Sciences, College of Mechanics and Materials, Hohai University, Nanjing 211100, China)

  • Zhuojia Fu

    (Key Laboratory of Ministry of Education for Coastal Disaster and Protection, Hohai University, Nanjing 210098, China
    Center for Numerical Simulation Software in Engineering and Sciences, College of Mechanics and Materials, Hohai University, Nanjing 211100, China)

  • Qiang Xi

    (Key Laboratory of Ministry of Education for Coastal Disaster and Protection, Hohai University, Nanjing 210098, China
    Center for Numerical Simulation Software in Engineering and Sciences, College of Mechanics and Materials, Hohai University, Nanjing 211100, China)

Abstract

A machine learning strategy based on the semi-analytical singular boundary method (SBM) is presented for the thermal conductivity identification of functionally graded materials (FGMs). In this study, only the temperature or heat flux on the surface or interior of FGMs can be measured by the thermal sensors, and the SBM is used to construct the database of the relationship between the thermal conductivity and the temperature distribution of the functionally graded structure. Based on the aforementioned constructed database, the artificial neural network-based machine learning strategy was implemented to identify the thermal conductivity of FGMs. Finally, several benchmark examples are presented to verify the feasibility, robustness, and applicability of the proposed machine learning strategy.

Suggested Citation

  • Wenzhi Xu & Zhuojia Fu & Qiang Xi, 2022. "Thermal Conductivity Identification in Functionally Graded Materials via a Machine Learning Strategy Based on Singular Boundary Method," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:458-:d:739126
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    Cited by:

    1. Yuan Gao & Yueling Guo & Nurul Atiqah Romli & Mohd Shareduwan Mohd Kasihmuddin & Weixiang Chen & Mohd. Asyraf Mansor & Ju Chen, 2022. "GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 10(11), pages 1-28, June.

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