Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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ResNet-Based Simulations for a Heat-Transfer Model Involving an Imperfect Contact

  • Guangxing, Wang (Computer and Big Data Science, JiuJiang University) ;
  • Gwanghyun, Jo (Department of Mathematics, Kunsan National University) ;
  • Seong-Yoon, Shin (School of Computer Information & Communication Engineering, Kunsan National University)
  • Received : 2022.10.12
  • Accepted : 2022.11.05
  • Published : 2022.12.31
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Abstract

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

Keywords

Acknowledgement

The second author (G. Jo) was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1C1C1A01005396). We would like to thank Editage (www.editage.co.kr) for English language editing.

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