ResNet-Based Simulations for a Heat-Transfer Model Involving an Imperfect Contact
- Authors
- Wang,Guangxing; 조광현; 신성윤
- Issue Date
- Dec-2022
- Publisher
- The Korean Institute of Information and Commucation Engineering
- Keywords
- Composite material; deep learning; heat transfer; Kapitza thermal resistance; ResNet
- Citation
- Journal of Information and Communication Convergence Engineering, v.20, no.4, pp 303 - 308
- Pages
- 6
- Indexed
- SCOPUS
KCI
- Journal Title
- Journal of Information and Communication Convergence Engineering
- Volume
- 20
- Number
- 4
- Start Page
- 303
- End Page
- 308
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115254
- DOI
- 10.56977/jicce.2022.20.4.303
- ISSN
- 2234-8255
2234-8883
- 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.
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