he Thermal Contact Resistance Dataset and the Artificial Intelligence-Driven Prediction of Thermal Contact Resistance in Multi-material Systems
In the current era of artificial intelligence, the advancement of high-performance computing based on electronic devices is hindered by thermal contact resistance. To accurately predict this resistance, we established a comprehensive database derived from extensive experimental work documented in previous studies. By employing machine learning algorithms, we developed a prediction model for thermal contact resistance that utilizes this dataset. This model can predict the thermal contact resistance among all learned materials, demonstrating a significant degree of general applicability. Our model shows strong performance on the test set (with a coefficient of determination of 0.982) , reflecting a high level of predictive accuracy. Additionally, the interpretability analyses conducted on the machine learning model are consistent with established theories of thermal contact resistance, further confirming the models accuracy. We anticipate that this database will support the development of thermal contact resistance prediction models and that our model will enhance the precision of thermal contact resistance predictions.
Pei Yao Guo、Zhuo Yan He、Ping Zhang、ao Lin、Man Zhou
School of Mechanical and Electrical Engineering, Guilin University of Electronic technology, No. 1 Jinji Road, Guilin, Guangxi, 541004, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic technology, No. 1 Jinji Road, Guilin, Guangxi, 541004, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic technology, No. 1 Jinji Road, Guilin, Guangxi, 541004, ChinaCollege of Aerospace Engineering, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing, 400044, China;School of Artificial Intelligence, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin, Guangxi, 541004, ChinaSchool of Mechanical and Electrical Engineering, Guilin University of Electronic technology, No. 1 Jinji Road, Guilin, Guangxi, 541004, China
热力工程、热机计算技术、计算机技术
hermal contact resistanceThermal managementDatasetMachine learningInterpretability
hermal contact resistanceThermal managementDatasetMachine learningInterpretability
Pei Yao Guo,Zhuo Yan He,Ping Zhang,ao Lin,Man Zhou.he Thermal Contact Resistance Dataset and the Artificial Intelligence-Driven Prediction of Thermal Contact Resistance in Multi-material Systems[EB/OL].(2025-04-11)[2025-08-02].https://chinaxiv.org/abs/202504.00193.点此复制
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