Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning
In this work, the thermal characteristics and steady-state temperatures (SST) of CPU and FPGA of electronic system in nuclear power plant are explored. Finite element analysis is performed to simulate the test process. Furthermore, three machine learning algorithms are used to predict chips temperat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573324004078 |
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| _version_ | 1841545533388226560 |
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| author | Fanyu Wang Dongwei Wang Qiang Deng Hao Yan Qi Chen Yang Zhao |
| author_facet | Fanyu Wang Dongwei Wang Qiang Deng Hao Yan Qi Chen Yang Zhao |
| author_sort | Fanyu Wang |
| collection | DOAJ |
| description | In this work, the thermal characteristics and steady-state temperatures (SST) of CPU and FPGA of electronic system in nuclear power plant are explored. Finite element analysis is performed to simulate the test process. Furthermore, three machine learning algorithms are used to predict chips temperatures at different operating conditions. It is found that when the ambient temperature is 20 °C and all the fans are power-off, the SST of the CPU and FPGA reaches 75 °C and 72 °C, respectively. While when the fans are power-on, the SST of the CPU and FPGA drops to 37.5 °C and 33 °C. When the ambient temperature increases to 55 °C and all the fans are power-on, the SST of the CPU and FPGA is 72.3 °C and 68.2 °C, respectively. The finite element model is verified and used to generate test data. Three machine learning models are verified by predicting the SST of CPU and FPGA under different operating conditions. It is found that M-SVR has better prediction ability than DT and ANN. The findings can be used for chip reliability evaluation of other electronic system devices, and provide a new method for predicting the possible steady-state temperature of chips under different service conditions. |
| format | Article |
| id | doaj-art-b9e919065133479a8539914474795196 |
| institution | Kabale University |
| issn | 1738-5733 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj-art-b9e919065133479a85399144747951962025-01-12T05:24:39ZengElsevierNuclear Engineering and Technology1738-57332025-01-01571103159Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learningFanyu Wang0Dongwei Wang1Qiang Deng2Hao Yan3Qi Chen4Yang Zhao5Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaCorresponding author.; Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaIn this work, the thermal characteristics and steady-state temperatures (SST) of CPU and FPGA of electronic system in nuclear power plant are explored. Finite element analysis is performed to simulate the test process. Furthermore, three machine learning algorithms are used to predict chips temperatures at different operating conditions. It is found that when the ambient temperature is 20 °C and all the fans are power-off, the SST of the CPU and FPGA reaches 75 °C and 72 °C, respectively. While when the fans are power-on, the SST of the CPU and FPGA drops to 37.5 °C and 33 °C. When the ambient temperature increases to 55 °C and all the fans are power-on, the SST of the CPU and FPGA is 72.3 °C and 68.2 °C, respectively. The finite element model is verified and used to generate test data. Three machine learning models are verified by predicting the SST of CPU and FPGA under different operating conditions. It is found that M-SVR has better prediction ability than DT and ANN. The findings can be used for chip reliability evaluation of other electronic system devices, and provide a new method for predicting the possible steady-state temperature of chips under different service conditions.http://www.sciencedirect.com/science/article/pii/S1738573324004078NuclearElectronic systemThermalChip temperatureFinite element analysisMachine learning |
| spellingShingle | Fanyu Wang Dongwei Wang Qiang Deng Hao Yan Qi Chen Yang Zhao Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning Nuclear Engineering and Technology Nuclear Electronic system Thermal Chip temperature Finite element analysis Machine learning |
| title | Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning |
| title_full | Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning |
| title_fullStr | Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning |
| title_full_unstemmed | Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning |
| title_short | Investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning |
| title_sort | investigation on the thermal characteristics of electronic system and prediction of chip temperature by machine learning |
| topic | Nuclear Electronic system Thermal Chip temperature Finite element analysis Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1738573324004078 |
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