Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN
The start-up and power-up processes of the heat pipe cooled reactor are essential parts of the autonomous operations. The rapid power fluctuation in the processes can affect the safety of the heat pipe reactor. The fast and accurate prediction of the peak power is significant for the safe operation...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1075945/full |
| _version_ | 1797956524998393856 |
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| author | Yu Liu Mengqi Huang Zhengyu Du Zhengyu Du Changhong Peng Zhe Wang |
| author_facet | Yu Liu Mengqi Huang Zhengyu Du Zhengyu Du Changhong Peng Zhe Wang |
| author_sort | Yu Liu |
| collection | DOAJ |
| description | The start-up and power-up processes of the heat pipe cooled reactor are essential parts of the autonomous operations. The rapid power fluctuation in the processes can affect the safety of the heat pipe reactor. The fast and accurate prediction of the peak power is significant for the safe operation of the heat pipe cooled reactor. This paper generates the peak power datasets of heat pipe cooled reactor start-up and power-up processes by coupling Monte Carlo sampling, and system analysis program with heat pipe cooled reactor MegaPoweras the research object. A fast prediction model of peak power was developed based on the artificial neural network and evaluated in terms of cost, accuracy, and interpretability. The results show that the artificial neural network model has high prediction accuracy and is suitable for large datasets with complex non-linear relations. However, the training cost is high, and the interpretability is weak. The above characteristics are explained by theoretical analysis, and the ability of ensemble algorithms to improve the accuracy of the artificial neural networks is discussed. |
| first_indexed | 2024-04-10T23:50:14Z |
| format | Article |
| id | doaj.art-72b2c364a0554238b0bfec1eb9b0f6ae |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T23:50:14Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-72b2c364a0554238b0bfec1eb9b0f6ae2023-01-10T18:57:22ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.10759451075945Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANNYu Liu0Mengqi Huang1Zhengyu Du2Zhengyu Du3Changhong Peng4Zhe Wang5Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, ChinaSchool of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, ChinaSchool of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaSchool of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui, ChinaNuclear and Radiation Safety Center of the Ministry of Ecology and Environment, Beijing, ChinaThe start-up and power-up processes of the heat pipe cooled reactor are essential parts of the autonomous operations. The rapid power fluctuation in the processes can affect the safety of the heat pipe reactor. The fast and accurate prediction of the peak power is significant for the safe operation of the heat pipe cooled reactor. This paper generates the peak power datasets of heat pipe cooled reactor start-up and power-up processes by coupling Monte Carlo sampling, and system analysis program with heat pipe cooled reactor MegaPoweras the research object. A fast prediction model of peak power was developed based on the artificial neural network and evaluated in terms of cost, accuracy, and interpretability. The results show that the artificial neural network model has high prediction accuracy and is suitable for large datasets with complex non-linear relations. However, the training cost is high, and the interpretability is weak. The above characteristics are explained by theoretical analysis, and the ability of ensemble algorithms to improve the accuracy of the artificial neural networks is discussed.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1075945/fullstart-uppower-upneural networkheat pipe cooled reactorpeak power |
| spellingShingle | Yu Liu Mengqi Huang Zhengyu Du Zhengyu Du Changhong Peng Zhe Wang Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN Frontiers in Energy Research start-up power-up neural network heat pipe cooled reactor peak power |
| title | Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN |
| title_full | Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN |
| title_fullStr | Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN |
| title_full_unstemmed | Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN |
| title_short | Peak power prediction method of heat pipe cooled reactor start-up and power-up processes based on ANN |
| title_sort | peak power prediction method of heat pipe cooled reactor start up and power up processes based on ann |
| topic | start-up power-up neural network heat pipe cooled reactor peak power |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1075945/full |
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