Reliability Evaluation Method of Bolts of Reactor Internals in High-neutron-fluence-rate Region Based on XGBoost

Wang Wenhui; Wan Anping; Deng Chaojun; Gong Zhipeng; Zhang Hongliang; Ye Yanghan; Wang Pengfei; Liu Canxian; Li Yuezhang

doi:10.13832/j.jnpe.2022.05.0154

Volume 43 Issue 5

Oct. 2022

Turn off MathJax

Article Contents

Article Navigation > Nuclear Power Engineering > 2022 > 43(5): 154-162

Wang Wenhui, Wan Anping, Deng Chaojun, Gong Zhipeng, Zhang Hongliang, Ye Yanghan, Wang Pengfei, Liu Canxian, Li Yuezhang. Reliability Evaluation Method of Bolts of Reactor Internals in High-neutron-fluence-rate Region Based on XGBoost[J]. Nuclear Power Engineering, 2022, 43(5): 154-162. doi: 10.13832/j.jnpe.2022.05.0154

Citation:

Wang Wenhui, Wan Anping, Deng Chaojun, Gong Zhipeng, Zhang Hongliang, Ye Yanghan, Wang Pengfei, Liu Canxian, Li Yuezhang. Reliability Evaluation Method of Bolts of Reactor Internals in High-neutron-fluence-rate Region Based on XGBoost[J]. Nuclear Power Engineering, 2022, 43(5): 154-162. doi: 10.13832/j.jnpe.2022.05.0154

Citation:

Wang Wenhui, Wan Anping, Deng Chaojun, Gong Zhipeng, Zhang Hongliang, Ye Yanghan, Wang Pengfei, Liu Canxian, Li Yuezhang. Reliability Evaluation Method of Bolts of Reactor Internals in High-neutron-fluence-rate Region Based on XGBoost[J]. Nuclear Power Engineering, 2022, 43(5): 154-162. doi: 10.13832/j.jnpe.2022.05.0154

PDF( 2683 KB)

Reliability Evaluation Method of Bolts of Reactor Internals in High-neutron-fluence-rate Region Based on XGBoost

doi: 10.13832/j.jnpe.2022.05.0154

1.
Department of Mechanical Engineering, Zhejiang University City College, Hangzhou, 310015, China
2.
College of Mechanical Engineering, Anhui University of Science and Technology, HuaiNan, Anhui, 232001, China
3.
Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China
4.
Zigong Innovation Center of Zhejiang University, Zigong, Sichuan, 643000, China

Received Date: 2021-10-03
Rev Recd Date: 2022-03-22
Publish Date: 2022-10-12

Abstract

Abstract

The bolts of reactor internals are in high temperature, high pressure and high radiation environment for a long time, and the bolts connecting the shroud and the forming plate are subject to irradiation assisted stress corrosion cracking (IASCC). In order to predict the service life of bolts in stress corrosion environment in advance and reduce the spare parts inventory of nuclear power plants, this paper uses the XGBoost(eXtreme Gradient Boosting) model to predict the service life of bolts of reactor internals in high irradiation environment. First, the whole-cycle residual life evolution data in the high-neutron-fluence-rate region of PWR are analyzed and processed, and the correlation model is obtained. Then, XGBoost model based on data driven is proposed to predict the residual life of bolts. This method has strong generalization and high accuracy, and can well evaluate the reliability of bolts in high-neutron-fluence-rate region; Finally, 35000 samples are used as the training set and 15000 samples as the test set, which are compared with the calculated values of the International Atomic Energy Agency (IAEA) empirical formula. The results show that the prediction accuracy of the XGBoost model is as high as 99.93%, which is better than the multiple linear regression method, AdaBoost (using a linear loss function), AdaBoost (using a squared loss function) and AdaBoost (using an exponential loss function) methods.
- XGBoost,
- Life prediction,
- Reactor internals,
- Data driven

FullText(HTML)

References(20)

References

[1]	杨武. 辐照促进应力腐蚀破裂研究的进展[J]. 材料保护,1994, 27(2): 1-4.
[2]	ISHIHARA T. Corrosion failures and related research subjects in light water reactor power plants[J]. Zairyo-to-Kankyo, 1990, 39(11): 621-629.
[3]	赵和安. 我国核电发展及其与紧固件的关系[J]. 兵器装备工程学报,2018, 39(4): 131-137. doi: 10.11809/bqzbgcxb2018.04.028
[4]	段远刚,赵晓刚,喻济兵. 压水堆堆内构件寿命管理中的关键问题[J]. 核动力工程,2003, 24(5): 401-404. doi: 10.3969/j.issn.0258-0926.2003.05.001
[5]	谢航,葛亮,蔡家藩. 核电站堆内构件围板螺栓的超声检测[J]. 无损检测,2020, 42(10): 49-51. doi: 10.11973/wsjc202010010
[6]	段远刚,许斌,唐传宝. 围板连接螺栓的辐照促进应力腐蚀裂纹研究[J]. 核动力工程,2007, 28(2): 62-65. doi: 10.3969/j.issn.0258-0926.2007.02.014
[7]	邓平,孙晨,彭群家,等. 堆芯结构材料辐照促进应力腐蚀开裂研究现状[J]. 中国腐蚀与防护学报,2015, 35(6): 479-487.
[8]	李守彬,孔晨光,范岩成. 核电厂围板螺栓裂纹的失效分析与超声检测[J]. 无损检测,2019, 41(1): 58-60. doi: 10.11973/wsjc201901014
[9]	WAS G S. Role of irradiation in stress corrosion cracking[M]//SICKAFUS K E, KOTOMIN E A, UBERUAGA B P. Radiation Effects in Solids. Dordrecht: Springer, 2007: 421-447.
[10]	刘增瑞,徐杰,辛正高. “华龙一号”核反应堆围板和成形板组件的安装[J]. 装备机械,2018, 2(3): 16-21. doi: 10.3969/j.issn.1662-0555.2018.03.004
[11]	张振声,孙立斌,王海涛. HTR石墨堆内构件应力评价概率论和确定论方法比较[J]. 核动力工程,2011, 32(S1): 57-60.
[12]	PHAM X T T, HO T H. Using boosting algorithms to predict bank failure: an untold story[J]. International Review of Economics & Finance, 2021, 76: 40-54.
[13]	ZHU M L. Construction of quantization strategy based on random forest and XGBoost[C]//Proceedings of 2020 Conference on Artificial Intelligence and Healthcare. Taiyuan: ACM, 2020: 5-9.
[14]	LI C, CHEN Z Y, LIU J B, et al. Power load forecasting based on the combined model of LSTM and XGBoost[C]//Proceedings of 2019 the International Conference on Pattern Recognition and Artificial Intelligence. Wenzhou: ACM, 2019: 46-51.
[15]	CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 785-794.
[16]	CHOI H S, KIM S, OH J E, et al. XGBoost-based instantaneous drowsiness detection framework using multitaper spectral information of electroencephalography[C]//Proceedings of 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Washington: ACM, 2018: 111-121.
[17]	ZHANG D, CHEN H D, ZULFIQAR H, et al. Developing and applying machine learning-based methods in special function protein identification[J]. Computational and Mathematical Methods in Medicine, 2021, 2021: 6664362.
[18]	陈梅. 秦山核电库存控制探讨[J]. 企业技术开发,2015, 34(24): 170,172.
[19]	International Atomic Energy Agency. Assessment and management of ageing of major nuclear power plant components important to safety: PWR vessel internals: 2007 update[R]. Vienna: International Atomic Energy Agency, 2007.
[20]	CHEN W, LEI X X, CHAKRABORTTY R, et al. Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility[J]. Journal of Environmental Management, 2021, 284: 112015. doi: 10.1016/j.jenvman.2021.112015