Intelligent optimization method for lead-bismuth reactor based on Kriging surrogate model

Li Qiong; Liu Zijing; Xiao Hao; XiaoYingjie; Zhao Pengcheng; Wang Chang; Yu Tao

doi:10.11884/HPLPB202234.210560

Volume 34 Issue 5

Apr. 2022

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Article Navigation > High Power Laser and Particle Beams > 2022 > 34(5): 056007

Li Qiong, Liu Zijing, Xiao Hao, et al. Intelligent optimization method for lead-bismuth reactor based on Kriging surrogate model[J]. High Power Laser and Particle Beams, 2022, 34: 056007. doi: 10.11884/HPLPB202234.210560

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Li Qiong, Liu Zijing, Xiao Hao, et al. Intelligent optimization method for lead-bismuth reactor based on Kriging surrogate model[J]. High Power Laser and Particle Beams, 2022, 34: 056007. doi: 10.11884/HPLPB202234.210560

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Intelligent optimization method for lead-bismuth reactor based on Kriging surrogate model

doi: 10.11884/HPLPB202234.210560

Li Qiong^{1, 2
,},
Liu Zijing^{1, 2
,
,},
Xiao Hao¹,
XiaoYingjie^{1, 2},
Zhao Pengcheng^{1, 2},
Wang Chang¹,
Yu Tao^{1, 2}

1.
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
2.
Hunan Engineering and Technology Research Center for Virtual Nuclear Reactor, University of South China, Hengyang 421001, China

Received Date: 2021-12-14
Rev Recd Date: 2022-01-14

Available Online: 2022-02-14

Publish Date: 2022-05-15

Abstract

Abstract

The extensive application requirements of lead-bismuth reactors require researchers to carry out a lot of optimization design work on the basis of existing core schemes. Aiming at the multi-dimensional nonlinear constrained optimization design problem of lead-bismuth reactor with multi-physical, multi-variable and multi-constraint coupling effects, an intelligent optimization method for lead-bismuth reactor was constructed based on Kriging surrogate model, orthogonal Latin hypercube sampling and SEUMRE spatial search technology. Coupled with physical Monte Carlo calculation/thermal ranalysis code, an optimization platform including sampling, pre-and post-processing of coupling program and reactor optimization analysis function was developed. Taking SPALLER-4 and URANUS as prototypes, the scheme optimization and parameter optimization verification of minimum fuel load were carried out respectively. The verification results show that the core intelligent optimization method is feasible and effective for the optimization of lead-bismuth reactor design scheme and core parameters. Compared with the traditional Monte Carlo calculation optimization, the calculation cost is greatly reduced under the premise of ensuring the prediction accuracy. Compared with the URANUS initial model, the fuel loading, the total mass of the core, the volume of the active zone and the total volume of the core are optimized by 10.8%, 11.5%, 18.1% and 17.1% respectively, which provides a reference for the intelligent optimization method based on the surrogate model applied to the optimization design of lead-bismuth reactor.
- lead-bismuth reactor,
- intelligent optimization,
- Kriging surrogate model,
- SEUMRE spatial search,
- orthogonal Latin hypercube sampling

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References(29)

References

[1]	王建强, 戴志敏, 徐洪杰. 核能综合利用研究现状与展望[J]. 中国科学院院刊, 2019, 34(4):460-468. (Wang Jianqiang, Dai Zhimin, Xu Hongjie. Research status and prospect of comprehensive utilization of nuclear energy[J]. Bulletin of the Chinese Academy of Sciences, 2019, 34(4): 460-468
[2]	吴宜灿. 铅基反应堆研究进展与应用前景[J]. 现代物理知识, 2018, 30(4):35-39. (Wu Yican. Research progress and application prospects of lead-based reactors[J]. Modern Physics, 2018, 30(4): 35-39
[3]	Zameer A, Mirza S M, Mirza N M. Core loading pattern optimization of a typical two-loop 300 MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA(SA) schemes[J]. Annals of Nuclear Energy, 2014, 65: 122-131. doi: 10.1016/j.anucene.2013.10.024
[4]	de Moura Meneses A A, Machado M D, Schirru R. Particle Swarm Optimization applied to the nuclear reload problem of a Pressurized Water Reactor[J]. Progress in Nuclear Energy, 2009, 51(2): 319-326. doi: 10.1016/j.pnucene.2008.07.002
[5]	Khoshahval F, Minuchehr H, Zolfaghari A. Performance evaluation of PSO and GA in PWR core loading pattern optimization[J]. Nuclear Engineering and Design, 2011, 241(3): 799-808. doi: 10.1016/j.nucengdes.2010.12.023
[6]	王成. 新型优化算法开发及其在核动力装置优化中的应用[D]. 哈尔滨: 哈尔滨工程大学, 2018 Wang Cheng. The development of new optimization algorithms and applications in optimal design for nuclear power plant[D]. Harbin: Harbin Engineering University, 2018
[7]	张扬. 多参数非线性系统全局敏感性分析与动态代理模型研究[D]. 长沙: 湖南大学, 2014 Zhang Yang. The study on global sensitivity analysis and dynamic metamodel of multiple-parameters nonlinear system[D]. Changsha: Hunan University, 2014
[8]	Kempf S, Forget B, Hu Linwen. Kriging-based algorithm for nuclear reactor neutronic design optimization[J]. Nuclear Engineering and Design, 2012, 247: 248-253. doi: 10.1016/j.nucengdes.2012.03.001
[9]	Zeng Kaiyue, Stauff N E, Hou J, et al. Development of multi-objective core optimization framework and application to sodium-cooled fast test reactors[J]. Progress in Nuclear Energy, 2020, 120: 103184. doi: 10.1016/j.pnucene.2019.103184
[10]	Kim K Y, Lee S M. Shape optimization of inlet plenum in a PBMR-type gas-cooled nuclear reactor[J]. Journal of Nuclear Science and Technology, 2009, 46(7): 649-652. doi: 10.1080/18811248.2007.9711571
[11]	李淞, 杨红义, 周志伟, 等. 基于克里金方法的快堆燃料组件设计[J]. 原子能科学技术, 2018, 52(7):1288-1293. (Li Song, Yang Hongyi, Zhou Zhiwei, et al. Design of fast reactor fuel assembly based on Kriging method[J]. Atomic Energy Science and Technology, 2018, 52(7): 1288-1293 doi: 10.7538/yzk.2017.youxian.0650
[12]	Pebesma E J, Heuvelink G B M. Latin hypercube sampling of Gaussian random fields[J]. Technometrics, 1999, 41(4): 303-312. doi: 10.1080/00401706.1999.10485930
[13]	Jin R, Chen W, Simpson T W. Comparative studies of metamodelling techniques under multiple modelling criteria[J]. Structural and Multidisciplinary Optimization, 2001, 23(1): 1-13. doi: 10.1007/s00158-001-0160-4
[14]	毛凤山, 陈昌富, 朱世民. 代理模型方法及其在岩土工程中的应用综述[J]. 地基处理, 2020, 2(4):295-306. (Mao Fengshan, Chen Changfu, Zhu Shimin. Surrogate model method and its application in geotechnical engineering[J]. Journal of Ground Improvement, 2020, 2(4): 295-306
[15]	Younis A, Dong Zuomin. Metamodelling and search using space exploration and unimodal region elimination for design optimization[J]. Engineering Optimization, 2010, 42(6): 517-533. doi: 10.1080/03052150903325540
[16]	Wang Kan, Li Zeguang, She Ding, et al. RMC – A Monte Carlo code for reactor core analysis[J]. Annals of Nuclear Energy, 2015, 82: 121-129. doi: 10.1016/j.anucene.2014.08.048
[17]	Zhao Pengcheng, Liu Zijing, Yu Tao, et al. Code development on steady-state thermal-hydraulic for small modular natural circulation lead-based fast reactor[J]. Nuclear Engineering and Technology, 2020, 52(12): 2789-2802. doi: 10.1016/j.net.2020.05.023
[18]	赵鹏程. 小型自然循环铅冷快堆SNCLFR-100一回路主冷却系统热工安全分析[D]. 合肥: 中国科学技术大学, 2017 Zhao Pengcheng. Thermal-hydraulic safety analysis of primary cooling system for small modular natural circulation LFR SNCLFR-100[D]. Hefei: University of Science and Technology of China, 2017
[19]	刘紫静, 赵鹏程, 张斌, 等. 超长寿命小型自然循环铅铋快堆堆芯概念设计研究[J]. 原子能科学技术, 2020, 54(7):1254-1265. (Liu Zijing, Zhao Pengcheng, Zhang Bin, et al. Research on core concept design of ultra-long life small natural circulation lead-based fast reactor[J]. Atomic Energy Science and Technology, 2020, 54(7): 1254-1265 doi: 10.7538/yzk.2019.youxian.0720
[20]	刘紫静, 赵鹏程, 任广益, 等. 长寿命小型自然循环铅基快堆燃料选型[J]. 原子能科学技术, 2020, 54(5):944-953. (Liu Zijing, Zhao Pengcheng, Ren Guangyi, et al. Fuel selection of long life small natural circulation lead-based fast reactor[J]. Atomic Energy Science and Technology, 2020, 54(5): 944-953 doi: 10.7538/yzk.2019.youxian.0402
[21]	Jooeun Lee. Conceptual neutronic design of inverted core for lead-bismuth cooled small modular reactor[D]. Seoul: Graduate School of Seoul National University, 2017.
[22]	Kwak J, Kim H R. Development of innovative reactor-integrated coolant system design concept for a small modular lead fast reactor[J]. International Journal of Energy Research, 2018, 42(13): 4197-4205. doi: 10.1002/er.4177
[23]	Shin Y H, Choi S, Cho J, et al. Advanced passive design of small modular reactor cooled by heavy liquid metal natural circulation[J]. Progress in Nuclear Energy, 2015, 83: 433-442. doi: 10.1016/j.pnucene.2015.01.002
[24]	Shin Y H, Choi S, Cho J, et al. ICONE23-2135 design status of small modular reactor cooled by lead-bismuth eutectic natural circulation: Uranus[C]//Proceedings of ICONE-23 23rd International Conference on Nuclear Engineering. Chiba, Japan: 2015.
[25]	Driscoll N J, Hejzlar P. Reactor physics challenges in Gen-IV reactor design[J]. Nuclear Engineering and Technology, 2005, 37(1): 1-10.
[26]	Zhang Yan, Wang Chenglong, Lan Zhike, et al. Review of thermal-hydraulic issues and studies of lead-based fast reactors[J]. Renewable and Sustainable Energy Reviews, 2020, 120: 109625. doi: 10.1016/j.rser.2019.109625
[27]	Grasso G, Petrovich C, Mattioli D, et al. The core design of ALFRED, a demonstrator for the European lead-cooled reactors[J]. Nuclear Engineering and Design, 2014, 278: 287-301. doi: 10.1016/j.nucengdes.2014.07.032
[28]	Wallenius J, Suvdantsetseg E, Fokau A. ELECTRA: European lead-cooled training reactor[J]. Nuclear Technology, 2012, 177(3): 303-313. doi: 10.13182/NT12-A13477
[29]	杨红义, 过明亮. 中国实验快堆的设计创新与实现[J]. 原子能科学技术, 2020, 54(S1):199-205. (Yang Hongyi, Guo Mingliang. Design innovation and fulfillment of China experimental fast reactor[J]. Atomic Energy Science and Technology, 2020, 54(S1): 199-205