Parameter identification and state estimation for nuclear reactor operation digital twin

被引:21
作者
Gong, Helin [1 ]
Zhu, Tao [2 ]
Chen, Zhang [3 ]
Wan, Yaping [2 ]
Li, Qing [3 ]
机构
[1] Shanghai Jiao Tong Univ, ParisTech Elite Inst Technol, Shanghai 200240, Peoples R China
[2] Univ South China, Sch Comp, Hengyang, Peoples R China
[3] Nucl Power Inst China, Sci & Technol Reactor Syst Design Technol Lab, Chengdu 610041, Peoples R China
基金
中国国家自然科学基金;
关键词
Digital twin; Machine learning; Differential evolution; Nuclear reactor physics; PARTIAL-DIFFERENTIAL-EQUATIONS; REDUCED BASIS APPROACH; MODEL-REDUCTION; ORDER MODELS; OPTIMIZATION; EVOLUTION; VALIDATION; DYNAMICS;
D O I
10.1016/j.anucene.2022.109497
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Reactor Operation Digital Twin (RODT) is now receiving increasing attention and investment in nuclear engineering domain. A prototype of a RODT was first brought out by Gong et al. at Nuclear Power Institute of China. The RODT contains a forward solver for online real-time simulation and an inverse problem solver for parameter identification and state estimation. To further improve the efficiency and accuracy of RODT and promote the practical deployment of RODT (i) we first propose an advanced differential evolution algorithm to upgrade the inverse solver; (ii) then we bring out a systematical uncertainty quantification of RODT considering assimilation noisy observations. The accuracy and validity of the proposed RODT are tested along one cycle operating stage of HPR1000, at various operating temperatures, control rod steps, general power level and the inlet temperature of a practical nuclear reactor core. Numerous numerical results confirm its potential for practical engineering applications for on-line parameter identification and state estimation.
引用
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页数:16
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