Study on a Data-Enabled Physics-Informed Reactor Physics Operational Digital Twin

被引：0
作者：
Gong H. [1 ]
Chen Z. [1 ]
Li Q. [1 ]
Cheng S. [2 ]
机构：
[1] Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu
[2] Data Science Institute, Department of Computing, Imperial College London, London
来源：
Li, Qing (liqing_npic@163.com) | 1600年 / Atomic Energy Press卷 / 42期
关键词：
Digital twin; Machine learning; Model order reduction; Nuclear reactor physics; Proper Orthogonal Decomposition (POD);
D O I：
10.13832/j.jnpe.2021.S2.0048
中图分类号：
学科分类号：
摘要：
To realize the fast and accurate online calculation and to predict the operation behavior of nuclear reactors, a physics-informed data-enabled reactor physics operational digital twin is proposed, to achieve rapid and accurate calculation of physical fields such as fast and thermal neutron flux and power distribution in the core. The physics-informed property is achieved through a fast calculation model of neutronics based on model order reduction technology and machine learning; the data enabled property is realized through an inverse model based on the fast calculation model. The test of the design and operation data of HPR1000 reactor shows that the digital twin meets the engineering requirements in terms of time and accuracy, and has the potential for online monitoring applications in real engineering. Copyright ©2021 Nuclear Power Engineering. All rights reserved.
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页码：48 / 53
页数：5
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