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Volume 42 Issue S2
Dec.  2021
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Gong Helin, Chen Zhang, Li Qing, Cheng Sibo. Study on a Data-Enabled Physics-Informed Reactor Physics Operational Digital Twin[J]. Nuclear Power Engineering, 2021, 42(S2): 48-53. doi: 10.13832/j.jnpe.2021.S2.0048
Citation: Gong Helin, Chen Zhang, Li Qing, Cheng Sibo. Study on a Data-Enabled Physics-Informed Reactor Physics Operational Digital Twin[J]. Nuclear Power Engineering, 2021, 42(S2): 48-53. doi: 10.13832/j.jnpe.2021.S2.0048

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

doi: 10.13832/j.jnpe.2021.S2.0048
  • Received Date: 2021-07-19
  • Accepted Date: 2021-12-06
  • Rev Recd Date: 2021-09-26
  • Publish Date: 2021-12-29
  • 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.

     

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