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• W4384497165 abstract "In constructing a digital twin for a nuclear reactor core, it is important to consider the influence of randomness from various sources. Data assimilation (DA) can combine time distribution observations with dynamic models to approximate the real state of a physical system. Machine learning (ML) and DA share similarities under the Bayesian framework, and using probabilistic ML may provide a way to improve or replace current DA techniques. This paper proposes using a probabilistic ML as Bayesian neural network (BNN) to solve an inverse problem of core monitoring and demonstrates its feasibility through a pressurized water reactor core simulation analysis. Model order reduction technology is also analyzed, and the feasibility and benefit of using it to achieve core monitoring under steady-state conditions is preliminarily verified and discussed. Future work will focus on improving estimation and prediction models under transient operating conditions by unifying DA and ML under the Bayesian framework." @default.
• W4384497165 created "2023-07-18" @default.
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• W4384497165 date "2023-12-01" @default.
• W4384497165 modified "2023-09-28" @default.
• W4384497165 title "Constructing a probability digital twin for reactor core with Bayesian network and reduced-order model" @default.
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• W4384497165 doi "https://doi.org/10.1016/j.anucene.2023.110016" @default.
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