Development of a novel data-driven NPP simulation method based on physics informed neural network for dynamic PSA application동적 확률론적 안전성 평가를 위한 새로운 데이터 기반의 시뮬레이션 프레임워크 구축에 대한 연구

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Because nuclear power plants (NPPs) are safety-critical systems with large sizes and high complexities, various methods have been developed to identify possible accidents and address potential risks. However, the current PSA has two limitations. The first is a data dependency issue, and the other is a dynamic interaction issue. The dynamic interaction can be classified into dynamic interaction with long-time constants and short-time constants. The dynamic interaction with a long-time constant can be considered in prognostics. However, the dynamic interaction with a short-time constant is hard to consider in the current stage because of the calculation speed of the conventional code analysis model. In addition, quantitative analysis of the interaction (ex., actions from safety systems) that affects the state variable is difficult. Therefore, in this study, we proposed a physics-informed neural network-based data-driven simulation framework to consider the calculation speed and interaction issues. The effectiveness of the methodology is confirmed by comparing the proposed model with the conventional analysis method. The proposed study is expected to contribute not only to the dynamic probabilistic safety assessment but designing a digital twin or creating a new simulator.
Advisors
Lee, Jeong Ikresearcher이정익researcherSeong, Poong Hyunresearcher성풍현researcher
Description
한국과학기술원 :원자력및양자공학과,
Publisher
한국과학기술원
Issue Date
2023
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 원자력및양자공학과, 2023.2,[iii, 83 p. :]

Keywords

Dynamic probabilistic safety assessment▼aPhysics informed neural network▼aDigital twin▼aSimulator; 동적 확률론적 안전성 평가▼a물리기반 인공신경망▼a디지털 트윈▼a시뮬레이터

URI
http://hdl.handle.net/10203/308661
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1030497&flag=dissertation
Appears in Collection
NE-Theses_Ph.D.(박사논문)
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