• Nuclear Engineering and Technology
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• 제19권1호
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• pp.10-21
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• 1987

극한적인 열제거 기능 상실사고인 급수관 파열사고에 대한 불확실성 해석을 반응표면방법과 Monte Carlo모사를 이용해서 원자력 1호기에 대하여 수행하였다. 여러번의 RELAP4/MOD6를 이용한 급수관 파열사고 해석을 통해 불확실성 해석의 Data Base를 마련하였으며, 비교 목적으로 평가모형 계산도 수행하였다. 급수관 파열사고 이후의 원자로 냉각재계통 최대 압력에 미치는 영향을 조사비교하기 위해 2증류의 입력 Set에 대한 반응표면방법이 활용되었다. 첫 Set는 6개의 주요 발전소 운전변수로 구성되며, 둘째 Set는 5개 주요 모형변수로 구성된다 결과의 비교 분석을 통해 모형변수의 불확실성 이 최대 압력에 미치는 영 향이 운전변수 불확실성의 영향보다 매우 큰 것이 밝혀졌고, 최대 압력 증가의 약 9%에 해 당되는 여유도 개선도 확인되었다. 또한, 평가모델에서 인정되고 있는 초기 냉각재 노심입구 온도에 대한 가정은 잘못된 것으로 밝혀졌다.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.