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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 2,534 - 2,546 (13page)
- DOI
- https://doi.org/10.1016/j.net.2021.02.028
이용수
초록· 키워드
오류제보하기
Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear powerplant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accidentdiagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, badcommunication, incomplete information, as well as complicated accident scenario make it hard todetermine 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, thispaper aims to develop a method for rapid source term estimation to support nuclear emergency decisionmaking in pressurized water reactor NPP. The method aims to make our knowledge on NPP providebetter support nuclear emergency.
Firstly, this paper studies how to build a Bayesian network model for the NPP based on professionalknowledge 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 thatsome physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensorsassociated directly with their occurrence, a weighted assignment approach based on expert assessmentis proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian networkmodel, the real-time status of pivotal events and initiating events can be determined based on thejunction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possiblerelease categories, the proposed method is capable to find the most likely release category for thecandidate accidents scenarios, namely the source term. The probabilities of possible accident sequencesand the source term are calculated. Finally, the prototype software is checked against several sets ofaccident scenario data which are generated by the simulator of AP1000-NPP, including large loss ofcoolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. Theresults show that the proposed method for rapid source term estimation under nuclear emergencydecision making is promising
목차
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참고문헌 (5)
참고문헌 신청
Guohua Wu / 2018 / Uncertainty analysis of containment dose rate for core damage assessment in nuclear power plants / Nuclear Engineering and Technology 50(5):673~682
Shiotsu H / 2018 / Influence of chemical speciation in reactor cooling system on pH of suppression pool during BWR severe accident / J. Nucl. Sci. Technol 55(4):363~373
Min-jun Peng / 2018 / An intelligent hybrid methodology of on-line system-level fault diagnosis for nuclear power plant / Nuclear Engineering and Technology 50(3):396~410
H. Vedam / 1999 / PCA-SDG based process monitoring and fault diagnosis / Contr. Eng. Pract 7(7):903~917
C. Huang / 1996 / Inference in belief networks : a procedural guide / Int. J. Approx. Reason 15(3):225~263
Shiotsu H / 2018 / Influence of chemical speciation in reactor cooling system on pH of suppression pool during BWR severe accident / J. Nucl. Sci. Technol 55(4):363~373
Min-jun Peng / 2018 / An intelligent hybrid methodology of on-line system-level fault diagnosis for nuclear power plant / Nuclear Engineering and Technology 50(3):396~410
H. Vedam / 1999 / PCA-SDG based process monitoring and fault diagnosis / Contr. Eng. Pract 7(7):903~917
C. Huang / 1996 / Inference in belief networks : a procedural guide / Int. J. Approx. Reason 15(3):225~263
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