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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.2
- 수록면
- 532 - 548 (17page)
- DOI
- https://doi.org/10.1016/j.net.2020.08.008
이용수
초록· 키워드
오류제보하기
Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of asevere accident (SA). Though a lot of analysis and research is carried out on SA, right from the developmentof the nuclear industry, not all the possible circumstances are taken into consideration. Therefore,to improve the efficacy of the safety of nuclear power plants, additional analytical studies areneeded that can directly monitor severe accident phenomena. This paper presents an interacting multiplemodel (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vesselphenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperaturedistribution during severe accidents in a nuclear power plant. The estimation of wall temperatureis treated as a state estimation problem where the time-varying wall temperature is estimatedusing IMM employing three multiple models for temperature evolution. From the estimated RV out-walltemperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown,corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with fivedifferent types of SA scenarios and the results show that the proposed method has estimated the outerwall temperature with good accuracy.
목차
등록된 정보가 없습니다.
참고문헌 (30)
참고문헌 신청
A. Suthar / 2019 / Critical accident scenario analysis of pressurized water reactor / ICTEA: International Conference on Thermal Engineering
X. Gaus-Liu / 2019 / Analysis of Severe Accidents, Annual Report 2017-2018 :29~36
J. J. Herbst / 1986 / Nuclear Plant Safety Evaluation System, U.S. Patent 4,632,802 / Combustion Engineering Inc
김승근 / 2015 / PREDICTION OF SEVERE ACCIDENT OCCURRENCE TIME USING SUPPORT VECTOR MACHINES / Nuclear Engineering and Technology 47(1):74~84
M. G. Na / 2004 / Prediction of major transient scenarios for severe accidents of nuclear power plants / IEEE Trans. Nucl. Sci 51:313~321
X. Gaus-Liu / 2019 / Analysis of Severe Accidents, Annual Report 2017-2018 :29~36
J. J. Herbst / 1986 / Nuclear Plant Safety Evaluation System, U.S. Patent 4,632,802 / Combustion Engineering Inc
김승근 / 2015 / PREDICTION OF SEVERE ACCIDENT OCCURRENCE TIME USING SUPPORT VECTOR MACHINES / Nuclear Engineering and Technology 47(1):74~84
M. G. Na / 2004 / Prediction of major transient scenarios for severe accidents of nuclear power plants / IEEE Trans. Nucl. Sci 51:313~321
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