지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 4,014 - 4,021 (8page)
- DOI
- https://doi.org/10.1016/j.net.2021.06.017
이용수
초록· 키워드
오류제보하기
If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails.
Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation isnecessary to prevent this progression. In this study, the corresponding time was defined as the goldentime. To achieve the objective of accurately predicting the golden time, the prediction was performedusing the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has anarchitecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in whichthe fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affectinference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regressionmodel. By using the prediction result through the proposed DFNN with rule-dropout, it is expected toprevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery,which failed in the LOCA situation
목차
등록된 정보가 없습니다.
참고문헌 (16)
참고문헌 신청
K. H. Yoo / 2016 / Prediction of golden time using SVR for recovering SIS under severe accidents / Ann. Nucl. Energy 94:102~108
윤소훈 / 2020 / Collapse moment estimation for wall-thinned pipe bends and elbows using deep fuzzy neural networks / Nuclear Engineering and Technology 52(11):2678~2685
Y. J. An / 2020 / Critical flow prediction using simplified cascade fuzzy neural networks / Ann. Nucl. Energy 136:107047
H.S. Jo / 2020 / Prediction of NPP containment states using deep fuzzy neural networks during LOCAs / Proceedings of the Korean Nuclear Society Virtual Autumn Meeting
EPRI / 2010 / MAAP4 Applications Guidance
윤소훈 / 2020 / Collapse moment estimation for wall-thinned pipe bends and elbows using deep fuzzy neural networks / Nuclear Engineering and Technology 52(11):2678~2685
Y. J. An / 2020 / Critical flow prediction using simplified cascade fuzzy neural networks / Ann. Nucl. Energy 136:107047
H.S. Jo / 2020 / Prediction of NPP containment states using deep fuzzy neural networks during LOCAs / Proceedings of the Korean Nuclear Society Virtual Autumn Meeting
EPRI / 2010 / MAAP4 Applications Guidance
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