메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
Kubo Yoshihiro (Graduate School of Advanced Science and Engineering, Waseda University) Yamaji Akifumi (Graduate School of Advanced Science and Engineering, Waseda University)
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology Vol.56 No.3
발행연도
2024.3
수록면
846 - 854 (9page)
DOI
10.1016/j.net.2023.10.014

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
FEMAXI-ATF is being developed for fuel performance modeling of SiC cladded UO2 fuel with focuses on modeling pellet-cladding mechanical interactions (PCMI). The code considers probability distributions of mechanical strengths of monolithic SiC (mSiC) and SiC fiber reinforced SiC matrix composite (SiC/SiC), while it models pseudo-ductility of SiC/SiC and propagation of cladding failures across the wall thickness direction in deterministic manner without explicitly modeling cracks based on finite element method in one-dimensional geometry. Some hypothetical BWR power ramp conditions were used to test sensitivities of different model parameters on the analyzed PCMI behavior. The results showed that propagation of the cladding failure could be modeled by appropriately reducing modulus of elasticities of the failed wall element, so that the mechanical load of the failed element could be re-distributed to other intact elements. The probability threshold for determination of the wall element failure did not have large influence on the predicted power at failure when the threshold was varied between 25 % and 75 %. The current study is still limited with respect to mechanistic modeling of SiC failure as it only models the propagation of the cladding wall element failure across the homogeneous continuum wall without considering generations and propagations of cracks.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

최근 본 자료

전체보기

댓글(0)

0