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
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제49권 제3호
발행연도
2017.1
수록면
549 - 555 (7page)

이용수

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

초록· 키워드

오류제보하기
A lead slowing-down spectrometer (LSDS) systemis under development to analyze isotopicfissile content that is applicable to spent fuel and recycled material. The source neutronmechanism for efficient and effective generation was also determined. The source neutroninteracts with a lead medium and produces continuous neutron energy, and this energygenerates dominant fission at each fissile, below the unresolved resonance region. Fromthe relationship between the induced fissile fission and the fast fission neutron detection, amathematical assay model for an isotopic fissile material was set up. The assay model canbe expanded for all fissile materials. The correction factor for self-shielding was defined inthe fuel assay area. The corrected fission signature provides well-defined fission propertieswith an increase in the fissile content. The assay procedure was also established. The assayenergy range is very important to take into account the prominent fission structure of eachfissile material. Fission detection occurred according to the change of the Pu239 weightpercent (wt%), but the content of U235 and Pu241 was fixed at 1 wt%. The assay result wasobtained with 2~3% uncertainty for Pu239, depending on the amount of Pu239 in the fuel. The results show that LSDS is a very powerful technique to assay the isotopic fissile contentin spent fuel and recycled materials for the reuse of fissile materials. Additionally, aLSDS is applicable during the optimum design of spent fuel storage facilities and theirmanagement. The isotopic fissile content assay will increase the transparency and credibilityof spent fuel storage.

목차

등록된 정보가 없습니다.

참고문헌 (9)

참고문헌 신청

함께 읽어보면 좋을 논문

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

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0