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
김요찬 (한국원자력연구원) Yung Hsien James Chan (U.S. Nuclear Regulatory Commission) 박진균 (한국원자력연구원) Lawrence Criscione (U.S. Nuclear Regulatory Commission)
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제54권 제3호
발행연도
2022.3
수록면
896 - 908 (13page)

이용수

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

초록· 키워드

오류제보하기
As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), theprimary role of human reliability analysis (HRA) is to provide credible estimations of the human errorprobabilities (HEPs) of safety-critical tasks. In this regard, it is vital to provide credible HEPs based onfirm technical underpinnings including (but not limited to): (1) how to collect HRA data from availablesources of information, and (2) how to inform HRA practitioners with the collected HRA data. Because ofthese necessities, the U.S. Nuclear Regulatory Commission and the Korea Atomic Energy ResearchInstitute independently developed two dedicated HRA data collection systems, SACADA (ScenarioAuthoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction),respectively. These systems provide unique frameworks that can be used to secure HRA data fromfull-scope training simulators of NPPs (i.e., simulator data). In order to investigate the applicability ofthese two systems, two papers have been prepared with distinct purposes. The first paper, entitled“SACADA and HuREX: Part 1. The Use of SACADA and HuREX Systems to Collect Human Reliability Data”,deals with technical issues pertaining to the collection of HRA data. This second paper explains how thetwo systems are able to inform HRA practitioners. To this end, the process of estimating HEPs isdemonstrated based on feed-and-bleed operations using HRA data from the two systems.

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!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0