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
Wei Tianhao (State Key Laboratory of Nuclear Physics and Technology, Peking University) Lu Yuanrong (State Key Laboratory of Nuclear Physics and Technology, Peking University) Wang Zhi (State Key Laboratory of Nuclear Physics and Technology, Peking University) Han Meiyun (State Key Laboratory of Nuclear Physics and Technology, Peking University) Xia Ying (State Key Laboratory of Nuclear Physics and Technology, Peking University)
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology Vol.56 No.9
발행연도
2024.9
수록면
3,933 - 3,941 (9page)
DOI
10.1016/j.net.2024.04.040

이용수

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

초록· 키워드

오류제보하기
The paper concerns a room-temperature cross-bar H-mode (CH) drift tube linac (DTL) with KONUS (Kombinierte Null Grad Struktur) [1,2] beam dynamics. To make the acceleration in DTL cell more efficient, we studied the correlation between transit time factor (TTF) and structural coefficients, first. Furthermore, we developed a new code with Python to demonstrate the longitudinal dynamics more clearly. The code computationally generates clusters, bunch centers, and emittance growth in a single figure. Thus, the stabilization region and cluster evolution at various negative phases can be studied. Based on the above studies, we designed a 162.5 MHz CHDTL to accelerate 10 mA D+ from 2.11 MeV to 3.25 MeV in continuous-wave (CW) mode. The proposed CH-DTL is a part of the Middle Energy Neutron Source (MENS). The dynamics and RF design were iterated to make the gap voltage error lower than 1 %. The initial beam is assumed to come from a Radio Frequency Quadrupole accelerator (RFQ). The geometries of the CH-DTL are optimized by using CST. Multiparticle tracking from LEBT to RFQ is performed with TraceWin and the transmission efficiency in the CH-DTL is 100 %.

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