기관회원 [로그인]
소속기관에서 받은 아이디, 비밀번호를 입력해 주세요.
개인회원 [로그인]

비회원 구매시 입력하신 핸드폰번호를 입력해 주세요.
본인 인증 후 구매내역을 확인하실 수 있습니다.

회원가입
서지반출
마이크로파 레플렉토메터리를 이용한 KSIAR 플라즈마 밀도분포 재구성에 관한 연구
[STEP1]서지반출 형식 선택
파일형식
@
서지도구
SNS
기타
[STEP2]서지반출 정보 선택
  • 제목
  • URL
돌아가기
확인
취소
  • 마이크로파 레플렉토메터리를 이용한 KSIAR 플라즈마 밀도분포 재구성에 관한 연구
저자명
노영수,Roh. Young-Su
간행물명
전기학회논문지. The transactions of the Korean Institute of Electrical Engineers. C/ C, 전기물성·응용부문
권/호정보
2005년|54권 8호|pp.365-370 (6 pages)
발행정보
대한전기학회
파일정보
정기간행물|
PDF텍스트
주제분야
기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

Microwave diagnostics have been widely utilized to measure the important parameters of high temperature and high density plasmas. Reflectometry is known as a promising microwave diagnostic which has a number of merits to measure electron density profiles. In the KSTAR device, X-mode FM reflectometry is planned to measure the plasma density profiles. FM reflectometry is required to extract phase information on raw mixer IF signals, thereby obtaining time-of-flight of reflectometry signals. It is known that the data analysis method is crucial to determine the performance of FM reflectometry In fact, there are several analysis programs which have been utilized in various FM systems. Since each program was developed for a specific device, however, it is difficult to directly apply it to a different reactor like the KSTAR device. It is necessary, therefore, to develop a data analysis program for the KSTAR FM reflectometry. In this paper, complex digital demodulation (CDM) and wavelet transformation are examined in terms of the performance of density profile reconstruction. For the comparison of both methods, FM reflectometry signals are generated on the basis of assumed profiles and the interaction of the X-mode wave and the plasma. In order to see how well both methods work under various conditions, three types of profiles are assumed and noise effects are included. As a result, both methods work well under the condition of gentle density gradient and small noise level. As density gradient becomes steeper and noise level gets higher. the reconstruction performance of wavelet is better than that of CDM.