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Development of a scintillator-based optical soft x-ray (OSXR) diagnostic system for KSTAR tokamak.
Lee, M W; Kim, Junghee; Kim, M H; Lim, Soobin; Kim, Jayhyun; Sung, C.
Afiliación
  • Lee MW; Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
  • Kim J; Korea Institute of Fusion Energy, Daejeon 34133, Republic of Korea.
  • Kim MH; Korea Institute of Fusion Energy, Daejeon 34133, Republic of Korea.
  • Lim S; Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Republic of Korea.
  • Kim J; Korea Institute of Fusion Energy, Daejeon 34133, Republic of Korea.
  • Sung C; Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
Rev Sci Instrum ; 94(5)2023 May 01.
Article en En | MEDLINE | ID: mdl-37204283
ABSTRACT
We have developed a new scintillator-based optical soft x-ray (OSXR) diagnostic system for KSTAR. By utilizing fiber optic faceplates, mm-size lens arrays, and fiber bundles, we have successfully constructed a novel optical system for scintillator-based soft x-ray detection to overcome the limited vacuum-port conditions in KSTAR. P47 (Y2SiO5), which has a fast rise (∼7 ns) and decay (∼100 ns) time sufficient for detecting plasma instabilities observed in the kHz-MHz spectral range, was selected as the scintillator material for the KSTAR OSXR system. Scintillation toward each detection channel is collected by the lens arrays coupled to optical fiber cores, which are connected to the photodetector system. Initial results obtained during the 2022 KSTAR experimental campaign support the validity of the OSXR data through the consistency of OSXR measurement results with other diagnostics. We also observe that the OSXR system can capture magnetohydrodynamic activities, such as sawtooth oscillations, and provide valuable information for disruption mitigation studies using shattered pellet injection.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Rev Sci Instrum Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Rev Sci Instrum Año: 2023 Tipo del documento: Article
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