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AC compensation of 3D magnetic diagnostic signals in DIII-D and National Spherical Torus Experiment-Upgrade (NSTX-U) for real-time application.
Munaretto, S; Myers, C E; Gerhardt, S P; Logan, N C; Menard, J E; Strait, E J.
Afiliação
  • Munaretto S; Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540, USA.
  • Myers CE; Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540, USA.
  • Gerhardt SP; Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540, USA.
  • Logan NC; Lawrence Livermore National Laboratory, Livermore, California 94551, USA.
  • Menard JE; Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540, USA.
  • Strait EJ; General Atomics, San Diego, California 92121, USA.
Rev Sci Instrum ; 93(9): 093528, 2022 Sep 01.
Article em En | MEDLINE | ID: mdl-36182482
ABSTRACT
A time domain algorithm has been developed to remove the vacuum pickup generated by both coil current (DC) and induced vessel current (AC) in real time from three dimensional (3D) magnetic diagnostic signals in the National Spherical Torus Experiment-Upgrade (NSTX-U) and DIII-D tokamaks. The possibility of detecting 3D plasma perturbations in real time is essential in modern and future tokamaks to avoid and control MHD instabilities. The presence of vacuum field pickup, due to toroidally asymmetric (3D) coils or to misalignment between sensors and axisymmetric (2D) coils, pollutes the measured plasma 3D field, making the detection of the magnetic field produced by the plasma challenging. Although the DC coupling between coils and sensors can be easily calculated and removed, the AC part is more difficult. An algorithm based on a layered low-pass filter approach for the AC compensation and its application for DIII-D and NSTX-U data is presented, showing that this method reduces the vacuum pickup to the noise level. Comparison of plasma response measurements with and without vacuum compensation shows that accurate mode locking detection and plasma response identification require precise AC and DC compensations.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article