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Title: Current State of DIII-D Plasma Control System

Journal Article · · Fusion Engineering and Design
 [1];  [2];  [2];  [2];  [2];  [2];  [2];  [3];  [3];  [4]
  1. General Atomics, San Diego, CA (United States); General Atomics, Energy & Advanced Concepts, DIII-D
  2. General Atomics, San Diego, CA (United States)
  3. Columbia Univ., New York, NY (United States)
  4. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Plasma Science and Fusion Center (PSFC)

We present that the DIII-D Plasma Control System (PCS) is a comprehensive software and hardware system used in real-time data acquisition and feedback control of numerous actuators on the DIII-D tokamak. It regulates many plasma characteristics including shape, position, divertor function, and core performance. The custom software developed at DIII-D provides an expandable platform from which new control algorithms can be incorporated. PCS has been expanding with the needs of the DIII-D research program, national, and international institutions that have adapted the PCS for use on their devices. The DIII-D PCS group in collaboration with many national and international groups have been instrumental in steadily improving the effectiveness and capability of the system. Lastly, enhancements on two key areas have been made: computer infrastructure upgrades and real time diagnostics control.

Research Organization:
General Atomics, San Diego, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Fusion Energy Sciences (FES) (SC-24)
Grant/Contract Number:
FC02-04ER54698; SC0010685
OSTI ID:
1597684
Journal Information:
Fusion Engineering and Design, Journal Name: Fusion Engineering and Design Journal Issue: C Vol. 150; ISSN 0920-3796
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (3)

TokSearch: A search engine for fusion experimental data journal April 2018
Super H-mode: theoretical prediction and initial observations of a new high performance regime for tokamak operation journal July 2015
A real-time machine learning-based disruption predictor in DIII-D journal July 2019