Abstract Details
| status: | file name: | submitted: | by: |
|---|---|---|---|
| approved | sabbagh_sherwood_2024_abstract_v1.pdf | 2024-03-29 23:30:28 | Steven Sabbagh |
Abstracts
Author: Steven A. Sabbagh
Requested Type: Consider for Invited
Submitted: 2024-03-29 23:27:53
Co-authors: S.A. Sabbagh, G. Bustos-Ramirez, J.D. Riquezes, M. Tobin, V. Zamkovska, F.C. Sheehan, G. Tillinghast, J.G. Bak, K. Erickson, C. Ham, J. Harrison, J. Kim, A. Kirk, W.H. Ko, L. Kogan, J.H. Lee, J.W. Lee, K.D. Lee, Y.S. Park, D. Ryan, A. Thornton, J. Yoo, S.
Contact Info:
Columbia University / PPPL
66 Briarwood Dr. East
Warren, NJ 07059
USA
Abstract Text:
Disruption prediction and avoidance in tokamaks is critical to maintain steady plasma operation and avoid device damage. Physics-based disruption event characterization and forecasting (DECAF) research determines the relation of events leading to disruption, and aims to provide event onset forecasts with high accuracy and early warning for disruption avoidance [1]. The first real-time application of DECAF was made on the KSTAR superconducting tokamak. Dedicated plasma experiments focusing on disruptions caused by locking MHD instabilities produced over 50 plasma shots with nearly equal disrupted / non-disrupted cases that were forecast with 100% accuracy. An MHD mode locking forecaster using a torque balance model of the rotating mode was implemented and utilized in real-time to produce these results and cue controlled plasma shutdown, trigger disruption mitigation using massive gas injection, and actuate electron cyclotron current drive and n = 1 rotating 3D fields for future disruption avoidance. Warnings were issued well before the expected plasma disruption time and early warning guidance timing given by ITER disruption mitigation needs. Hardware and software for real-time diagnostic acquisition and DECAF analysis include magnetics, electron temperature, Te, profiles from electron cyclotron emission (ECE), 2D Te fluctuation data from ECE imaging, and toroidal velocity profiles. The Te profile measurements are used to reconstruct a ‘crash profile’ to identify sawteeth, ELMs, and other MHD as NTM triggers and direct disruption precursors. Different disruption event chains are observed based on the plasma state at the time of the trigger event. Analysis shows for datasets spanning entire run campaigns very high true positive rates, in some cases over 99%. A multi-device study (KSTAR, MAST-U, NSTX) conducted for plasma vertical instability produced high prediction accuracy levels of 98.6% - 100%.
[1] S.A. Sabbagh, et al., Phys. Plasmas 30, 032506 (2023)
Comments:
Please consider as an invited talk. Theory will be included in the talk. Additionally, Sherwood has in the past has invited experimental talks to motivate energetic discussions / collaboration.