Bulletin of the American Physical Society
64th Annual Meeting of the APS Division of Plasma Physics
Volume 67, Number 15
Monday–Friday, October 17–21, 2022; Spokane, Washington
Session BP11: Poster Session I: In-Person, Hall A (9:30-11:00am) and Virtual Poster Presentations (11:15am-12:30pm)
MFE: Analytical,computational; Energetic Particles; Disruptions; Power Handling
ICF: Z-Pinch; MagnetoInertial Fusion; Hydrodynamics; Indirect drive
9:30 AM - 12:30 PM
Monday, October 17, 2022
Room: Exhibit Hall A and Online
Abstract: BP11.00004 : Data-Driven Prediction and Control of Alfvén Eigenmodes at DIII-D*
Presenter:
Azarakhsh Jalalvand
(Ghent University)
Authors:
Azarakhsh Jalalvand
(Ghent University)
Ralph Kube
(Princeton Plasma Physics Laboratory)
Mark D Boyer
(Princeton Plasma Physics Laboratory)
Alvin V Garcia
(University of California, Irvine)
Max Austin
(University of Texas at Austin)
Geert Verdoolaege
(Ghent University)
William W Heidbrink
(University of California, Irvine)
Egemen Kolemen
(Princeton University)
Collaboration:
DIII-D
Here, we report on follow-up research to develop multimodal machine learning models to predict and control the AE modes based on the current plasma profiles, diagnostics, and actuators. The model is trained on various input data such as ECE, neutron rate, electron density, plasma pressure, injected neutral beam power and ECH. Our preliminary results show the potenital of the proposed model in predicting the probability of pronounced AE activities 200ms in advance. To control the AE activities in real-time, such a model can be used to tune the actuator values by predicting the evolution of AE modes based on the current plasma conditions and different proposals for actuator values.
The models will be deployed for integration in the DIII-D plasma control system (PCS) to be tested in the 2022-23 campaign.
*Supported by US DOE Grant Nos. DE-SC0021275, DE-FC02-04ER54698, and Ghent University Special Research Award No. BOF19/PDO/134.
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