11 papers
Real-time feedback control of ELM frequency using divertor gas puffing and its effects on tungsten-induced radiation and plasma performance in KSTAR
Minseok Kim, Young-Ho Lee, SangKyeun Kim +10
The edge-localized mode (ELM) frequency () was successfully controlled in real time on KSTAR using a proportional-integral (PI) feedback controller, employing a $…
Characterization of ELM Pacing via Vertical Jogs on DIII-D
Kei Yasoda, Dario Panici, Andrew Oak Nelson +3
Edge localized mode (ELM) pacing via vertical plasma oscillations or jogging has been successfully demonstrated on DIII-D. Rapid vertical movement of the plasma toward the X-point…
FPGA-Accelerated Real-Time Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference
Abhilasha Dave, Semin Joung, SangKyeun Kim +11
In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion rea…
Comparison of plasma response models for RMP effects on the divertor and scrape-off layer in KSTAR
H. Frerichs, J. Van Blarcum, T. Cote +3
Resonant magnetic perturbations (RMPs) are beneficial for control of edge localized modes (ELMs) in tokamaks. Nevertheless, a side effect is the appearance of a helical striations…
FPGA-Accelerated Real-Time Beam Emission Spectroscopy Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference
Abhilasha Dave, James Russell, Mudit Mishra +9
Achieving reliable real-time control of tokamak plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate…
Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments
Andrew Rothstein, Hiro Joseph Farre-Kaga, Jalal Butt +7
We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and control…