Abstract:
A Machine-learning (ML) based detection scheme that automatically detects
Alfvén eigenmodes (AE) in a labelled DIII-D database is presented here.
Controlling AEs is important for the success of planned burning plasma
devices such as ITER, since resonant fast ions can drive AEs unstable and
degrade the performance of the plasma or damage the first walls of the
machine vessel. Artificial Intelligence (AI) could be useful for real-time
detection and control of AEs in steady-state plasma scenarios by
implementing ML-based models into control algorithms that drive actuators
for mitigation of AE impacts. Thus, the objective is to compare differences
in performance between using two different recurrent neural network systems
(Reservoir Computing Network and Long Short Term Memory Network) and two
different representations of the CO2 phase data (simple and crosspower
spectrograms). All CO2 interferometer chords are used to train both models,
but only one is processed during each training step. The results from the
model and data comparison show higher performance for the RCN model (True
Positive Rate = 90% and False Positive Rate = 15%), and that using simple
magnitude spectrograms is sufficient to detect AEs. Also, the vertical CO2
interferometer chord closest to the magnetic axis is slightly better for
Machine Learning-based detection of AEs.