In tokamaks, operators are able to control plasmas through a variety of “actuators” during shots: from neutral beams that heat and rotate the plasma, to coils which induce plasma current. Experimental proposals entail physicists specifying a desired plasma “state” of interest, which can be described by profiles of density, temperature, pressure, safety factor (q), and rotation. Operators and physicists usually work together to pre-specify a “path” of actuator signals through time that will successfully realize the state, using feedback control for realtime adjustments. However, the process of finding a successful actuator path is difficult and entails a lot of trial-and-error. “Model-predictive control” could make this process more efficient, saving physicists time and ensuring more successful physics experiments during future tokamak campaigns. In model-predictive control, a realtime model predicts the way the plasma state will evolve given various settings on actuators, then chooses settings which yield the plasma state closest to the physicist’s desired end-state. Realtime physics models are not always accurate for regimes of interest. We are therefore developing a machine-learning model which generates a single prediction in under 100 microseconds, using only realtime diagnostics.

Algorithm would choose to increase the “pinj” signal to move the plasma toward the highest temperature profile (shown in green).

Plasma Behavior Monitoring using High Resolution Diagnostics

Plasma Control Group is leading a multi-institutional research project to investigate machine learning for real-time fusion plasma behavior monitoring using high resolution diagnostics. We focus on two tracks of research; (1) detecting and classifying instabilities such as Alfven-Eigen (AE) modes in the core of plasma based on Electron Cyclotron Emission signals (2) developing a general framework for preprocessing such data, e.g., denoising spectrograms, before feeding them to the ML model.
We develop several ML models to classify five AE modes, namely, BAAE, BAE, EAE, RSAE, TAE in a dataset of ~1000 discharges. We particularly pay attention to the ML models such as Reservoir Computing Networks which are easy to train yet effective in processing temporal information in time-series data such as ECE. Figure below shows the performance of a 2-layer RC model which has been trained on down-sampled ECE signals (from 500KHz to 1KHz). Our preliminary experiments show a hit rate of ~90% in detecting AE modes.

Output of a 2-layer RCN model trained for classifying five AE modes using raw ECE as inputs.

A General Framework for Spectrogram Data Processing

With the aim of analyzing shape and location of instabilities in plasma, we work on enhancing ECE spectrograms. To that end, we investigate low-cost image processing filters such as Sobel, Weiner, Gaussian Blur and moving quantile. Figure below presents the result of a three- step denoising pipeline (quantile>Blur>mean) on an ECE spectrogram. In the next steps, we would also consider more advanced deep models such as (Variational) Auto-Encoders which have been proven to be effective in extracting informative features from noisy images.

Enhancing ECE spectrograms using image processing techniques. (top) original spectrogram, (bottom) enhanced spectrogram.