7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Prediction of radiative collapse by analyzing videos of plasma discharge withConvolutional Neural Network in LHD
Yuya Suzuki1, Mamoru Shoji1,2, Naoki Kenmochi1,2, and Masayuki Yokoyama1,2
1
SOKENDAI, 2National Institutes of Natural Sciences, National Institute for Fusion Sciencee-mail (speaker): suzuki.yuya@nifs.ac.jp

The termination of plasma discharges has been a criticalissue for nuclear fusion research. In Tokamaks, thedisruption causes severe damage to the vacuum vessel andthe divertor. In helical devices, radiative collapse prevents
sustaining high-density plasmas. In the previous researchon the Large Helical Device (LHD), effective parameters
to predict and avoid radiative collapse were identified
using accumulated experimental data, sparse modeling,
and machine learning methods. A plasma discharge
experiment for avoiding radiative collapse succeededusing these parameters by controlling gas puffing andplasma heating [1]. This experiment shows theeffectiveness of a data-driven approach in radiativecollapse control.
The images of the radiative collapse are included in thevideos of plasma discharge. The preliminary analysis ofthe videos suggested that they had some features to predict
radiative collapse. A quantitative investigation of theradiative collapse by analyzing the videos has not yet been
conducted in LHD. In this study, the analysis of the videos
with a Convolutional Neural Network (CNN) is applied
for the prediction of radiative collapse in LHD.
For monitoring the plasma discharges, about 16
cameras have been installed at the viewports on thevacuum vessel in LHD. The videos of about 180 thousand
plasma discharges have been accumulated so far. Plasma
discharges interrupted with the radiative collapse wereextracted from the videos for analyzing the collapse using
machine learning techniques. According to the reference[2], video frames were labeled by density exponent 𝑥 =̇ /𝑃𝑟𝑎𝑑 )/(𝑛̅̇𝑒 /𝑛̅𝑒 ) for training a CNN model to(𝑃𝑟𝑎𝑑discriminate between the stable state and the collapsed
state in plasma discharge. Here, dot, 𝑃𝑟𝑎𝑑 , and 𝑛̅𝑒 mean
6-O

9-O

1.5-L

10-O

References[1] T. Yokoyama, et al., Plasma Fusion Res. 17,2402042, (2022)[2] B.J. Peterson, et al., Plasma Fusion Res. 1, 045,
(2006).
[3] Mingxing Tan and Quoc V.Le, arXiv:1905.11946(2019)[4] Selvaraju R.R., et al., arXiv:1610.02391 (2016)(b) collapsed(a) stable
6-T

the time derivatives, the radiation power, and the line
averaged electron density, respectively. When 𝑥 exceeds
3, thermal instability is driven, and radiative collapse
causes. The discriminating model was generated by an
EfficientNet [3] which is a kind of CNN model using
training datasets labeled by the above exponent. To
visualize focus areas to discriminate stable or collapsed
states, a Gradient-weighted Class Activation Mapping(Grad-CAM) [4] was used.
As a result, it proved that this model could discriminate
between stable or collapsed states in plasma discharges
with a probability of more than 95%. Figure 1 shows the
combined images taken with all cameras for (a) a stableand (b) a collapsed state which were discriminated by thismodel. The heatmap, which shows the areas contributing
to the discriminations (colored areas in the figures), wasgenerated by the Grad-CAM. The plasma images
observed from 3-O left and the 10-O right ports are
identified as possible candidates for the region of interest
to determine the plasma state. At present, a model todiscriminate the transition state from a stable to acollapsed state is in preparation, which is for predicting
radiative collapse. The prediction performance using thismodel will be presented.

4.5-U

6-T

10.5-U

9-O

4.5-U
2.5-L

1.5-L

2.5-L
3-O left

6-O

3-O left10-O

10.5-U

10-O right
4-O
4-O
2-O
10.O-right2-O
Figure 1. The combined images which were taken with all cameras installed in LHD. Labels like 6-T, 6-O, 9-O, etc.
represent viewports where the cameras are installed. Figure 1 (a) and (b) show the combined images discriminated as astable state and a collapsed state of the plasma discharge, respectively. The heatmap shows areas that contributed todiscriminating between the stable and the collapsed states.