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

Characterization of transition to detachmentof magnetic confinement plasmas via data-driven approach
Y.Isobe1, H.Yamada1, T.Yokoyama2, M.Kobayashi1,4,
Y.Narushima3,4, Y.Takemura3, C.Suzuki3,4, R.Sakamoto3
1
Graduate School of Frontier Sciences, The University of Tokyo,
2
National Institutes for Quantum Science and Technology, 3National Institute for Fusion Science/NINS, 4School of Physical Sciences, Graduate University for Advance Studies
e-mail (speaker): isobe.yugo22@ae.k.u-tokyo.ac.jp
This study aims to clarify the occurrence conditions ofdetached plasma in the Large Helical Device (LHD). andperforms state classification and feature parameters
extraction by using machine learning. Transition between
detachment and attachment is defined as a problem ofbinary classification.
Reduction of excessive heat load to the divertor is a
critical issue in the development of magnetic confinement
fusion reactors. For this reason, so-called detached plasma
operation is inevitable where the edge plasma does notcontact directly the divertor plate. LHD has succeeded inrealizing a stable detached plasma by applying resonantmagnetic perturbation (RMP) field [1]. The m/n =1/1
magnetic island generated at the plasma edge regulates
radiation which secures detached plasma with preventing
radiation collapse. Identification of what plasma
parameters contribute to the occurrence of detachedplasma is expected to lead to the establishment of stabledetached operation control and understanding of theunderlying physics.
In this study, Support Vector Machine (SVM) andExhaustive Search (ES) were used as a data-driven
approach. A linear support vector machine (SVM) wasused as a binary classifier, and data from experiments
were used for training and evaluation. Since the phases ofattachment/ detachment can be identified clearly by theion saturation current onto the divertor plate Isat as well as
electron temperature at the plasma edge Te,edge, the phaseis labelled by the level of Isat. Exhaustive search (ES) wasused to extract feature parameters to describe thecondition of detached plasma from 14 parameters such asline averaged electron density 𝑛"! , magnetic field strength
B, radiation power fraction 𝑃"#$ ⁄𝑃%&'() , beta b, impurity
line emissions, m/n = 1/1 resonant perturbed magnetic
flux ΔΦ*++ , and the RMP coil current, etc. excluding two
decisive parameters of Isat and Te,edge. ES is a sparsemodeling technique in which all possible combinations ofparameters are evaluated and compared each other [2].
The data set has been extended from the previous one andthe analysis has been revisited. In particular, the CIII
signal, which was thought to play a major role indetachment, was not included in the parameters in theprevious analysis.

As a result of the ES-SVM analysis, the following 3
parameters were extracted: 𝑛"! , 𝐵 , and ΔΦ*++ . As
shown in Fig.1, attachment and detachment can beclassified according to the combination of ΔΦ*++ , which
is an indicator of the magnetic island width actually
generated, and other parameters. On the other hand, theRMP coil current, which was considered to be importantfor detached plasma operation, was not chosen. This isbecause the width of the magnetic island generated by theresponse of the plasma is important for detachment, notby the externally applied RMP alone.
Since the present ES-SVM analysis does not take careof temporal changes, the result indicates correlation but
not causality. Therefore, the pre- and post-relationships ofeach parameter in time are also discussed towards
causality of the detachment transition with the anomaly
detection by the singular value decomposition ofwaveforms. Here, it is found that CIII, CIV, and𝑃"#$ /𝑃%&'() change before the detachment transition,
while ΔΦ*++ occurs at a timing close to the detachmenttransition.
Effect of Ne puff, which facilitates detachment, is also
discussed in order to examine the extrapolation andredundancy of the model.
References[1] M.Kobayashi et al., Nucl. Fusion 53, 093032 (2013).
[2] Y.Igarashi et al., J.Phys: Conference Series 1036,012001 (2018)

Figure1 Distribution of attachment (red) anddetachment (blue) data based on theequation of classification boundary.