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Identification of Magnetic Islands in Optimized Configuration

Sora YABUMOTO¹⁾, Shinsuke SATAKE^1,2) and Hiroyuki YAMAGUCHI^1,2)

1)

The Graduate University for Advanced Studies, SOKENDAI, 322-6 Oroshi-cho, Toki, Gifu 509-5292, Japan

2)

National Institute for Fusion Science, 322-6 Oroshi-cho, Toki 509-5292, Japan

(Received 6 September 2022 / Accepted 23 December 2022 / Published 5 April 2023)

Abstract

To aim at the realization of a helical fusion reactor, we study multi-objective optimization of coil shapes, which satisfy various requirements. In the magnetic field configuration created by these coils, several unfavorable examples are found: some of them have magnetic islands or doublet configurations. In order to automatically and quickly exclude such cases that hinder the optimization, we have developed a new method to detect unfavorable magnetic surfaces by using image recognition. Binarization and erosion are performed as preprocessing, and then blanks of magnetic islands and doublets are extracted as recognition targets. Consequently, we have developed a classifier with high performance. Using this trained classifier, we have shown that almost all cases with unfavorable magnetic surfaces in various magnetic configurations can be excluded in a short time and with high precision.

Copyright (c) 2023 The Japan Society of Plasma Science and Nuclear Fusion Research

Keywords

Haar-like feature, image recognition, image processing, machine learning, magnetic island

DOI: 10.1585/pfr.18.1403018

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