Title: A semi-supervised machine learning detector for physics events in tokamak discharges

Abstract

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelitymore » as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.« less

Authors:

Montes, Kevin Joseph  ^[1];

• Search DOE PAGES for author "Montes, Kevin Joseph"
• Search DOE PAGES for ORCID "0000-0002-0762-3708"

Rea, Cristina  ^[1];

• Search DOE PAGES for author "Rea, Cristina"
• Search DOE PAGES for ORCID "0000-0002-9948-2649"

Tinguely, Roy Alexander  ^[1];

• Search DOE PAGES for author "Tinguely, Roy Alexander"
• Search DOE PAGES for ORCID "0000-0002-3711-1834"

Sweeney, Ryan  ^[1];

• Search DOE PAGES for author "Sweeney, Ryan"
• Search DOE PAGES for ORCID "0000-0003-3408-1497"

Zhu, Jinxiang ^[1]; Granetz, Robert ^[1]

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+ Show Author Affiliations

1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Publication Date:

Wed Nov 25 00:00:00 EST 2020

Research Org.:

General Atomics, San Diego, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Fusion Energy Sciences (FES)

OSTI Identifier:

1735519

Grant/Contract Number:  

FC02-04ER54698; SC0010492

Resource Type:

Accepted Manuscript

Journal Name:

Nuclear Fusion

Additional Journal Information:

Journal Volume: 61; Journal Issue: 2; Journal ID: ISSN 0029-5515

Publisher:

IOP Science

Country of Publication:

United States

Language:

English

Subject:

70 PLASMA PHYSICS AND FUSION TECHNOLOGY; Disruption; event; detection; semi-supervised; machine-learning