Machine learning control for disruption and tearing mode avoidance

Fu, Yichen; Eldon, David; Erickson, Keith; Kleijwegt, Kornee; Lupin-Jimenez, Leonard; Boyer, Mark D.; Eidietis, Nick; Barbour, Nathaniel; Izacard, Olivier; Kolemen, Egemen

doi:10.1063/1.5125581

Title: Machine learning control for disruption and tearing mode avoidance

Journal Article · 03 February 2020 · Physics of Plasmas

DOI: https://doi.org/10.1063/1.5125581 · OSTI ID:1607465

^[1];

^[2]; Erickson, Keith ^[3]; Kleijwegt, Kornee ^[4];

^[5]; Boyer, Mark D. ^[3]; Eidietis, Nick ^[2]; Barbour, Nathaniel ^[6];

^[5];

^[7]

Princeton Univ., NJ (United States); General Atomics, Energy & Advanced Concepts, DIII-D
General Atomics, San Diego, CA (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Eindhoven Univ. of Technology, Eindhoven (The Netherlands)
Princeton Univ., NJ (United States)
Yale Univ., New Haven, CT (United States)
Eindhoven Univ. of Technology, Eindhoven (The Netherlands); Princeton Univ., NJ (United States)

In this work, real-time feedback control based on machine learning algorithms (MLA) was successfully developed and tested on DIII-D plasmas to avoid tearing modes and disruptions while maximizing the plasma performance, which is measured by normalized plasma beta. Control uses MLAs that were trained with ensemble learning methods using only the data available to the real-time Plasma Control System (PCS) from several thousand DIII-D discharges. A `tearability' metric that quantifies the likelihood of the onset of 2/1 tearing modes (TM) in a given time window, and a 'disruptivity' metric that quantifies the likelihood of the onset of plasma disruptions were first tested o -line then implemented on the PCS. A real-time control system based on these MLAs was successfully tested on DIII-D discharges, using feedback algorithms to maximize β_Ν while avoiding tearing modes and to dynamically adjust ramp down to avoid high-current disruptions in ramp down.

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Research Organization:: General Atomics, San Diego, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES) (SC-24)

Grant/Contract Number:: FC02-04ER54698; AC02-09CH11466; SC0015878

OSTI ID:: 1607465

Journal Information:: Physics of Plasmas, Journal Name: Physics of Plasmas Journal Issue: 2 Vol. 27; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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