skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A real-time machine learning-based disruption predictor in DIII-D

Abstract

A disruption prediction algorithm, named DPRF (Disruption Prediction using Random Forests), has run in real-time in the DIII-D plasma control system (PCS) over more than 900 discharges. DPRF naturally provides a probability mapping associated to its predictions, i.e. the disruptivity signal, now incorporated in the DIII-D PCS. Here, we report on disruption prediction accomplishments in terms of shot-by-shot performances, by simulating alarms on each discharge as in the PCS framework. Due to the optimised performance metric chosen to evaluate DPRF, we find that almost all disruptive discharges are detected in average with few hundred of milliseconds of warning time, but this comes at a high cost of false alarms produced. Performances are not satisfying ITER requirements, where the success rate has to be higher than 95%, but this is not completely unexpected. DPRF is trained on many years of major disruptions occurring during the flattop phase of the plasma current on DIII-D, but without any differentiation by cause. Furthermore, we find that DPRF is affected by a relatively high fraction of false alarms occurring during the first 500 milliseconds from the flattop onset. This subtle effect, more evident on the discharges where DPRF ran in real-time, can be marginalized bymore » taking specific precautions on the validity range of the predictions, and performances do improve. Even if presently burdened by some limitations, DPRF provides an incredible and novel advantage. Thanks to the feature contribution analysis (e.g., the identification of which signals contributed to triggering an alarm), it is possible to interpret and explain DPRF predictions. It is the first time that such interpretability features is exploited by a disruption predictor: by uncovering the disruption events' causes, a better understanding of disruption dynamics is achieved, and a clear path toward the design of disruption avoidance strategies can be provided.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [2]
  1. General Atomics, San Diego, CA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  3. Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Publication Date:
Research Org.:
General Atomics, San Diego, CA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Fusion Energy Sciences (FES)
OSTI Identifier:
1562300
Report Number(s):
DOE-GA-54698
Journal ID: ISSN 0029-5515
Grant/Contract Number:  
FC02-04ER54698; SC0014264; AC02-09CH11466
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Fusion
Additional Journal Information:
Journal Volume: 59; Journal Issue: 9; Journal ID: ISSN 0029-5515
Publisher:
IOP Science
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY; disruption prediction; machine learning; random forests; DIII-D; real-time

Citation Formats

Rea, C., Montes, K. J., Erickson, K. G., Granetz, R. S., and Tinguely, R. A. A real-time machine learning-based disruption predictor in DIII-D. United States: N. p., 2019. Web. doi:10.1088/1741-4326/ab28bf.
Rea, C., Montes, K. J., Erickson, K. G., Granetz, R. S., & Tinguely, R. A. A real-time machine learning-based disruption predictor in DIII-D. United States. doi:10.1088/1741-4326/ab28bf.
Rea, C., Montes, K. J., Erickson, K. G., Granetz, R. S., and Tinguely, R. A. Tue . "A real-time machine learning-based disruption predictor in DIII-D". United States. doi:10.1088/1741-4326/ab28bf. https://www.osti.gov/servlets/purl/1562300.
@article{osti_1562300,
title = {A real-time machine learning-based disruption predictor in DIII-D},
author = {Rea, C. and Montes, K. J. and Erickson, K. G. and Granetz, R. S. and Tinguely, R. A.},
abstractNote = {A disruption prediction algorithm, named DPRF (Disruption Prediction using Random Forests), has run in real-time in the DIII-D plasma control system (PCS) over more than 900 discharges. DPRF naturally provides a probability mapping associated to its predictions, i.e. the disruptivity signal, now incorporated in the DIII-D PCS. Here, we report on disruption prediction accomplishments in terms of shot-by-shot performances, by simulating alarms on each discharge as in the PCS framework. Due to the optimised performance metric chosen to evaluate DPRF, we find that almost all disruptive discharges are detected in average with few hundred of milliseconds of warning time, but this comes at a high cost of false alarms produced. Performances are not satisfying ITER requirements, where the success rate has to be higher than 95%, but this is not completely unexpected. DPRF is trained on many years of major disruptions occurring during the flattop phase of the plasma current on DIII-D, but without any differentiation by cause. Furthermore, we find that DPRF is affected by a relatively high fraction of false alarms occurring during the first 500 milliseconds from the flattop onset. This subtle effect, more evident on the discharges where DPRF ran in real-time, can be marginalized by taking specific precautions on the validity range of the predictions, and performances do improve. Even if presently burdened by some limitations, DPRF provides an incredible and novel advantage. Thanks to the feature contribution analysis (e.g., the identification of which signals contributed to triggering an alarm), it is possible to interpret and explain DPRF predictions. It is the first time that such interpretability features is exploited by a disruption predictor: by uncovering the disruption events' causes, a better understanding of disruption dynamics is achieved, and a clear path toward the design of disruption avoidance strategies can be provided.},
doi = {10.1088/1741-4326/ab28bf},
journal = {Nuclear Fusion},
number = 9,
volume = 59,
place = {United States},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER
journal, September 2013


Integrated modeling applications for tokamak experiments with OMFIT
journal, July 2015


Unbiased and non-supervised learning methods for disruption prediction at JET
journal, April 2009


Magnetic diagnostic system of the DIII-D tokamak
journal, February 2006

  • Strait, E. J.
  • Review of Scientific Instruments, Vol. 77, Issue 2
  • DOI: 10.1063/1.2166493

A First Analysis of JET Plasma Profile-Based Indicators for Disruption Prediction and Avoidance
journal, July 2018


Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-D
journal, February 2018


Results of the JET real-time disruption predictor in the ITER-like wall campaigns
journal, October 2013


Automatic disruption classification in JET with the ITER-like wall
journal, October 2015


Survey of disruption causes at JET
journal, April 2011


Improvements in disruption prediction at ASDEX Upgrade
journal, October 2015


Tokamak disruption alarm based on a neural network model of the high- beta limit
journal, June 1997


Implementing a finite-state off-normal and fault response system for disruption avoidance in tokamaks
journal, March 2018


Advanced tokamak research in DIII-D
journal, November 2004


Multivariate statistical models for disruption prediction at ASDEX Upgrade
journal, October 2013


Real time equilibrium reconstruction for tokamak discharge control
journal, July 1998


An advanced disruption predictor for JET tested in a simulated real-time environment
journal, January 2010


A reduced resistive wall mode kinetic stability model for disruption forecasting
journal, May 2017

  • Berkery, J. W.; Sabbagh, S. A.; Bell, R. E.
  • Physics of Plasmas, Vol. 24, Issue 5
  • DOI: 10.1063/1.4977464

Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST
journal, July 2019


Random Forests
journal, January 2001


Advanced Disruption Predictor Based On The Locked Mode Signal: Application To Jet
conference, October 2016

  • Vega, Jesús; Moreno, Raul; Pereira, Augusto
  • Proceedings of 1st EPS conference on Plasma Diagnostics — PoS(ECPD2015)
  • DOI: 10.22323/1.240.0028

A cross-tokamak neural network disruption predictor for the JET and ASDEX Upgrade tokamaks
journal, April 2005