Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

Boyer, M. D.; Rea, C.; Clement, M.

doi:10.1088/1741-4326/ac359e

Title: Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

Journal Article · 15 December 2021 · Nuclear Fusion

DOI: https://doi.org/10.1088/1741-4326/ac359e · OSTI ID:1866603

^[1];

^[2];

^[3]

Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); General Atomics, Energy & Advanced Concepts, DIII-D
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptive boundary can be calculated. The disruptivity of points is calculated from an empirical machine learning predictor that generates the likelihood of disruption. While the likelihood generated by such empirical models can be compared to a threshold to trigger a disruption mitigation system, the safe operating region calculation enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested using a random forest disruption predictor fit on data from DIII-D. The safe operating region identification algorithm is applied to historical data from DIII-D showing the evolution of disruptive boundaries and the potential impact of optimization of the operating point. Real-time relevant execution times are made possible by parallelizing many of the calculation steps and implementing the algorithm on a graphics processing unit. Lastly, a real-time capable algorithm for optimizing the target operating point within the identified constraints is also proposed and simulated.

View Accepted Manuscript (DOE)

Cite

Export

Save

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

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

Grant/Contract Number:: AC02-09CH11466; FC02-04ER54698; SC0014264

OSTI ID:: 1866603

Report Number(s):: DOE-GA-54698

Journal Information:: Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 2 Vol. 62; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

References (25)

Towards model-based current profile control at DIII-D Ou, Y.; Luce, T. C.; Schuster, E. Fusion Engineering and Design, Vol. 82, Issue 5-14 https://doi.org/10.1016/j.fusengdes.2007.04.016	journal	October 2007
Avoidance of vertical displacement events in DIII-D using a neural network growth rate estimator Sammuli, B. S.; Barr, J. L.; Humphreys, D. A. Fusion Engineering and Design, Vol. 169 https://doi.org/10.1016/j.fusengdes.2021.112492	journal	August 2021
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/a:1010933404324	journal	January 2001
Predicting disruptive instabilities in controlled fusion plasmas through deep learning Kates-Harbeck, Julian; Svyatkovskiy, Alexey; Tang, William Nature, Vol. 568, Issue 7753 https://doi.org/10.1038/s41586-019-1116-4	journal	April 2019
A reduced resistive wall mode kinetic stability model for disruption forecasting Berkery, J. W.; Sabbagh, S. A.; Bell, R. E. Physics of Plasmas, Vol. 24, Issue 5 https://doi.org/10.1063/1.4977464	journal	May 2017
Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks Rea, C.; Montes, K. J.; Pau, A. Fusion Science and Technology, Vol. 76, Issue 8 https://doi.org/10.1080/15361055.2020.1798589	journal	September 2020
Tokamak disruption alarm based on a neural network model of the high- beta limit Wroblewski, D.; Jahns, G. L.; Leuer, J. A. Nuclear Fusion, Vol. 37, Issue 6 https://doi.org/10.1088/0029-5515/37/6/i02	journal	June 1997
A cross-tokamak neural network disruption predictor for the JET and ASDEX Upgrade tokamaks Windsor, C. G.; Pautasso, G.; Tichmann, C. Nuclear Fusion, Vol. 45, Issue 5 https://doi.org/10.1088/0029-5515/45/5/004	journal	April 2005
Unbiased and non-supervised learning methods for disruption prediction at JET Murari, A.; Vega, J.; Rattá, G. A. Nuclear Fusion, Vol. 49, Issue 5 https://doi.org/10.1088/0029-5515/49/5/055028	journal	April 2009
Survey of disruption causes at JET de Vries, P. C.; Johnson, M. F.; Alper, B. Nuclear Fusion, Vol. 51, Issue 5 https://doi.org/10.1088/0029-5515/51/5/053018	journal	April 2011
Toroidal current profile control during low confinement mode plasma discharges in DIII-D via first-principles-driven model-based robust control synthesis Barton, Justin E.; Boyer, Mark D.; Shi, Wenyu Nuclear Fusion, Vol. 52, Issue 12 https://doi.org/10.1088/0029-5515/52/12/123018	journal	November 2012
Control of the tokamak safety factor profile with time-varying constraints using MPC Maljaars, E.; Felici, F.; de Baar, M. R. Nuclear Fusion, Vol. 55, Issue 2 https://doi.org/10.1088/0029-5515/55/2/023001	journal	January 2015
Integrated modeling applications for tokamak experiments with OMFIT Meneghini, O.; Smith, S. P.; Lao, L. L. Nuclear Fusion, Vol. 55, Issue 8 https://doi.org/10.1088/0029-5515/55/8/083008	journal	July 2015
Real-time capable first principle based modelling of tokamak turbulent transport Citrin, J.; Breton, S.; Felici, F. Nuclear Fusion, Vol. 55, Issue 9 https://doi.org/10.1088/0029-5515/55/9/092001	journal	July 2015
Non-linear model-based optimization of actuator trajectories for tokamak plasma profile control Felici, F.; Sauter, O. Plasma Physics and Controlled Fusion, Vol. 54, Issue 2 https://doi.org/10.1088/0741-3335/54/2/025002	journal	January 2012
First-principles-driven model-based current profile control for the DIII-D tokamak via LQI optimal control Boyer, Mark D.; Barton, Justin; Schuster, Eugenio Plasma Physics and Controlled Fusion, Vol. 55, Issue 10 https://doi.org/10.1088/0741-3335/55/10/105007	journal	September 2013
Automatic disruption classification in JET with the ITER-like wall Cannas, B.; de Vries, P. C.; Fanni, A. Plasma Physics and Controlled Fusion, Vol. 57, Issue 12 https://doi.org/10.1088/0741-3335/57/12/125003	journal	October 2015
Self-consistent core-pedestal transport simulations with neural network accelerated models Meneghini, O.; Smith, S. P.; Snyder, P. B. Nuclear Fusion, Vol. 57, Issue 8 https://doi.org/10.1088/1741-4326/aa7776	journal	July 2017
Enhanced reproducibility of L-mode plasma discharges via physics-model-based q -profile feedback control in DIII-D Schuster, E.; Wehner, W. P.; Barton, J. E. Nuclear Fusion, Vol. 57, Issue 11 https://doi.org/10.1088/1741-4326/aa7cab	journal	August 2017
Real-time capable modeling of neutral beam injection on NSTX-U using neural networks Boyer, M. D.; Kaye, S.; Erickson, K. Nuclear Fusion, Vol. 59, Issue 5 https://doi.org/10.1088/1741-4326/ab0762	journal	March 2019
Progress in disruption prevention for ITER Strait, E. J.; Barr, J. L.; Baruzzo, M. Nuclear Fusion, Vol. 59, Issue 11 https://doi.org/10.1088/1741-4326/ab15de	journal	June 2019
Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST Montes, K. J.; Rea, C.; Granetz, R. S. Nuclear Fusion, Vol. 59, Issue 9 https://doi.org/10.1088/1741-4326/ab1df4	journal	July 2019
A real-time machine learning-based disruption predictor in DIII-D Rea, C.; Montes, K. J.; Erickson, K. G. Nuclear Fusion, Vol. 59, Issue 9 https://doi.org/10.1088/1741-4326/ab28bf	journal	July 2019
Fast Model Predictive Control Using Online Optimization Wang, Yang; Boyd, Stephen IEEE Transactions on Control Systems Technology, Vol. 18, Issue 2 https://doi.org/10.1109/tcst.2009.2017934	journal	March 2010
Backstepping Control of the Toroidal Plasma Current Profile in the DIII-D Tokamak Boyer, Mark D.; Barton, Justin; Schuster, Eugenio IEEE Transactions on Control Systems Technology, Vol. 22, Issue 5 https://doi.org/10.1109/tcst.2013.2296493	journal	September 2014

Similar Records

Machine learning control for disruption and tearing mode avoidance

Journal Article · 2020 · Physics of Plasmas · OSTI ID:1607465

Fu, Yichen; Eldon, David; Erickson, Keith; +7 more

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

Journal Article · 2019 · Nuclear Fusion · OSTI ID:1562300

Rea, C.; Montes, K. J.; Erickson, K. G.; +2 more

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Journal Article · 2024 · Journal of Fusion Energy (Online) · OSTI ID:2377869

Keith, Zander; Nagpal, Chirag; Rea, Cristina; +1 more

Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
avoidance
control
disruption
graphics processing unit
real-time
tokamak

Title: Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

Citation Formats

References (25)

Similar Records

Related Subjects