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

Montes, K. J.; Rea, C.; Granetz, R. S.; Tinguely, R. A.; Eidietis, N.; Meneghini, O. M.; Chen, D. L.; Shen, B.; Xiao, B. J.; Erickson, K.; Boyer, M. D.

doi:10.1088/1741-4326/ab1df4

Title: Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

Journal Article · Tue Apr 30 00:00:00 EDT 2019 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ab1df4· OSTI ID:1569035

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^[1]; Granetz, R. S. ^[1];

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^[2]; Meneghini, O. M. ^[2]; Chen, D. L. ^[3]; Shen, B. ^[3]; Xiao, B. J. ^[3]; Erickson, K. ^[4];

^[4]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
General Atomics, San Diego, CA (United States)
Chinese Academy of Sciences (CAS), Hefei (China)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

In this paper, we report on disruption prediction using a shallow machinelearning method known as Random Forests, trained on large databases containingonly plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ~10⁶ times throughout ~10⁴ discharges (disruptive and non-disruptive) over the last 4years of operation. It is found that a number of parameters (e.g.P_rad/P_input,l_i,n/n_G,B_n=1/B₀) exhibit changes in aggregate as a disruption is approached on one ormore of these tokamaks. However, for each machine, the most useful parameters,as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices "close to disruption" and "far from disruption," it is found that the prediction algorithms differ substantially in performance among the three machines ona time slice-by-time slice basis, but have similar disruption detection rates (~80-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which developmentof a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing inputsignals, which may have implications for avoiding disruptive scenarios. Lastly, to further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); General Atomics, San Diego, CA (United States)

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

Grant/Contract Number:: FC02-04ER54698; FC02-99ER54512; SC0014264; SC0010720; SC0010492

OSTI ID:: 1569035

Journal Information:: Nuclear Fusion, Vol. 59, Issue 9; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 44 works

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Cited By (5)

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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
A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET Pau, A.; Fanni, A.; Carcangiu, S. Nuclear Fusion, Vol. 59, Issue 10 https://doi.org/10.1088/1741-4326/ab2ea9	journal	August 2019
27th IAEA Fusion Energy Conference: summary of sessions EX/C, EX/S and PPC Campbell, D. J. Nuclear Fusion, Vol. 60, Issue 2 https://doi.org/10.1088/1741-4326/ab5cee	journal	January 2020
An application of survival analysis to disruption prediction via Random Forests Tinguely, R. A.; Montes, K. J.; Rea, C. arXiv https://doi.org/10.48550/arxiv.1907.04291	text	January 2019