Neural net modeling of equilibria in NSTX-U

Wai, J. T.; Boyer, M. D.; Kolemen, E.

doi:10.1088/1741-4326/ac77e6

Title: Neural net modeling of equilibria in NSTX-U

Journal Article · Wed Jul 06 00:00:00 EDT 2022 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ac77e6· OSTI ID:1880110

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Princeton Univ., NJ (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Princeton Univ., NJ (United States); Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two NNs relevant to equilibrium and shape control modeling, which are part of a suite of tools being developed for the National Spherical Torus Experiment-Upgrade for fast prediction, optimization, and visualization of plasma scenarios. The networks include Eqnet, a free-boundary equilibrium solver trained on the EFIT01 (Equilibrium FITtting 01) reconstruction algorithm, and Pertnet, which is trained on the Gspert code and predicts the non-rigid plasma response, a nonlinear term that arises in shape control modeling. The NNs are trained with different combinations of inputs and outputs in order to offer flexibility in use cases. In particular, Eqnet can use magnetic diagnostics as inputs and act as an EFIT-like reconstruction algorithm, or, by using pressure and current profile information the NN can act as a forward Grad–Shafranov equilibrium solver. This forward-mode version is envisioned to be implemented in the suite of tools for simulation of plasma scenarios. The reconstruction-mode version gives some performance improvements compared to the online reconstruction code real-time EFIT, especially when vessel eddy currents are significant. Here, we report strong performance for all NNs indicating that the models could reliably be used within closed-loop simulations or other applications. Some limitations are discussed.

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Research Organization:: Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-09CH11466; SC0015480; SC0015878; SC0021275

OSTI ID:: 1880110

Journal Information:: Nuclear Fusion, Vol. 62, Issue 8; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

References (32)

Identification of noncircular plasma equilibria using a neural network approach Coccorese, E.; Morabito, C.; Martone, R. Nuclear Fusion, Vol. 34, Issue 10 https://doi.org/10.1088/0029-5515/34/10/i05	journal	October 1994
Equilibrium of a toroidal plasma in a magnetic field Shafranov, V. D. Journal of Nuclear Energy. Part C, Plasma Physics, Accelerators, Thermonuclear Research, Vol. 5, Issue 4 https://doi.org/10.1088/0368-3281/5/4/307	journal	January 1963
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
Reconstruction of current profile parameters and plasma shapes in tokamaks Lao, L. L.; St. John, H.; Stambaugh, R. D. Nuclear Fusion, Vol. 25, Issue 11 https://doi.org/10.1088/0029-5515/25/11/007	journal	November 1985
Machine learning for the identification of scaling laws and dynamical systems directly from data in fusion Murari, A.; Vega, J.; Mazon, D. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 623, Issue 2 https://doi.org/10.1016/j.nima.2010.02.080	journal	November 2010
Estimation of plasma equilibrium parameters via a neural network approach* Zhu, Zi-Jian; Guo, Yong; Yang, Fei Chinese Physics B, Vol. 28, Issue 12 https://doi.org/10.1088/1674-1056/ab55d1	journal	December 2019
Deep neural network Grad–Shafranov solver constrained with measured magnetic signals Joung, Semin; Kim, Jaewook; Kwak, Sehyun Nuclear Fusion, Vol. 60, Issue 1 https://doi.org/10.1088/1741-4326/ab555f	journal	December 2019
Equilibrium properties of spherical torus plasmas in NSTX Sabbagh, S. A.; Kaye, S. M.; Menard, J. Nuclear Fusion, Vol. 41, Issue 11 https://doi.org/10.1088/0029-5515/41/11/309	journal	November 2001
Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas Piccione, A.; Berkery, J. W.; Sabbagh, S. A. Nuclear Fusion, Vol. 60, Issue 4 https://doi.org/10.1088/1741-4326/ab7597	journal	March 2020
Nonrigid, Linear Plasma Response Model Based on Perturbed Equilibria for Axisymmetric Tokamak Control Design Welander, A. S.; Deranian, R. D.; Humphreys, D. A. Fusion Science and Technology, Vol. 47, Issue 3 https://doi.org/10.13182/fst05-a778	journal	April 2005
An alternative approach to the determination of scaling law expressions for the L–H transition in Tokamaks utilizing classification tools instead of regression Gaudio, P.; Murari, A.; Gelfusa, M. Plasma Physics and Controlled Fusion, Vol. 56, Issue 11 https://doi.org/10.1088/0741-3335/56/11/114002	journal	October 2014
The plasma shape control system in the tokamak with the artificial neural network as a plasma equilibrium reconstruction algorithm Prokhorov, Artem A.; Mitrishkin, Yuri V.; Korenev, Pavel S. IFAC-PapersOnLine, Vol. 53, Issue 2 https://doi.org/10.1016/j.ifacol.2020.12.843	journal	January 2020
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
Vessel eddy current measurement for the National Spherical Torus Experiment Gates, D. A.; Menard, J. E.; Marsala, R. J. Review of Scientific Instruments, Vol. 75, Issue 12 https://doi.org/10.1063/1.1813091	journal	December 2004
Plasma boundary shape control and real-time equilibrium reconstruction on NSTX-U Boyer, M. D.; Battaglia, D. J.; Mueller, D. Nuclear Fusion, Vol. 58, Issue 3 https://doi.org/10.1088/1741-4326/aaa4d0	journal	January 2018
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
Machine learning control for disruption and tearing mode avoidance Fu, Yichen; Eldon, David; Erickson, Keith Physics of Plasmas, Vol. 27, Issue 2 https://doi.org/10.1063/1.5125581	journal	February 2020
Machine learning of turbulent transport in fusion plasmas with neural network Li, Hui; Fu, Yanlin; Li, Jiquan Plasma Science and Technology, Vol. 23, Issue 11 https://doi.org/10.1088/2058-6272/ac15ec	journal	September 2021
The linearized CREATE-L plasma response model for the control of current, position and shape in tokamaks Albanese, R.; Villone, F. Nuclear Fusion, Vol. 38, Issue 5 https://doi.org/10.1088/0029-5515/38/5/307	journal	May 1998
Closed-loop simulation with Grad-Shafranov equilibrium evolution for plasma control system development Welander, Anders; Olofsson, Erik; Sammuli, Brian Fusion Engineering and Design, Vol. 146 https://doi.org/10.1016/j.fusengdes.2019.03.191	journal	September 2019
CREATE-NL+: A robust control-oriented free boundary dynamic plasma equilibrium solver Albanese, R.; Ambrosino, R.; Mattei, M. Fusion Engineering and Design, Vol. 96-97 https://doi.org/10.1016/j.fusengdes.2015.06.162	journal	October 2015
Artificial Neural Networks for Data Analysis of Magnetic Measurements on East Wang, Bo; Xiao, Bingjia; Li, Jiangang Journal of Fusion Energy, Vol. 35, Issue 2 https://doi.org/10.1007/s10894-015-0044-z	journal	December 2015
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
Prediction of electron density and pressure profile shapes on NSTX-U using neural networks Boyer, M. D.; Chadwick, J. Nuclear Fusion, Vol. 61, Issue 4 https://doi.org/10.1088/1741-4326/abe08b	journal	March 2021
Identification of Plasma Equilibria in ITER from Magnetic Measurements Via Functional Parameterization and Neural Networks Albanese, Raffaele; Coccorese, Enzo; Gruber, Otto Fusion Technology, Vol. 30, Issue 2 https://doi.org/10.13182/fst96-a30752	journal	November 1996
Neural Network Differential Equation and Plasma Equilibrium Solver van Milligen, B. Ph.; Tribaldos, V.; Jiménez, J. A. Physical Review Letters, Vol. 75, Issue 20 https://doi.org/10.1103/physrevlett.75.3594	journal	November 1995
Data-driven profile prediction for DIII-D Abbate, J.; Conlin, R.; Kolemen, E. Nuclear Fusion, Vol. 61, Issue 4 https://doi.org/10.1088/1741-4326/abe08d	journal	March 2021
Real-Time Control of a Tokamak Plasma Using Neural Networks Bishop, Chris M.; Haynes, Paul S.; Smith, Mike E. U. Neural Computation, Vol. 7, Issue 1 https://doi.org/10.1162/neco.1995.7.1.206	journal	January 1995
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
On the potential of ruled-based machine learning for disruption prediction on JET Lungaroni, M.; Murari, A.; Peluso, E. Fusion Engineering and Design, Vol. 130 https://doi.org/10.1016/j.fusengdes.2018.02.087	journal	May 2018
Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping Lister, J. B.; Schnurrenberger, H. Nuclear Fusion, Vol. 31, Issue 7 https://doi.org/10.1088/0029-5515/31/7/005	journal	July 1991
Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma Jalalvand, Azarakhsh; Abbate, Joseph; Conlin, Rory IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, Issue 6 https://doi.org/10.1109/tnnls.2021.3085504	journal	June 2022

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