Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

Lao, L. L.; Kruger, S.; Akcay, C.; Balaprakash, P.; Bechtel, T. A.; Howell, E.; Koo, J.; Leddy, J.; Leinhauser, M.; Liu, Y. Q.; Madireddy, S.; McClenaghan, J.; Orozco, D.; Pankin, A.; Schissel, D.; Smith, S.; Sun, X.; Williams, S.

doi:10.1088/1361-6587/ac6fff

Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

Journal Article · Mon Jun 06 00:00:00 EDT 2022 · Plasma Physics and Controlled Fusion

DOI:https://doi.org/10.1088/1361-6587/ac6fff· OSTI ID:1873871

^[1]; Kruger, S. ^[2]; Akcay, C. ^[3]; Balaprakash, P. ^[4]; Bechtel, T. A. ^[5]; ^[2]; ^[4]; ^[2]; ^[6]; ^[3]; ^[4]; McClenaghan, J. ^[3]; Orozco, D. ^[3]; Pankin, A. ^[7]; Schissel, D. ^[3]; Smith, S. ^[3]; Sun, X. ^[5]; Williams, S. ^[8]

General Atomics, San Diego, CA (United States); General Atomics, Energy & Advanced Concepts, DIII-D
TechX, Boulder, CO (United States)
General Atomics, San Diego, CA (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
General Atomics, San Diego, CA (United States); Oak Ridge Associated Univ., Oak Ridge, TN (United States)
Univ. of Delaware, Newark, DE (United States)
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Recent progress in the application of machine learning (ML)/artificial intelligence (AI) algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, motional Stark effect, and kinetic reconstruction data has been generated for developments of EFIT model-order-reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. Furthermore, a neural-network MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian process Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the Magnetohydrodynamic Resistive Spectrum - Feedback (MARS-F) code for developments of 3D-MOR surrogate models.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); General Atomics, San Diego, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

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

Grant/Contract Number:: AC02-05CH11231; AC02-06CH11357; FC02-04ER54698; FG02-95ER54309; SC0021203

OSTI ID:: 1873871

Alternate ID(s):: OSTI ID: 1893820
OSTI ID: 1962799

Report Number(s):: DOE-GA-21203

Journal Information:: Plasma Physics and Controlled Fusion, Journal Name: Plasma Physics and Controlled Fusion Journal Issue: 7 Vol. 64; ISSN 0741-3335

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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