Real-time capable modeling of neutral beam injection on NSTX-U using neural networks

Boyer, M. D.; Kaye, S.; Erickson, K.

doi:10.1088/1741-4326/ab0762

Title: Real-time capable modeling of neutral beam injection on NSTX-U using neural networks

Journal Article · Fri Mar 22 00:00:00 EDT 2019 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/ab0762· OSTI ID:1503000

^[1]; Kaye, S. ^[1];

^[1]

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

A new model of heating, current drive, torque and other effects of neutral beam injection on NSTX-U that uses neural networks has been developed. The model has been trained and tested on the results of the Monte Carlo code NUBEAM for the database of experimental discharges taken during the first operational campaign of NSTX-U. By projecting flux surface quantities onto empirically derived basis functions, the model is able to efficiently and accurately reproduce the behavior of both scalars, like the total neutron rate and shine through, and profiles, like beam current drive and heating. With this being said, the model has been tested on the NSTX-U real-time computer, demonstrating a rapid execution time orders of magnitude faster than the Monte Carlo code that is well suited for the iterative calculations needed to interpret experimental results, optimization during scenario development activities, and real-time plasma control applications. Simulation results of a proposed design for a nonlinear observer that embeds the neural network calculations to estimate the poloidal flux profile evolution, as well as Z_eff and fast ion diffusivity, are presented.

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

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-09CH11466

OSTI ID:: 1503000

Journal Information:: Nuclear Fusion, Vol. 59, Issue 5; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 34 works

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