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

Abstract

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.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]
  1. Princeton Plasma Physics Lab.(PPPL), Princeton, NJ (United States)
Publication Date:
Research Org.:
Princeton Plasma Physics Lab.(PPPL), Princeton, NJ (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1503000
Grant/Contract Number:  
AC02-09CH11466
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Fusion
Additional Journal Information:
Journal Volume: 59; Journal Issue: 5; Journal ID: ISSN 0029-5515
Publisher:
IOP Science
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Citation Formats

Boyer, M. D., Kaye, S., and Erickson, K. Real-time capable modeling of neutral beam injection on NSTX-U using neural networks. United States: N. p., 2019. Web. doi:10.1088/1741-4326/ab0762.
Boyer, M. D., Kaye, S., & Erickson, K. Real-time capable modeling of neutral beam injection on NSTX-U using neural networks. United States. doi:10.1088/1741-4326/ab0762.
Boyer, M. D., Kaye, S., and Erickson, K. Fri . "Real-time capable modeling of neutral beam injection on NSTX-U using neural networks". United States. doi:10.1088/1741-4326/ab0762.
@article{osti_1503000,
title = {Real-time capable modeling of neutral beam injection on NSTX-U using neural networks},
author = {Boyer, M. D. and Kaye, S. and Erickson, K.},
abstractNote = {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 Zeff and fast ion diffusivity, are presented.},
doi = {10.1088/1741-4326/ab0762},
journal = {Nuclear Fusion},
number = 5,
volume = 59,
place = {United States},
year = {2019},
month = {3}
}

Journal Article:
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This content will become publicly available on March 22, 2020
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