U.S. Department of EnergyOffice of Scientific and Technical Information
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. 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 effective charge and fast ion diffusivity, are presented.
- Authors:
-
- OSTI
- Publication Date:
- DOE Contract Number:
- AC02-09CH11466
- Research Org.:
- Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
- Sponsoring Org.:
- U. S. Department of Energy
- Subject:
- Neural networks; Neutral beam modeling; Plasma Control
- OSTI Identifier:
- 1562069
- DOI:
- https://doi.org/10.11578/1562069
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:10.11578/1562069.
Boyer, M D, Kaye, S, & Erickson, K. Real-time capable modeling of neutral beam injection on NSTX-U using neural networks. United States. doi:https://doi.org/10.11578/1562069
Boyer, M D, Kaye, S, and Erickson, K. 2019.
"Real-time capable modeling of neutral beam injection on NSTX-U using neural networks". United States. doi:https://doi.org/10.11578/1562069. https://www.osti.gov/servlets/purl/1562069. Pub date:Fri Feb 01 04:00:00 UTC 2019
@article{osti_1562069,
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. 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 effective charge and fast ion diffusivity, are presented.},
doi = {10.11578/1562069},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Feb 01 04:00:00 UTC 2019},
month = {Fri Feb 01 04:00:00 UTC 2019}
}