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

Boyer, M. D. ; Kaye, S. ; Erickson, K.
Issue date: 2019
Rights:
Creative Commons Attribution 4.0 International (CC BY)
Cite as:
Boyer, M. D., Kaye, S., & Erickson, K. Real-time capable modeling of neutral beam injection on NSTX-U using neural networks [Data set]. Princeton Plasma Physics Laboratory, Princeton University. https://doi.org/10.11578/1562069
@electronic{boyer_m_d_unknown,
  author      = {Boyer, M. D. and
                Kaye, S. and
                Erickson, K.},
  title       = {{Real-time capable modeling of neutral be
                am injection on NSTX-U using neural netw
                orks}},
  publisher   = {{Princeton Plasma Physics Laboratory, Pri
                nceton University}},
  url         = {https://doi.org/10.11578/1562069}
}
Description:

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.

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