Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

Boyer, Mark ; Chadwick, Jason
Issue date: 2021
Rights:
Creative Commons Attribution 4.0 International (CC BY)
Cite as:
Boyer, Mark & Chadwick, Jason. (2021). Prediction of electron density and pressure profile shapes on NSTX-U using neural networks [Data set]. Princeton Plasma Physics Laboratory, Princeton University. https://doi.org/10.11578/1814948
@electronic{boyer_mark_2021,
  author      = {Boyer, Mark and
                Chadwick, Jason},
  title       = {{Prediction of electron density and press
                ure profile shapes on NSTX-U using neura
                l networks}},
  publisher   = {{Princeton Plasma Physics Laboratory, Pri
                nceton University}},
  year        = 2021,
  url         = {https://doi.org/10.11578/1814948}
}
Description:

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. A potential application of the model to real-time profile estimation is demonstrated.

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# Filename Filesize
1 README.txt 1.27 KB
2 ARK_DATA.zip 74.7 MB