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Title: Prediction of electron density and pressure profile shapes on NSTX-U using neural networks
Contributors: Boyer, Mark
Chadwick, Jason
U. S. Department of Energy
Issue Date: Feb-2021
Publisher: Princeton Plasma Physics Laboratory, Princeton University
Related Publication: Nuclear Fusion
Abstract: 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.
Appears in Collections:NSTX-U

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