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dc.contributor.authorBoyer, Mark
dc.contributor.authorChadwick, Jason
dc.description.abstractA 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.en_US
dc.description.tableofcontentsreadme and digital data filesen_US
dc.publisherPrinceton Plasma Physics Laboratory, Princeton Universityen_US
dc.relationNuclear Fusionen_US
dc.titlePrediction of electron density and pressure profile shapes on NSTX-U using neural networksen_US
dc.contributor.funderU. S. Department of Energyen_US
Appears in Collections:NSTX-U

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