Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp015999n646v
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 |
Keywords: | |
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. |
URI: | http://arks.princeton.edu/ark:/88435/dsp015999n646v |
Appears in Collections: | NSTX-U |
Files in This Item:
File | Description | Size | Format | |
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README.txt | 1.27 kB | Text | View/Download | |
ARK_DATA.zip | 76.54 MB | Unknown | View/Download |
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