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http://arks.princeton.edu/ark:/88435/dsp015999n646v
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Boyer, Mark | |
dc.contributor.author | Chadwick, Jason | |
dc.date.accessioned | 2021-02-19T20:53:48Z | - |
dc.date.available | 2021-02-19T20:53:48Z | - |
dc.date.issued | 2021-02 | |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp015999n646v | - |
dc.description.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. | en_US |
dc.description.tableofcontents | readme and digital data files | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Princeton Plasma Physics Laboratory, Princeton University | en_US |
dc.relation | Nuclear Fusion | en_US |
dc.subject | en_US | |
dc.title | Prediction of electron density and pressure profile shapes on NSTX-U using neural networks | en_US |
dc.type | Dataset | en_US |
dc.contributor.funder | U. S. Department of Energy | en_US |
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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