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

Boyer, M. D.; Chadwick, J.

doi:10.1088/1741-4326/abe08b

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

Journal Article · Mon Mar 15 00:00:00 EDT 2021 · Nuclear Fusion

DOI:https://doi.org/10.1088/1741-4326/abe08b· OSTI ID:1779908

Boyer, M. D. ^[1]; Chadwick, J. ^[2]

Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)

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. Finally, a potential application of the model to real-time profile estimation is demonstrated.

View Accepted Manuscript (DOE)

Research Organization:: Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: AC02-09CH11466

OSTI ID:: 1779908

Alternate ID(s):: OSTI ID: 23129693

Journal Information:: Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 4 Vol. 61; ISSN 0029-5515

Publisher:: IOP ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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