Abstract
Current spectroscopic based erosion diagnostics require both Te and ne measurements in addition to detailed atomic physics and collisional radiative (CR) modeling. Machine Learning (ML) techniques are used to address the temperature measurement requirement for erosion diagnosis. ML techniques are combined with tungsten spectroscopic diagnosis trained with co-located Langmuir probe measurements in the Compact Toroidal Hybrid (CTH) to obtain a spectroscopic based local electron temperature diagnostic. Initial analysis using synthetic data and a Neutral Network (NN) suggests a temperature diagnostic obtained with experimental data is feasible. ML methods have the potential to bypass sources of error in traditional tungsten erosion diagnosis by taking the place of required atomic and CR modeling which introduce inherent uncertainties. Temperature diagnosed could be used as input to current erosion diagnosis techniques (the S/XB method).
Original language | English |
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Article number | 101304 |
Journal | Nuclear Materials and Energy |
Volume | 34 |
DOIs | |
State | Published - Mar 2023 |
Funding
This research was supported by the U.S. Department of Energy Fusion Energy Sciences Postdoctoral Research Program administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE . ORISE is managed by Oak Ridge Associated Universities (ORAU), USA under DOE contract number DE-SC0014664 . Work also supported under DOE, USA grants DE-AC05-00OR22725 , DE-FG02-00ER54610 and DE-SC0015877 . All opinions expressed in this paper are the author’s and do not necessarily reflect the policies and views of DOE, ORAU, or ORISE. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).
Funders | Funder number |
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U.S. Department of Energy Fusion Energy Sciences | |
U.S. Department of Energy | |
Oak Ridge Associated Universities | DE-AC05-00OR22725, DE-SC0015877, DE-FG02-00ER54610, DE-SC0014664 |
Oak Ridge Institute for Science and Education |
Keywords
- Collisional radiative
- Line ratios
- Machine learning
- Spectroscopy
- Tungsten