| Autor: |
C.A. Johnson, E.A. Unterberg, D.A. Ennis, G.J. Hartwell, D.A. Maurer |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Nuclear Materials and Energy, Vol 34, Iss , Pp 101304- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2352-1791 |
| DOI: |
10.1016/j.nme.2022.101304 |
| Popis: |
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). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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