DOAJ Open Access 2023

Diagnosing electron temperature using machine learning and neutral tungsten spectral emission

C.A. Johnson E.A. Unterberg D.A. Ennis G.J. Hartwell D.A. Maurer

Abstrak

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).

Topik & Kata Kunci

Nuclear engineering. Atomic power

Penulis (5)

C.A. Johnson

E.A. Unterberg

D.A. Ennis

G.J. Hartwell

D.A. Maurer

Format Sitasi

APA MLA BibTeX

Johnson, C., Unterberg, E., Ennis, D., Hartwell, G., Maurer, D. (2023). Diagnosing electron temperature using machine learning and neutral tungsten spectral emission. https://doi.org/10.1016/j.nme.2022.101304

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.1016/j.nme.2022.101304
Akses: Open Access ✓