• Energy Research

The properties of emitting ions in a plasma provides both potential for plasma diagnostics and key information required for plasma modeling. Generalized collisional radiative theory provides a powerful tool for the modeling of low and moderately dense plasmas. A new Python program is presented that solves the collisional radiative and ionization balance equations for application to fusion, laboratory, and astrophysical plasmas. It produces generalized coefficients that can be easily imported into existing plasma modeling codes and spectral diagnostics. An overview of the code is presented, along with selected results for applications to high-Z plasma facing components. 2010 MSC: 00-01, 99-00

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).

Nuclear materials and energy 37, 101514 - (2023). doi:10.1016/j.nme.2023.101514 Published by Elsevier, Amsterdam [u.a.]