Bulletin of the American Physical Society
75th Annual Gaseous Electronics Conference
Volume 67, Number 9
Monday–Friday, October 3–7, 2022;
Sendai International Center, Sendai, Japan
The session times in this program are intended for Japan Standard Time zone in Tokyo, Japan (GMT+9)
Session DR5: Optical Diagnostics
4:00 PM–6:00 PM,
Thursday, October 6, 2022
Sendai International Center
Room: Tachibana
Chair: Holger Kersten, Kiel University, Germany
Abstract: DR5.00003 : Optical Emission Spectroscopy Measurement for Plasma Parameter Identification — from Kinetic Modeling to Data Science
4:30 PM–5:00 PM
Presenter:
Hiroshi Akatsuka
(Tokyo Institute of Technology, Tokyo)
Author:
Hiroshi Akatsuka
(Tokyo Institute of Technology, Tokyo)
Traditionally, the observed number densities of excited states obtained by line-intensity measurement have been interpreted by excitation kinetic model like modified CR models. Simultaneous equations are solved based on the kinetic models to calculate electron temperature Te, density Ne, and even the energy distribution parameter x, with which the electron energy distribution function (EEDF) is described as f(ε) ∝ exp[–C(ε/kTe)x]. With this scheme, however, troublesome mathematical procedure is inevitable to extract dominant processes. Recently, another strategy is presented to minimize the summation of the square-root-deviation between the excited-state densities observed by the OES and those calculated with the CR model. Such levels must be selected by a trust region method, BOBYQA, which is a root-finding algorithm for bound constrained optimization without using derivatives of the objective function. Examples will be presented for argon plasmas under several discharge conditions, such as low-pressure ICP, medium-pressure MWD and atmospheric-pressure DBD.
On the other hand, continuum emission analysis for the atmospheric-pressure cold plasma can be done by theoretical fitting of the observed spectrum to deduce Te and Ne. By this spectrum, the EEDF can be also examined to observe through the reinforcement learning-based visible bremsstrahlung inversion (VBI) method, where it is found that the EEDF over a limited energy-range can be reconstructed, which will be discussed in detail in the meeting.
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