Integration of Machine Learning in High-Enthalpy Plasma Spectroscopy
Paul Erik Hofmeyer, Hendrik Burghaus, Constantin Ruhdorfer, Johannes Oswald, Georg Herdrich
International Conference on Flight vehicles, Aerothermodynamics and Re-entry (FAR), pp. 1–8, 2025.
Abstract
Optical emission spectroscopy is widely used to characterize high-enthalpy plasmas because it enables measuring a range of plasma parameters. Although the spectra are relatively easy to acquire, extracting meaningful information requires extensive analysis. In this work, a novel approach is developed to automate the analysis of broadband emission spectra by training two machine learning models on synthetic data. The first model is applied to predict plasma temperatures and species number densities in a CO2 plasma jet. The second model is designed to identify the radiation from potentially occurring species in time-resolved spectra of a titanium material sample demising in an air plasma. Developing a synthetic dataset that allows a trained machine learning model to analyze experimental spectra accurately is identified as a major challenge. Overall, these models offer a significant opportunity to automate the analysis of optical emission spectra.Links
Paper: hofmeyer25_far.pdf
BibTeX
@inproceedings{hofmeyer25_far,
author = {Hofmeyer, Paul Erik and Burghaus, Hendrik and Ruhdorfer, Constantin and Oswald, Johannes and Herdrich, Georg},
year = {2025},
pages = {1--8},
title = {Integration of Machine Learning in High-Enthalpy Plasma Spectroscopy},
booktitle = {International Conference on Flight vehicles, Aerothermodynamics and Re-entry (FAR)}
}
