Machine learning assisted optical diagnostics on a cylindrical atmospheric pressure surface dielectric barrier discharge

• Main Authors: Stefas, Dimitrios, Giotis, Konstantinos, Invernizzi, Laurent, Höft, Hans, Hassouni, Khaled, Prasanna, Swaminathan, Svarnas, Panagiotis, Lombardi, Guillaume, Gazeli, Kristaq
• Format: Journal Article
• Language: English
• Published: 10-04-2024
• Subjects: Physics - Data Analysis, Statistics and Probability; Physics - Plasma Physics
• DOI: 10.48550/arxiv.2404.06817

The present study explores combining machine learning (ML) algorithms with
standard optical diagnostics (such as time-integrated emission spectroscopy and
imaging) to accurately predict operating conditions and assess the emission
uniformity of a cylindrical surface Dielectric Barrier Discharge (SDBD). It is
demonstrated that ML can be complementary with these optical diagnostics and
identify peculiarities associated with the discharge emission pattern at
different high voltage waveforms (AC and pulsed) and amplitudes. By employing
unsupervised (Principal Component Analysis (PCA)) and supervised (Multilayer
Perceptron (MLP) neural networks) algorithms, the applied voltage waveform and
amplitude are categorised and predicted based on correlations/differences
identified within large amounts of corresponding data. PCA allowed us to
effectively classify the voltage waveforms and amplitudes applied to the SDBD
through a transformation of the spectroscopic/imaging data into principal
components (PCs) and their projection to a two-dimensional PC space.
Furthermore, an accurate prediction of the voltage amplitude is achieved using
the MLP which is trained with PCA-preprocessed data. A particularly interesting
aspect of this concept involves examining the uniformity of the emission
pattern of the discharge. This is achieved by analysing spectroscopic data
recorded at four different regions around the SDBD surface using the two
ML-based techniques. These discoveries are instrumental in enhancing
plasma-induced processes. They open up new avenues for real-time control,
monitoring, and optimization of plasma-based applications across diverse fields
such as flow control for the present SDBD.