Predicting Discharge Mode in Dielectric Barrier Discharge (DBD) Systems using Machine Learning: Importance, Algorithms, and Perspectives

This work addresses the importance of predicting discharge modes in dielectric barrier discharge (DBD) plasma systems, which are essential for optimising their performance and ensuring their safe operation. Several parameters can influence discharge characteristics, including applied voltage, gas pressure, gas temperature, electrode configuration, dielectric material and gas type. One of the critical factors affecting the performance of DBD systems is the mode of discharge, which can be filamentary or homogeneous. This paper highlights the potential of machine learning to predict the discharge mode in DBD systems and examines the main data collection challenges in this area. It aims to explore ways of collecting more data in order to train more accurate and reliable machine learning models for predicting discharge modes in DBD systems. [slides]