Modulating sparks in a pulse train for repetitive and energy efficient plasma generation

Abstract

Spark is a widely studied plasma source for active species production; however, it experiences unstable transitions (e.g. to a thermal arc) at high frequencies or long pulse durations. In this study, the sparks generated in a pulse train were studied and modulated based on a physics-corrected deep learning method. Our results show that a highly repeatable and stable spark plasma source can be achieved by automatically adjusting the voltage amplitude according to the discharge frequency in a high-frequency pulse train within the time scale of the fluid response. The influences of the electron number density increasing mode and modulated driven voltage profiles on the energy efficiencies were also studied.