Experimental and numerical investigation on the uniformity of nanosecond pulsed dielectric barrier discharge influenced by pulse parameters

Abstract

Nanosecond (ns) pulsed dielectric barrier discharge (DBD) is considered as a promising method to produce controllable large-volume and high activity low-temperature plasma at atmospheric pressure, which makes it suitable for wide applications. In this paper, the ns pulse power supply is used to excite Ar DBD and the influences of the pulse parameters (voltage amplitude, pulse width, pulse rise and fall times) on the DBD uniformity are investigated. The gas gap voltage (Ug) and conduct current (Ig) are separated from the measured voltage and current waveforms to analyze the influence of electrical parameters. The spectral line intensity ratio of two Ar excited species is used as an indicator of the electron temperature (Te). The time resolved discharge processes are recorded by ICCD camera and a one-dimensional fluid model is employed to simulate the spatial and temporal distributions of electrons, ions, metastable argon atoms and Te. Combined the experimental and numerical results, the mechanism of the pulse parameters influencing on the discharge uniformity is discussed. It shows the space electric field intensity and the space particles’ densities are mainly responsible for the variation of discharge uniformity. With the increasing of voltage and pulse width, the electric field intensity and the density of space particles increased, which result in the discharge mode transition from non-uniform to uniform, and then non-uniform. Furthermore, the extension of pulse rise and fall times leads to the discharge transition from uniform to non-uniform. The results are helpful to reveal the mechanism of ns pulsed DBD mode transition and realize controllable and uniform plasma sources at atmospheric pressure.

Generation of meter-scale nanosecond pulsed DBD and the intelligent evaluation based on multi-dimensional feature parameter extraction

“…Considering the complicated discharge characteristics of large-scale DBD under different experimental conditions, it is necessary to extract the discharge characteristic parameters and establish the correction between multi-dimensional discharge parameters and plasma characteristics [20,21]. Machine learning is an effective method that can independently learn the structure and internal patterns of input sample data, which has been applied in deep-going analysis of plasma properties [22], such as surface micro-discharge identification [23], trace gas detection [24], carbon dioxide methane reforming [25], etc.…”

Effect of dielectric material on the uniformity of nanosecond pulsed dielectric barrier discharge

“…These findings have demonstrated that the dielectric materials are of great importance for the discharge characteristics and the application effects of DBDs. However, the functions of the dielectric materials participating in the DBD processes are more complex, especially in DBD pulsed by nanosecond (ns) pulses, which involve the fast spatiotemporal evolution of the space electric field, the seed electron production, the charge accumulation and decay on dielectric barrier layer surface [30][31][32]. The previous results are mostly observed in specific conditions and there is lack of the comprehensive studies on the mechanisms of the dielectric materials on DBD processes.…”