In this paper, we investigated the optimization of pulse-modulated radio frequency (rf) discharges in the range of very high frequency from 50 to 800 MHz by a fluid model. A very strong Peak Current in the First Period (PCFP) during the power-on phase can be observed only when the excitation frequency is large enough, usually larger than 50 MHz, and the reversal electric field near the anode due to the accumulation of electrons contributes greatly to the formation of this peak current from the simulation data. The highest electron temperature is achieved in the first period, while the largest electron density is usually obtained in the last period during the power-on phase. By increasing the duty cycle, the value of PCFP increases initially, then it reaches the peak value at a duty cycle of approximately 70%, and later it drops to the normal value generated in a continuous rf discharge, and the maximum electron temperature also shows the similar evolution. However, as the duty cycle is increased, the electron density is always enhanced during the power-on phase. According to the simulation results, the duty cycle and modulation frequency can be effectively applied to modulate and optimize the electron density and electron temperature for applications.
In this paper, we performed a one-dimensional fluid model to study the mechanism and optimization of pulse-modulated Radio-Frequency (RF) discharges at atmospheric pressure assisted by short pulse voltages. The evolution of discharge current density, helium metastable (He*) density, and total electron density from the simulation demonstrates that the ignition of RF discharge could be effectively enhanced by the short pulsed discharge, and a large Peak Current in the First Period (PCFP) can be produced, which agrees well with the experimental measurements. Due to the assistance of pulsed voltage, a strong electric field could be formed near the anode with the same polarity of that near the cathode, which can reaccelerate the electrons near the anode to generate a large PCFP. Based on the simulation results, reducing the time interval and increasing the pulse rise rate are very helpful to enhance the ignition of subsequent RF discharge by strengthening the electric field near the anode. It is shown that by choosing the appropriate time interval and pulse rise rate, the pulse-modulated RF discharge assisted by the pulsed discharge can be effectively modulated and optimized for applications.
Plasma simulation is an important but sometimes time-consuming approach to study the discharge behaviors of atmospheric pulsed discharges. In this work, an efficient simulation method is proposed by introducing deep learning to investigate the discharge characteristics driven by very short pulsed voltages. A loss function is designed and optimized to minimize the discrepancy between the Deep Neural Network (DNN) and the verified fluid model. The prediction data obtained via well-trained DNN can accurately and efficiently reveal the key discharge characteristics, such as the waveforms of discharge current and gap voltage, spatial profiles of charged particles density and electric field. The spatial distributions of charged particles density and electric field obtained from DNN are also given to unveil the underlying mechanisms. Additionally, the predictions from deep learning and the formula analysis both highlight that the breakdown voltage and current density can be effectively reduced by increasing repetition frequency, which quantitatively agrees well with the experimental observations. This study provides a great potential promise for vastly improving the simulation efficiency by introducing deep learning in the field of atmospheric plasmas computation.
Pulse-modulated discharge is an effective way to improve the stability of radio-frequency (rf) discharges. Previous studies have shown that with the power frequency increasing to the ultra-high frequency (UHF) band, the introduction of pulse modulation in rf discharges will bring about new discharge behaviors. In this paper, the fluid model is adopted to numerically investigate the new discharge characteristics in dielectric barrier discharges (DBDs) with the rf frequency larger than 500 MHz. A very large current peak occurs in the first positive and negative half cycle during the power-on phase, respectively. The spatial structure of electric field is given to further understand the underpinning physics of the large current peaks. Furthermore, the effects of duty cycle, modulation frequency and voltage modulation rates on the large current peaks are examined based on the computational data. This numerical study will deepen the understanding of DBDs modulated by pulses in the UHF band.
Numerical simulation is an essential way to investigate the discharge behaviors of atmospheric low-temperature plasmas (LTPs). In this study, a deep neural network (DNN) with multiple hidden layers is constructed to surrogate the fluid model to investigate the discharge characteristics of atmospheric helium dielectric barrier discharges (DBDs) with very high computational efficiency, working as an example to show the ability and validity of DNN to explore LTPs. The DNN is trained by the well-formed training datasets obtained from a verified fluid model, and a designed loss function coupled in the DNN program is continuously optimized to achieve a better prediction performance. The predicted data show that the essential discharge characteristics of atmospheric DBDs such as the discharge current waveforms, spatial profiles of charged particles, and electric field can be yielded by the well-trained DNN program with great accuracy only in several seconds, and the predicted evolutionary discharge trends are consistent with the previous simulations and experimental observations. Additionally, the constructed DNN shows good generalization performance for multiple input attributes, which indicates a great potential promise for vastly extending the range of discharge parameters. This study provides a useful paradigm for future explorations of machine learning-based methods in the field of atmospheric LTP simulation without high-cost calculation.
In recent years, the in situ resource utilization of CO2 on Mars for oxygen and carbon monoxide production has attracted increasing attention. Dielectric barrier discharges (DBDs) have great potential for large-scale industrial application of CO2 decomposition, and the nonlinear behaviors of DBDs are directly related to the discharge stability. In this paper, a fluid model is built to investigate the influence of gap width on temporal nonlinear behaviors in CO2 DBDs driven by tailored voltages under Martian conditions (the pressure and temperature are 4.5 Torr and 210 K, respectively). The simulation results show that, with the increase in the gap width, the discharge evolves from period-one state into period-two state, then changes into chaos, and finally undergoes an inverse period-doubling bifurcation from reverse period-two discharge to period-one discharge. After the CO2 discharge is extinguished, the electron density drops rapidly, and the dominant charged particles in the discharge region are heavy CO3− and CO2+ ions. As the gap width increases, the heavy ions produced by the previous discharge cannot be completely dissipated and stay in the sheath region, which makes the subsequent discharge easy to be ignited and reduces the breakdown voltage, leading to the evolution from period-one discharge to period-two discharge. When the gap width is increased to 5 mm, a lot of charged particles stay in the discharge gap, and these charged particles, especially electrons, are driven to the electrodes by the applied voltage, forming a reverse electric field, which inhibits the development of positive discharge and facilitates the formation of negative discharge. Then, as the gap width continues to increase, the density and spatial distribution of residual ions in the sheath region at the beginning of the negative discharge for two consecutive voltage periods are gradually equal, resulting in the discharge evolution from reverse period-two state to reverse period-one state. This study could deepen the understanding of the underpinning physics of nonlinear behaviors, and provide a groundwork for actively regulating the evolution of nonlinear behaviors.
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