The bipolar ionic liquid thruster employs ionic liquid as a propellant to discharge positively and negatively charged high-energy particles under an alternating current(AC)power source,effectively suppressing electroc...The bipolar ionic liquid thruster employs ionic liquid as a propellant to discharge positively and negatively charged high-energy particles under an alternating current(AC)power source,effectively suppressing electrochemical reaction and ensuring charge neutrality.Determining an optimal AC supply power source frequency is critical for sustained stable thruster operation.This study focuses on the emission characteristics of the ionic liquid thruster under varied AC conditions.The AC power supply was set within the frequency range of 0.5-64 Hz,with eight specific frequency conditions selected for experimentation.The experimental results indicate that the thruster operates steadily within a voltage range of±1470 to±1920 V,with corresponding positive polarity current ranging from 0.41 to 4.91μA and negative polarity current ranging from−0.49 to−4.10μA.During voltage polarity switching,an emission delay occurs,manifested as a prominent peak signal caused by circuit capacitance characteristics and a minor peak signal resulting from liquid droplets.Extended emission test was conducted at 16 Hz,demonstrating approximately 1 h and 50 min of consistent emission before intermittent discharge.These findings underscore the favorable impact of AC conditions within the 8-16 Hz range on the self-neutralization capability of the ionic liquid thruster.展开更多
Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing ...Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E_(th), maximum sputtering energy E_(max) and maximum sputtering yield SY_(max). In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors(R^(2) evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R^(2) of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.展开更多
基金co-supported by the National Key R&D Program of China(No.2020YFC2201001)the Shenzhen Science and Technology Program(No.20210623091808026).
文摘The bipolar ionic liquid thruster employs ionic liquid as a propellant to discharge positively and negatively charged high-energy particles under an alternating current(AC)power source,effectively suppressing electrochemical reaction and ensuring charge neutrality.Determining an optimal AC supply power source frequency is critical for sustained stable thruster operation.This study focuses on the emission characteristics of the ionic liquid thruster under varied AC conditions.The AC power supply was set within the frequency range of 0.5-64 Hz,with eight specific frequency conditions selected for experimentation.The experimental results indicate that the thruster operates steadily within a voltage range of±1470 to±1920 V,with corresponding positive polarity current ranging from 0.41 to 4.91μA and negative polarity current ranging from−0.49 to−4.10μA.During voltage polarity switching,an emission delay occurs,manifested as a prominent peak signal caused by circuit capacitance characteristics and a minor peak signal resulting from liquid droplets.Extended emission test was conducted at 16 Hz,demonstrating approximately 1 h and 50 min of consistent emission before intermittent discharge.These findings underscore the favorable impact of AC conditions within the 8-16 Hz range on the self-neutralization capability of the ionic liquid thruster.
基金supported by the National Key Research and Development Program of China (No. 2020YFC2201101)the Shenzhen Key Laboratory of Intelligent Microsatellite Constellation (No. ZDSYS20210623091808026)Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515110500)。
文摘Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E_(th), maximum sputtering energy E_(max) and maximum sputtering yield SY_(max). In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors(R^(2) evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R^(2) of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.
