Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less com...Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.展开更多
In this study, we examined the key particles and chemical reactions that substantially influence plasma characteristics. In summarizing the chemical reaction model for the discharge process of N_(2)–O_(2)–H_(2)O(g)m...In this study, we examined the key particles and chemical reactions that substantially influence plasma characteristics. In summarizing the chemical reaction model for the discharge process of N_(2)–O_(2)–H_(2)O(g)mixed gases, 65 particle types and 673 chemical reactions were investigated. On this basis, a global model of atmospheric pressure humid air discharge plasma was developed, with a focus on the variation of charged particles densities and chemical reaction rates with time under the excitation of a 0–200 Td pulsed electric field. Particles with a density greater than 1% of the electron density were classified as key particles. For such particles, the top ranking generation or consumption reactions(i.e. where the sum of their rates was greater than 95% of the total rate of the generation or consumption reactions) were classified as key chemical reactions. On the basis of the key particles and reactions identified, a simplified global model was derived. A comparison of the global model with the simplified global model in terms of the model parameters, particle densities, reaction rates(with time), and calculation efficiencies demonstrated that both models can adequately identify the key particles and chemical reactions reflecting the chemical process of atmospheric pressure discharge plasma in humid air. Thus, by analyzing the key particles and chemical reaction pathways, the charge and substance transfer mechanism of atmospheric pressure pulse discharge plasma in humid air was revealed, and the mechanism underlying water vapor molecules’ influence on atmospheric pressure air discharge was elucidated.展开更多
In this paper, a pulsed-dc CH;OH/Ar plasma jet generated at atmospheric pressure is studied by laser-induced fluorescence（LIF） and optical emission spectroscopy（OES）. A gas–liquid bubbler system is proposed to in...In this paper, a pulsed-dc CH;OH/Ar plasma jet generated at atmospheric pressure is studied by laser-induced fluorescence（LIF） and optical emission spectroscopy（OES）. A gas–liquid bubbler system is proposed to introduce the methanol vapor into the argon gas, and the CH3OH/Ar volume ratio is kept constant at about 0.1%. Discharge occurs in a 6-mm needle-to-ring gap in an atmospheric-pressure CH;OH/Ar mixture. The space-resolved distributions of OH LIF inside and outside the nozzle exhibit distinctly different behaviors. And, different production mechanisms of OH radicals in the needle-to-ring discharge gap and afterglow of plasma jet are discussed. Besides, the optical emission lines of carbonaceous species, such as CH, CN, and C;radicals, are identified in the CH;OH/Ar plasma jet. Finally, the influences of operating parameters（applied voltage magnitude, pulse frequency, pulsewidth） on the OH radical density are also presented and analyzed.展开更多
基金partially supported by National Natural Science Foundation of China(No.52377155)the State Key Laboratory of Reliability and Intelligence of Electrical Equipment(No.EERI-KF2021001)Hebei University of Technology。
文摘Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.
基金supported by National Natural Science Foundation of China(No.51907145)。
文摘In this study, we examined the key particles and chemical reactions that substantially influence plasma characteristics. In summarizing the chemical reaction model for the discharge process of N_(2)–O_(2)–H_(2)O(g)mixed gases, 65 particle types and 673 chemical reactions were investigated. On this basis, a global model of atmospheric pressure humid air discharge plasma was developed, with a focus on the variation of charged particles densities and chemical reaction rates with time under the excitation of a 0–200 Td pulsed electric field. Particles with a density greater than 1% of the electron density were classified as key particles. For such particles, the top ranking generation or consumption reactions(i.e. where the sum of their rates was greater than 95% of the total rate of the generation or consumption reactions) were classified as key chemical reactions. On the basis of the key particles and reactions identified, a simplified global model was derived. A comparison of the global model with the simplified global model in terms of the model parameters, particle densities, reaction rates(with time), and calculation efficiencies demonstrated that both models can adequately identify the key particles and chemical reactions reflecting the chemical process of atmospheric pressure discharge plasma in humid air. Thus, by analyzing the key particles and chemical reaction pathways, the charge and substance transfer mechanism of atmospheric pressure pulse discharge plasma in humid air was revealed, and the mechanism underlying water vapor molecules’ influence on atmospheric pressure air discharge was elucidated.
基金supported by the National Natural Science Foundation of China(Grant Nos.11465013 and 11375041)the Natural Science Foundation of Jiangxi Province,China(Grant Nos.20151BAB212012 and 20161BAB201013)the International Science and Technology Cooperation Program of China(Grant No.2015DFA61800)
文摘In this paper, a pulsed-dc CH;OH/Ar plasma jet generated at atmospheric pressure is studied by laser-induced fluorescence（LIF） and optical emission spectroscopy（OES）. A gas–liquid bubbler system is proposed to introduce the methanol vapor into the argon gas, and the CH3OH/Ar volume ratio is kept constant at about 0.1%. Discharge occurs in a 6-mm needle-to-ring gap in an atmospheric-pressure CH;OH/Ar mixture. The space-resolved distributions of OH LIF inside and outside the nozzle exhibit distinctly different behaviors. And, different production mechanisms of OH radicals in the needle-to-ring discharge gap and afterglow of plasma jet are discussed. Besides, the optical emission lines of carbonaceous species, such as CH, CN, and C;radicals, are identified in the CH;OH/Ar plasma jet. Finally, the influences of operating parameters（applied voltage magnitude, pulse frequency, pulsewidth） on the OH radical density are also presented and analyzed.
