典型文献
Machine learning-based method to adjust electron anomalous conductivity profile to experimentally measured operating parameters of Hall thruster
文献摘要：
The problem of determining the electron anomalous conductivity profile in a Hall thruster,when its operating parameters are known from the experiment,is considered.To solve the problem,we propose varying the parametrically set anomalous conductivity profile until the calculated operating parameters match the experimentally measured ones in the best way.The axial 1D3V hybrid model was used to calculate the operating parameters with parametrically set conductivity.Variation of the conductivity profile was performed using Bayesian optimization with a Gaussian process(machine learning method),which can resolve all local minima,even for noisy functions.The calculated solution corresponding to the measured operating parameters of a Hall thruster in the best way proved to be unique for the studied operating modes of KM-88.The local plasma parameters were calculated and compared to the measured ones for four different operating modes.The results show the qualitative agreement.An agreement between calculated and measured local parameters can be improved with a more accurate model of plasma-wall interaction.
文献关键词：
中图分类号：
作者姓名：
Andrey SHASHKOV;Mikhail TYUSHEV;Alexander LOVTSOV;Dmitry TOMILIN;Dmitrii KRAVCHENKO
作者机构：
JSC'Keldysh Research Center',8 Onezhskaya St.,Moscow 125438,Russia
文献出处：
引用格式：
[1]Andrey SHASHKOV;Mikhail TYUSHEV;Alexander LOVTSOV;Dmitry TOMILIN;Dmitrii KRAVCHENKO-.Machine learning-based method to adjust electron anomalous conductivity profile to experimentally measured operating parameters of Hall thruster)[J].等离子体科学和技术（英文版）,2022(06):148-156
A类：
thruster,1D3V
B类：
Machine,learning,method,adjust,electron,anomalous,conductivity,profile,experimentally,measured,operating,parameters,Hall,problem,determining,when,its,known,from,considered,To,propose,varying,parametrically,set,until,calculated,match,ones,best,way,axial,hybrid,model,was,used,Variation,performed,using,Bayesian,optimization,Gaussian,process,machine,which,can,resolve,local,minima,even,noisy,functions,solution,corresponding,unique,studied,modes,KM,plasma,were,compared,four,different,results,show,qualitative,agreement,An,between,improved,more,accurate,wall,interaction
AB值：
0.455696
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
