等离子体与火药相互作用过程的系统辨识
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摘要
电热化学炮的内弹道性能依赖于电能和火药化学能的有效利用率,而等离子体与火药的作用规律是影响电热化学发射中能量利用效率的关键因素。基于此情况,本文以密闭爆发器内等离子体与火药相互作用过程中的电功率和压力之间的关系以及压力和燃速之间的关系作为研究对象,并建立它们的关系辨识模型,根据这些模型可以由电功率预测出压力、再由压力预测出燃速。这些研究为等离子体与火药相互作用的机理研究提供了一种研究手段。
本文的主要内容如下:
(1)应用小波变换方法进行信号除噪。因为密闭爆发器实验信号中的噪声和燃烧现象造成了数据抖动,利用传统的滤波方法对所测的信号进行除噪效果不理想。而本文根据小波理论对所测的信号进行时频分解,并通过高频滤波滤掉了耦合在信号中的噪声
(2)利用神经网络方法,建立了等离子体与火药相互作用过程的“电功率-压力”辨识模型。该模型经过网络训练和测试后所得的最终模型的输出结果与实测的数据误差很小,说明建立的模型可以准确地对压力信号进行预测。
(3)利用小波神经网络方法结合燃速计算公式建立“压力-燃速”辨识模型。该模型仿真后得到的结果基本符合实际情况。该模型可用于压力与燃速之间规律的分析,并可以指导以后的实验。
The interior ballistic performance of the electrothermal-chemical gun depends on effective utilization of the electrical energy and chemical energy. The interaction of plasma with propellant is the pivotal factor that influences energy efficiency of electrothermal chemical launch. Under this condition, the paper takes the relationship of electric power -pressure and the relationship of pressure- burning rate as researching objects, and establishes their relations identification models. The Models can predict pressure according to electric power, and then predict the burning rate. The researches provides a new research method for improving the ballistic performance.
The main contents are as follows:
Firstly, the knowledge of the wavelet transform is used for denoising. While measuring the signals in the closed bomb, it will make the data dithering owing to the noises and combustion phenomena. The paper does the decomposition of time-frequency signals according to the theory of wavelet and filters out high-frequency signals in the coupling of the useful signals.
Secondly, the paper establishes the identification model of "electric power-pressure" in Plasma-Propellant interactions process using wavelet neural network of knowledge. After the network training and testing, the error between the final model output and the actual data is very small, which proves that the final model can accurately predict the pressure signal.
Finally, the paper establishes the identification model of "pressure - the burning rate" based on the burning rate formula and exerting the knowledge of wavelet neural network. The simulated results are in accord with the actual situation basically, which proves that we can master the law of electric power-pressure and the pressure-burning rate. Moreover, the research results can guide the future researches and reduce times of the experiments.
本文的主要内容如下:
(1)应用小波变换方法进行信号除噪。因为密闭爆发器实验信号中的噪声和燃烧现象造成了数据抖动,利用传统的滤波方法对所测的信号进行除噪效果不理想。而本文根据小波理论对所测的信号进行时频分解,并通过高频滤波滤掉了耦合在信号中的噪声
(2)利用神经网络方法,建立了等离子体与火药相互作用过程的“电功率-压力”辨识模型。该模型经过网络训练和测试后所得的最终模型的输出结果与实测的数据误差很小,说明建立的模型可以准确地对压力信号进行预测。
(3)利用小波神经网络方法结合燃速计算公式建立“压力-燃速”辨识模型。该模型仿真后得到的结果基本符合实际情况。该模型可用于压力与燃速之间规律的分析,并可以指导以后的实验。
The interior ballistic performance of the electrothermal-chemical gun depends on effective utilization of the electrical energy and chemical energy. The interaction of plasma with propellant is the pivotal factor that influences energy efficiency of electrothermal chemical launch. Under this condition, the paper takes the relationship of electric power -pressure and the relationship of pressure- burning rate as researching objects, and establishes their relations identification models. The Models can predict pressure according to electric power, and then predict the burning rate. The researches provides a new research method for improving the ballistic performance.
The main contents are as follows:
Firstly, the knowledge of the wavelet transform is used for denoising. While measuring the signals in the closed bomb, it will make the data dithering owing to the noises and combustion phenomena. The paper does the decomposition of time-frequency signals according to the theory of wavelet and filters out high-frequency signals in the coupling of the useful signals.
Secondly, the paper establishes the identification model of "electric power-pressure" in Plasma-Propellant interactions process using wavelet neural network of knowledge. After the network training and testing, the error between the final model output and the actual data is very small, which proves that the final model can accurately predict the pressure signal.
Finally, the paper establishes the identification model of "pressure - the burning rate" based on the burning rate formula and exerting the knowledge of wavelet neural network. The simulated results are in accord with the actual situation basically, which proves that we can master the law of electric power-pressure and the pressure-burning rate. Moreover, the research results can guide the future researches and reduce times of the experiments.
引文
[1]谢玉树.等离子体与火药相互作用的研究.南京理工大学博士学位论文,2005,7
[2]G.P.Wren,W.F.Oberle,W.F.Morrison.Utilization of Electrical Energy to Enhance Performance of Solid Propellant Guns,ARL-TR-16,1992
[3]K.J.White,G.L.Katulka.Electrothermal Chemical plasm interaction with propellant.Proceedings of the 16thlnternational Symposium on Ballistics,1996(1):387- 396
[4]Bourham M A,Gilligan J G,Oberle W F.Analysis of Solid Propellant Combustion Behavior under Electrothermal Plasma Injection for Electrothermal Chemical Launchers.IEEE Transaction in magneties,VOL.33,NO.1,Jan.1997:278-283[5]C.M.Edwards.Experimental Studies of the Plasma-Propellant Interface for Electrothermal-Chemical Launchers.IEEE Transaction on Magnetics,1995,31(1):404-409
[5]C.M.Edwards.Experimental Studies of the Plasma-Propellant Interface for Electrothermal-Chemical Launchers.IEEE Transaction on Magnetics.1995,31(1):404-409
[6]Haugh D C,Gilbert S.U.K.Electric Gun National Overview.IEEE Transaction on Magnetics,2003,39(1):18-21
[7]R.A.Fifer,E.S.Sagan,and R.A.Beyer.Chemical Effects in Plasma Ignition.IEEE Magnetics,2003,39(1):218-222
[8]张小兵,袁亚雄,谢玉树.等离子体点火密闭爆发器实验研究.火炸药学报,2003,26(2):26-31
[9]曾凡锋,蔡自兴.基于小波神经网络的非线性动态系统辨识.中南工业大学报,1998,29(3):280-283
[10]宋执环.小波分析理论在工业过程辨识与控制中应用研究.浙江大学博士学位论文,1997,1
[11]王影.BP 神经网络在股市预测中的应用与研究.成都电子科技大学硕士学位论文,2006,4
[12]谢华.小波神经网络在非线性系统辨识中的应用.华北电力大学硕士学位论文,2005,12
[13]历隽泽,许晓鸣,杨煜普.基于神经网络的智能控制.化工自动化及仪表.1995,22(3):53-59
[14]张欣.人工神经网络和遗传算法在微波设计中的应用.南京理工大学硕士学位论 文,2003,3
[15]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001
[16]周开利.神经网络模型及MATLAB仿真程序设计.北京:清华大学出版社,2004
[17]Ackley D.H,Hinton G.F,Sejnowski T.J.A leaning algorithm for Boltzman machines.Cognitive Science,1992,9:147-166
[18]Hagan T,Demuth H,Beale M.Neural Network Design,Boston,MA:PWS Publishing,1996
[19]Scarselli,F.and A.C.Tsoi,Universal Approximation using Feedforward Neural Networks:A Survey of Some Existing Methods,and Some New Results,Neural Nenvorks,1998,11:15-37
[20]Homik,K.,M.Stinchcombe,and H.White,Multilayer Feedforward Networks are UniversalApproximators,Neural Networks,1989,2:359-366
[21]胡田田.小波神经网络及其在软测量建模中的应用.华东理工大学硕士学位论文,2006,12
[22]汤笑笑,李介谷.基于小波神经网络的系统辨识方法.信息与控制,1998,27(4):277-281
[23]杨可苹.基于小波神经网络的高炉炉温预报模型研究.浙江大学自动化学报,2006.5
[24]吕朝霞,吴晓蓓等.用于函数逼近的小波网络.系统工程与电子技术,2000,22(5):43-45
[25]Delyon B,Juditsky A,Benveniste A.Accuracy Analysis for wavelet Approximations.IEEE Trans on NeuralNetwork,1995,6(2):332-348
[26]Pati Y C,Krishnaprasad P S.Analysis and synthesis of Feedfoard Neural Network Using Discrete Affine Wavelet Transformations.IEEE Trans on Neural Network,1993,4(1):73-85
[27]高平,薛桂玉.基于小波神经网络的大坝变形监测模型与预报.水利学报,2003,7:107-11
[28]林庆华,栗保明.密闭爆发器压力信号的时频分析与特征提取.火工品,2007,2:22-25
[29]张艳丽,元慧,陈振生.小波变换在ECG信号滤波中的应用研究.中国医学物理学杂志,2005,(1):38.
[30]钱敏平,龚光鲁.随机过程论.北京:北京大学出版社,2000
[31]田景文,高美娟.人工神经网络算法研究及应用.北京:北京理工大学出版社,2006
[32]杨德.简述非线性系统的神经网络辨识.运城学院学报,2005,2(23):48-49
[33]李海元,栗保明,李鸿志.等离子体点火密闭爆发器中火药燃速特性的研究[J].爆炸与冲击,2004,24(2):145-150
[34]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE transaction on pattern analysis and machine intelligence,1989,11(7):674-693
[35]周浩敏.信号处理技术基础[M].北京:北京航空航天大学出版社,2001:257-258
[36]Bakshi,B.R.and G.Stephanopoulos,"Wavelet as basis function for Localized learning in a multi-resclution hierarchy".Proceedings of IEEE intemation Joint Conference on Neural Networks,1992,pp.140-145
[37]葛哲学,孙志强.神经网络理论与MATLAB R2007实现.北京:电子工业出版社,2007
[38]闻新等.MATLAB神经网络应用设计.北京:科学出版社,2005
[39]金志明,袁亚雄,宋明.现代内弹道学[M].北京:北京理工大学出版社,1992
[40]韩力群.人工神经网络教程.北京:北京邮电大学出版社,2006
[41]董长虹.Matlab小波分析工具箱原理与应用.北京:国防工业出版社,2004
[42]董长虹.Malab神经网络与应用.北京:国防工业出版社,2004
[43]朱虹,于向军,吕震中.基于小波变化的压力脉动信号除噪.噪声与振动控制,2006,2:46-47
[44]匡胤.基于人工神经网络的系统建模及MATLAB实现.四川理工学院学报(自然科学版),2007,20(5):6-9
[45]成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004,9
[46]张岳新.Visual C++程序设计.苏州:苏州大学出版社,2002
[2]G.P.Wren,W.F.Oberle,W.F.Morrison.Utilization of Electrical Energy to Enhance Performance of Solid Propellant Guns,ARL-TR-16,1992
[3]K.J.White,G.L.Katulka.Electrothermal Chemical plasm interaction with propellant.Proceedings of the 16thlnternational Symposium on Ballistics,1996(1):387- 396
[4]Bourham M A,Gilligan J G,Oberle W F.Analysis of Solid Propellant Combustion Behavior under Electrothermal Plasma Injection for Electrothermal Chemical Launchers.IEEE Transaction in magneties,VOL.33,NO.1,Jan.1997:278-283[5]C.M.Edwards.Experimental Studies of the Plasma-Propellant Interface for Electrothermal-Chemical Launchers.IEEE Transaction on Magnetics,1995,31(1):404-409
[5]C.M.Edwards.Experimental Studies of the Plasma-Propellant Interface for Electrothermal-Chemical Launchers.IEEE Transaction on Magnetics.1995,31(1):404-409
[6]Haugh D C,Gilbert S.U.K.Electric Gun National Overview.IEEE Transaction on Magnetics,2003,39(1):18-21
[7]R.A.Fifer,E.S.Sagan,and R.A.Beyer.Chemical Effects in Plasma Ignition.IEEE Magnetics,2003,39(1):218-222
[8]张小兵,袁亚雄,谢玉树.等离子体点火密闭爆发器实验研究.火炸药学报,2003,26(2):26-31
[9]曾凡锋,蔡自兴.基于小波神经网络的非线性动态系统辨识.中南工业大学报,1998,29(3):280-283
[10]宋执环.小波分析理论在工业过程辨识与控制中应用研究.浙江大学博士学位论文,1997,1
[11]王影.BP 神经网络在股市预测中的应用与研究.成都电子科技大学硕士学位论文,2006,4
[12]谢华.小波神经网络在非线性系统辨识中的应用.华北电力大学硕士学位论文,2005,12
[13]历隽泽,许晓鸣,杨煜普.基于神经网络的智能控制.化工自动化及仪表.1995,22(3):53-59
[14]张欣.人工神经网络和遗传算法在微波设计中的应用.南京理工大学硕士学位论 文,2003,3
[15]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001
[16]周开利.神经网络模型及MATLAB仿真程序设计.北京:清华大学出版社,2004
[17]Ackley D.H,Hinton G.F,Sejnowski T.J.A leaning algorithm for Boltzman machines.Cognitive Science,1992,9:147-166
[18]Hagan T,Demuth H,Beale M.Neural Network Design,Boston,MA:PWS Publishing,1996
[19]Scarselli,F.and A.C.Tsoi,Universal Approximation using Feedforward Neural Networks:A Survey of Some Existing Methods,and Some New Results,Neural Nenvorks,1998,11:15-37
[20]Homik,K.,M.Stinchcombe,and H.White,Multilayer Feedforward Networks are UniversalApproximators,Neural Networks,1989,2:359-366
[21]胡田田.小波神经网络及其在软测量建模中的应用.华东理工大学硕士学位论文,2006,12
[22]汤笑笑,李介谷.基于小波神经网络的系统辨识方法.信息与控制,1998,27(4):277-281
[23]杨可苹.基于小波神经网络的高炉炉温预报模型研究.浙江大学自动化学报,2006.5
[24]吕朝霞,吴晓蓓等.用于函数逼近的小波网络.系统工程与电子技术,2000,22(5):43-45
[25]Delyon B,Juditsky A,Benveniste A.Accuracy Analysis for wavelet Approximations.IEEE Trans on NeuralNetwork,1995,6(2):332-348
[26]Pati Y C,Krishnaprasad P S.Analysis and synthesis of Feedfoard Neural Network Using Discrete Affine Wavelet Transformations.IEEE Trans on Neural Network,1993,4(1):73-85
[27]高平,薛桂玉.基于小波神经网络的大坝变形监测模型与预报.水利学报,2003,7:107-11
[28]林庆华,栗保明.密闭爆发器压力信号的时频分析与特征提取.火工品,2007,2:22-25
[29]张艳丽,元慧,陈振生.小波变换在ECG信号滤波中的应用研究.中国医学物理学杂志,2005,(1):38.
[30]钱敏平,龚光鲁.随机过程论.北京:北京大学出版社,2000
[31]田景文,高美娟.人工神经网络算法研究及应用.北京:北京理工大学出版社,2006
[32]杨德.简述非线性系统的神经网络辨识.运城学院学报,2005,2(23):48-49
[33]李海元,栗保明,李鸿志.等离子体点火密闭爆发器中火药燃速特性的研究[J].爆炸与冲击,2004,24(2):145-150
[34]Mallat S.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE transaction on pattern analysis and machine intelligence,1989,11(7):674-693
[35]周浩敏.信号处理技术基础[M].北京:北京航空航天大学出版社,2001:257-258
[36]Bakshi,B.R.and G.Stephanopoulos,"Wavelet as basis function for Localized learning in a multi-resclution hierarchy".Proceedings of IEEE intemation Joint Conference on Neural Networks,1992,pp.140-145
[37]葛哲学,孙志强.神经网络理论与MATLAB R2007实现.北京:电子工业出版社,2007
[38]闻新等.MATLAB神经网络应用设计.北京:科学出版社,2005
[39]金志明,袁亚雄,宋明.现代内弹道学[M].北京:北京理工大学出版社,1992
[40]韩力群.人工神经网络教程.北京:北京邮电大学出版社,2006
[41]董长虹.Matlab小波分析工具箱原理与应用.北京:国防工业出版社,2004
[42]董长虹.Malab神经网络与应用.北京:国防工业出版社,2004
[43]朱虹,于向军,吕震中.基于小波变化的压力脉动信号除噪.噪声与振动控制,2006,2:46-47
[44]匡胤.基于人工神经网络的系统建模及MATLAB实现.四川理工学院学报(自然科学版),2007,20(5):6-9
[45]成礼智,王红霞,罗永.小波的理论与应用.北京:科学出版社,2004,9
[46]张岳新.Visual C++程序设计.苏州:苏州大学出版社,2002
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