采用神经网络与遗传算法对Mg/PTFE贫氧推进剂配方的优化设计研究
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摘要
为对Mg/PTFE贫氧推进剂的配方进一步优化,本文在研究分析传统配方优化方式及存在问题的基础上,采用人工神经网络和遗传算法相结合的配方设计优化方法确定出相对较优的设计配方,并进行了试验效果检验。文章首先依据神经网络和遗传算法基本原理,分析研究了反向传播算法(BP)、广义回归神经网络(GRNN)、支持向量机(SVM)等三种不同的数据网络模型的结构和算法；接着采用均匀设计法设计了试验配方,在对试验配方的燃烧热、燃烧温度和燃速测试的基础上获得了神经网络所必需的训练数据；然后分别用三种不同的神经网络模型对试验数据进行了建模和预测,结果显示,支持向量基(SVM)网络预测误差均在10%以内,精度较高。本文以推进剂的性能预测结果为优化目标,通过遗传算法的优化得出最佳的推进剂配方为：PTFE/Mg质量比为0.491,酚醛树脂含量为12.5%,镁粉粒度为26.90μm, PTFE粒度为111.331μm,并对该设计配方进行了试验验证,结果表明,该配方的燃烧热、燃烧温度和燃速都处于较高水平,并且具有较低的感度和良好的安定性,能够满足冲压发动机对其贫氧推进剂的性能要求。
本文还使用GUIDE编程语言建立了配方优化系统的可视化用户界面,该系统具有一定的推广性,可以用于其它推进剂及复合体系烟火药的配方研究。
In order to the composition optimization of Mg/PTFE fuel rich propellant, this thesis presents a method of formula optimization by the neural networks combined with genetic algorithm after analyzing the traditional optimize methods and their shortcoming. Firstly, the algorithm and structure of three different network models——BP,GRNN and SVM were studid on the base of the basic principles of neural networks and genetic algorithm. secondly, the uniform design method was used to design the experimental formula of Mg/PTFE fuel rich propellant,and the combustion heat, combustion temperature and combustion rate were measured for the network trainning.After that,the three network models were used for the modeling and prediction of the experiment data respectively, the results showed that,the prediction error of SVM were less than10%. Then, the genetic algorithm was used to get the best formula with the propellant property predictions as optimization goal, the best formula is: the ratio of Mg to PTFE is0.491, the binder content is12.5%,the diameter of Mg is26.90μm, the diameter of PTFE is26.90μm.Next,the performance of the best formulas was tested, the results showed that their combustion heat, combustion temperature and combustion rate were all at a high level,and they also had low sensitivity and high stability which can meet the requirements of ramjet.
This thesis also built a visible user interface of the prescription optimization system by GUI which can be used in the study on propellant and other composite pyrotechnic system.
本文还使用GUIDE编程语言建立了配方优化系统的可视化用户界面,该系统具有一定的推广性,可以用于其它推进剂及复合体系烟火药的配方研究。
In order to the composition optimization of Mg/PTFE fuel rich propellant, this thesis presents a method of formula optimization by the neural networks combined with genetic algorithm after analyzing the traditional optimize methods and their shortcoming. Firstly, the algorithm and structure of three different network models——BP,GRNN and SVM were studid on the base of the basic principles of neural networks and genetic algorithm. secondly, the uniform design method was used to design the experimental formula of Mg/PTFE fuel rich propellant,and the combustion heat, combustion temperature and combustion rate were measured for the network trainning.After that,the three network models were used for the modeling and prediction of the experiment data respectively, the results showed that,the prediction error of SVM were less than10%. Then, the genetic algorithm was used to get the best formula with the propellant property predictions as optimization goal, the best formula is: the ratio of Mg to PTFE is0.491, the binder content is12.5%,the diameter of Mg is26.90μm, the diameter of PTFE is26.90μm.Next,the performance of the best formulas was tested, the results showed that their combustion heat, combustion temperature and combustion rate were all at a high level,and they also had low sensitivity and high stability which can meet the requirements of ramjet.
This thesis also built a visible user interface of the prescription optimization system by GUI which can be used in the study on propellant and other composite pyrotechnic system.
引文
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