气相和粒子冲刷速度对丁羟推进剂燃速影响分析

基于试验获得的气相和粒子冲刷条件下的固体推进剂燃速数据,采用误差反向传播算法(BP算法)的人工神经网络技术开展了推进剂的燃速特性分析。网络训练和预示结果表明,利用BP算法开展冲刷条件下的燃速影响因素分析的精度在4%以内。分析结果表明,气相和粒子冲刷速度都会影响固体推进剂的燃速。在低气相速度条件下,推进剂燃速对粒子冲刷速度的变化更为敏感。粒子冲刷对固体推进剂燃速的影响存在界限效应,当粒子冲刷速度大于某一界限值时,推进剂燃速增加幅度增大,并由粒子冲刷主导。

Gas and particle velocity impact on the burning rate of HTPB solid propellant

Based on the experimental burning data under gas and particle erosion,Artificial Neural Network technique using BP arithmetic was used to study analysis the burning rate property of HTPB solid propellant.Training and prediction results show that the error of BP arithmeticis less than 4%. The results indicate that the burning rate of HTPB propellant can be affected by both gas phase and particle velocity.Burning rate is sensitive to particle velocity at lower gas phase velocity.There exists a threshold of particle velocity.If particle velocity exceeds this value,burning rate of solid propellant is dominant by particle velocity and it increases dramatically.