NEPE推进剂无损型贮存寿命预估

Non-destructive Storage Life Prediction of NEPE Propellant

为了实现对硝酸酯增塑聚醚(NEPE)推进剂药柱贮存寿命进行预估时的测量无损性,通过对施加10%定压缩应变的NEPE推进剂进行高温热加速老化实验、气体含量监测实验、单向拉伸力学性能实验,基于相关性分析和寿命预估模型,提出了一种以特征气体含量变化为基础数据的无损型寿命预估模型。结果表明,NEPE推进剂贮存老化过程中,CO气体释放量最大,不同温度条件下的释放量均达到1300 mg以上,且其和NO气体均呈现老化初期释放量增长缓慢,后期迅速增加的规律,HCl气体释放量在老化初期和后期增长较快,老化中期增长较慢;老化初期最大抗拉强度σm和最大延伸率ε_(m)小幅增大,老化中期前者小幅震荡,后者逐渐增大,老化后期两者均急剧减小;不同温度条件下CO气体释放量与最大抗拉强度关联度值最大,为0.93~0.95,且两者存在单一相关性;基于传统老化寿命预估模型和改进的老化寿命预估模型,建立了四种NEPE推进剂寿命预估方法,通过相关性系数比较和预估结果分析,得出以CO气体释放量作为预估参数的改进型寿命预估模型的相关性系数最大,寿命预估结果最为有效。

In order to realize the non-destructive measurement when predicting the storage life of nitrate ester plasticized polyether(NEPE)propellant,the high temperature accelerated aging,gas content monitoring and uniaxial tensile mechanical property experiment were carried out on NEPE propellant with 10% constant compression strain. The non-destructive storage life prediction model based on characteristic gas contents was proposed through correlation analysis and remaining life prediction model. The results show that during the storage and aging processes,the total amount of CO gas is the largest,reaching more than1300 mg at different temperatures. The generating rates of NO and CO are growing slowly in the early aging period,and growing faster in the late period. The generating rate of HCl increases rapidly during the eraly and late aging period and slowly in the middle. Maximum tensile strength σmand maximum elongation ε_(m)increase slightly in the early aging period,the former oscillates slightly and the latter gradually increases in the middle period,and both of them decrease sharply in the late period. The correlation between the contents of CO and the maximum tensile strength is largest and there is a single correlation between them at different temperatures. The maximum correlation value reaches about 0.93-0.95. Four life prediction methods of NEPE propellant are established based on traditional and improved aging life prediction model,tensile strength and CO content. The maximum correlation coefficient and estimation results show that the improved prediction model based on the content of CO gas release is most effective.