Introducing Neutral Polymeric bonding agents(NPBA) into the Nitrate Ester Plasticized Polyether(NEPE)propellant could improve the adhesion between filler/matrix interface, thereby contributing to the development of ne...Introducing Neutral Polymeric bonding agents(NPBA) into the Nitrate Ester Plasticized Polyether(NEPE)propellant could improve the adhesion between filler/matrix interface, thereby contributing to the development of new generations of the NEPE propellant with better mechanical properties. Therefore,understanding the effects of NPBA on the deformation and damage evolution of the NEPE propellant is fundamental to material design and applications. This paper studies the uniaxial tensile and stress relaxation responses of the NEPE propellant with different amounts of NPBA. The damage evolution in terms of interface debonding is further investigated using a cohesive-zone model(CZM). Experimental results show that the initial modulus and strength of the NEPE propellant increase with the increasing amount of NPBA while the elongation decreases. Meanwhile, the relaxation rate slows down and a higher long-term equilibrium modulus is reached. Experimental and numerical analyses indicate that interface debonding and crack propagation along filler-matrix interface are the dominant damage mechanism for the samples with a low amount of NPBA, while damage localization and crack advancement through the matrix are predominant for the ones with a high amount of NPBA. Finally, crosslinking density tests and simulation results also show that the effect of the bonding agent is interfacial rather than due to the overall crosslinking density change of the binder.展开更多
固体推进剂中AP颗粒的级配不同会导致力学性能相差巨大,为了探究AP颗粒的粒度值和质量分数对端羟基聚丁二烯(HTPB)推进剂力学性能的影响规律,采用机器学习的方法对推进剂的力学性能进行仿真预测,降低了实验成本并提高了预测效率。首先,...固体推进剂中AP颗粒的级配不同会导致力学性能相差巨大,为了探究AP颗粒的粒度值和质量分数对端羟基聚丁二烯(HTPB)推进剂力学性能的影响规律,采用机器学习的方法对推进剂的力学性能进行仿真预测,降低了实验成本并提高了预测效率。首先,对不同级配的HTPB推进剂进行拉伸试验,得到不同温度下抗拉强度和伸长率;其次,以拉伸试验结果为样本进行机器学习,分别构建了反向传播(Back Propagation,BP)神经网络、粒子群算法优化的反向传播(Particle Swarm Optimization Back Propagation,PSOBP)神经网络和遗传算法优化的反向传播(Genetic Algorithms Back Propagation,GABP)神经网络对推进剂的力学性能进行预测。结果表明,力学性能与颗粒级配的内在关系较为复杂,并非简单的线性关系。PSOBP和GABP可以用于预测不同级配下HTPB推进剂力学性能,并且GABP神经网络可以更好地预测推进剂的力学性能变化。展开更多
基金National Natural Science Foundation of China(U22B20131)for supporting this project.
文摘Introducing Neutral Polymeric bonding agents(NPBA) into the Nitrate Ester Plasticized Polyether(NEPE)propellant could improve the adhesion between filler/matrix interface, thereby contributing to the development of new generations of the NEPE propellant with better mechanical properties. Therefore,understanding the effects of NPBA on the deformation and damage evolution of the NEPE propellant is fundamental to material design and applications. This paper studies the uniaxial tensile and stress relaxation responses of the NEPE propellant with different amounts of NPBA. The damage evolution in terms of interface debonding is further investigated using a cohesive-zone model(CZM). Experimental results show that the initial modulus and strength of the NEPE propellant increase with the increasing amount of NPBA while the elongation decreases. Meanwhile, the relaxation rate slows down and a higher long-term equilibrium modulus is reached. Experimental and numerical analyses indicate that interface debonding and crack propagation along filler-matrix interface are the dominant damage mechanism for the samples with a low amount of NPBA, while damage localization and crack advancement through the matrix are predominant for the ones with a high amount of NPBA. Finally, crosslinking density tests and simulation results also show that the effect of the bonding agent is interfacial rather than due to the overall crosslinking density change of the binder.
文摘固体推进剂中AP颗粒的级配不同会导致力学性能相差巨大,为了探究AP颗粒的粒度值和质量分数对端羟基聚丁二烯(HTPB)推进剂力学性能的影响规律,采用机器学习的方法对推进剂的力学性能进行仿真预测,降低了实验成本并提高了预测效率。首先,对不同级配的HTPB推进剂进行拉伸试验,得到不同温度下抗拉强度和伸长率;其次,以拉伸试验结果为样本进行机器学习,分别构建了反向传播(Back Propagation,BP)神经网络、粒子群算法优化的反向传播(Particle Swarm Optimization Back Propagation,PSOBP)神经网络和遗传算法优化的反向传播(Genetic Algorithms Back Propagation,GABP)神经网络对推进剂的力学性能进行预测。结果表明,力学性能与颗粒级配的内在关系较为复杂,并非简单的线性关系。PSOBP和GABP可以用于预测不同级配下HTPB推进剂力学性能,并且GABP神经网络可以更好地预测推进剂的力学性能变化。
