A machine learning potential function was developed using a deep neural network model based on a first principles calculation dataset for the key component of a four component HTPB propellant(HTPB/Al/AP/RDX).Based on the newly de-veloped potential function,a four component HTPB propellant combustion surface model was established,and a large-scale molecular dynamics simulation was conducted to systematically analyze the spatiotemporal evolution of microstructure,temper-ature,and reaction components during propellant combustion.The results show that the newly developed potential function can accurately describe the energy and force characteristics of the propellant components and the interface between them,and is a high-precision and high-efficiency machine learning potential function;The combustion surface model accurately simulates the pyrolysis process of AP,RDX,and HTPB during propellant combustion,elucidates the formation mechanism of diffusion flames and the microscopic process of aluminum powder peeling off from the combustion surface,and reveals the interaction mecha-nism of each component interface.This indicates that molecular dynamics simulation can achieve time-resolved three-dimen-sional reconstruction at the atomic scale,thereby obtaining the microscopic mechanism of propellant combustion,providing a new tool for the theoretical research of solid propellants.
关键词
物理化学/HTPB推进剂/燃烧性能/机器学习势函数/分子动力学/神经网络模型
Key words
physical chemistry/HTPB propellant/combustion property/machine learning potential/molecular dynamics/neu-ral network model
维普
万方数据