HTPB/Al/AP/RDX推进剂初始燃烧的分子模拟

Molecular Simulations of HTPB/Al/AP/RDX Propellants Combustion

针对一种四组元HTPB推进剂(HTPB/Al/AP/RDX)关键组分,基于第一性原理计算的数据集,使用深度神经网络模型开发了一个机器学习势函数;基于新开发的势函数,建立了四组元HTPB推进剂燃面模型,并进行了大规模的分子动力学模拟计算,对推进剂燃烧时的微观结构、温度、反应组分的时空演化进行了系统统计分析。结果表明,新开发的势函数能够准确描述推进剂组分单质及两两之间界面的能量和受力特性,是一个高精度、高效率的机器学习势函数;燃面模型实现了对推进剂燃烧过程中的AP、RDX、HTPB热解过程精准模拟,阐明了扩散火焰的形成机理以及铝粉从燃面剥离等微观过程,揭示了其中的各组分界面相互作用机制。表明分子动力学模拟能够在原子尺度上实现时间分辨的三维重建,进而获得推进剂燃烧的微观机理,可为固体推进剂的理论研究提供了新的工具。

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 developed 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,temperature,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 mechanism of each component interface.This indicates that molecular dynamics simulation can achieve time-resolved three-dimensional reconstruction at the atomic scale,thereby obtaining the microscopic mechanism of propellant combustion,providing a new tool for the theoretical research of solid propellants.