HTPB/AI/AP/RDX推进剂初始燃烧的分子模拟

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 propel lant combustion surface model was established, and a large-scale moleojlar dynamics simulation was conducted to systematically analyze the spatiotemporal evohjtion of microstructure, temperatureu, and reaction components during propel lant 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 propel lant 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.