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

被引：0
|
作者：
Chu Q.-Z. [1 ]
Fu X.-L. [2 ]
Zheng X.-M. [3 ]
Liu J.-L. [3 ]
Chen D.-P. [1 ]
机构：
[1] State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing
[2] Xi'an Modern Chemistry Research Institute, Xi an
[3] Helongjiang North Tool Co. Ltd., Heilongjiang, Mudanjiang
来源：
Huozhayao Xuebao/Chinese Journal of Explosives and Propellants | 2024年 / 47卷 / 03期
关键词：
comb; HTPB propel lant; machine learning potential; moleojlar dynamics; neural network model; physical chemistry; stion property;
D O I：
10.14077/j.issn.1007-7812.202308001
中图分类号：
学科分类号：
摘要：
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. © 2024 China Ordnance Industry Corporation. All rights reserved.
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页码：254 / 261
页数：7
相关论文
共 23 条
  • [11] CHU Q, CHANG X, CHEN D., A physiochemical model for the combustion of aluminum nano-agglomerates in highspeed flows, Combustion and Flame, 237, (2021)
  • [12] KANG J, HAN B., First-principles characterization of the unknown crystal structure and ionic conductivity of Li, P: Sr, I as a solid electrolyte for high-voltage Li ion batteries, The Journal of Physical Chemistry Letters, 7, 14, pp. 2671-2675, (2016)
  • [13] YEH I C, ANDZELM JW., Computational study of structural and energetic properties of ammonium perchlorate at interfaces, The Journal of Physical Chemistry C, 125, 22, pp. 12297-12304, (2021)
  • [14] VAN DUIN ACT, DASGUPTA S, LORANT F, Et al., ReaxFF: A reactive force field for hydrocarbons, The Journal of Physical Chemistry A, 105, 41, pp. 9396-9409, (2001)
  • [15] WANG N, PENG J, PANG A, Et al., Thermodynamic simulation of the RDX-aluminum interface using ReaxFF molecular dynamics, The Journal of Physical Chemistry C, 121, 27, pp. 14597-14610, (2017)
  • [16] CHU Q, WEN M, FU X, Et al., Reaction network of ammonium perchlorate (AP) decomposition: The missing piece from atomic simulations, The Journal of Physical Chemistry C, 127, 27, pp. 12976-12982, (2023)
  • [17] WANG H, ZHANG L, HAN J., DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics, Computer Physics Communications, 228, pp. 178-184, (2018)
  • [18] KUHNE T D, IANNUZZI M, DEL BEN M, Et al., CP2K: An electronic structure and molecular dynamics software package-quickstep : Efficient and accurate electronic structure calculations, Journal of Chemical Physics, 152, 19, (2020)
  • [19] CHANG X, CHU Q, CHEN D., Monitoring the melting behavior of boron nanoparticles using a neural network potential, Physical Chemistry Chemical Physics, 25, 18, pp. 12841-12853, (2023)
  • [20] CHU Q, LUO K H, CHEN D., Exploring complex reaction networks using neural network-based molecular dynamics simulation, Journal of Physical Chemistry Letters, 13, pp. 4052-4057, (2022)
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