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Fully elucidating catalyst-driven combustion mechanisms in double-base propellants through molecular dynamics simulations

  • Xi'an Modern Chemistry Research Institute
  • Beijing Institute of Technology
  • China North Industries Group Corporation

Research output: Contribution to journalArticlepeer-review

Abstract

The combustion performance of double-base propellants (DBPs) is crucial for ensuring their safe and efficient application in various propulsion systems. However, accurately predicting the combustion behavior, particularly the impact of catalytic effects under varying temperature and pressure conditions, remains a significant challenge due to the complexity of the involved microscopic mechanisms. This study introduces a novel approach to address these challenges by high-precision large-scale molecular dynamics (MD) simulations. First, a reactive neural network potential (NNP) is developed by integrating deep learning techniques with density functional theory (DFT) calculations, providing high-precision force fields for the combustion process of DBPs. This NNP model is capable of accurately predicting the energy and forces involved in reactions, overcoming the limitations of traditional methods in capturing combustion mechanisms at the microscopic level. Second, large-scale MD simulations, based on the machine learning potential, are conducted to model the combustion process of DBPs under extreme conditions, particularly focusing on the dynamic behavior of the flame front. The simulation framework demonstrates both accuracy and efficiency, offering a novel computational approach for predicting propellant combustion performance. Finally, the study reveals the catalytic effects on the combustion process by systematically investigating how catalysts regulate the reaction rate under various temperature and pressure conditions. The results show that catalysts significantly influence the thermal decomposition pathways and reaction kinetics, providing new theoretical insights into the catalytic reaction mechanisms of propellant combustion. This research offers a comprehensive and efficient framework for simulating and understanding the combustion of DBPs, contributing to the design and optimization of propellants.

Original languageEnglish
Article number139841
JournalFuel
Volume427
DOIs
Publication statusPublished - 1 Jan 2027
Externally publishedYes

Keywords

  • Combustion behavior
  • Double-base propellant (DBP)
  • Molecular dynamics (MD) simulation
  • Neural network potential (NNP)
  • Temperature effect

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