TY - JOUR
T1 - Fully elucidating catalyst-driven combustion mechanisms in double-base propellants through molecular dynamics simulations
AU - Wen, Mingjie
AU - Han, Jiahe
AU - Zhang, Xiaohong
AU - Zhao, Yu
AU - Zhang, Yan
AU - Chen, Dongping
AU - Chu, Qingzhao
N1 - Publisher Copyright:
© 2026 Elsevier Ltd.
PY - 2027/1/1
Y1 - 2027/1/1
N2 - 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.
AB - 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.
KW - Combustion behavior
KW - Double-base propellant (DBP)
KW - Molecular dynamics (MD) simulation
KW - Neural network potential (NNP)
KW - Temperature effect
UR - https://www.scopus.com/pages/publications/105038787409
U2 - 10.1016/j.fuel.2026.139841
DO - 10.1016/j.fuel.2026.139841
M3 - Article
AN - SCOPUS:105038787409
SN - 0016-2361
VL - 427
JO - Fuel
JF - Fuel
M1 - 139841
ER -