Abstract
Developing advanced microstructured energetic composites provides an effective strategy for regulating combustion behavior and improving the energy utilization efficiency of aluminized solid propellants. Through tailored microstructural assembly, this advanced interface engineering not only mitigates aluminum agglomeration to boost the combustion efficiency of metallic fuels, but also effectively desensitizes high-energy explosives. In this study, large-scale molecular dynamics simulations driven by a high-fidelity neural network potential were employed to investigate the combustion dynamics of two distinct microunit architectures: Al-core/RDX-shell (Al@RDX) and RDX-core/Al-shell (RDX@Al). Particular emphasis was placed on clarifying how interfacial topology influences local decomposition behavior, heat release, and pressure evolution at the nanoscale. Under condensed-phase conditions, Al@RDX exhibits rapid initial heat release due to fast RDX decomposition. However, decomposition fragments can diffuse away from the Al interface, limiting sustained interfacial reactions. In contrast, the confined RDX@Al structure initially suppresses decomposition, but gradually enhances interfacial reactions through the accumulation of reactive fragments near the surrounding Al shell, leading to stronger interfacial coupling and accelerated Al consumption. Vacuum simulations further show that RDX@Al maintains stronger interfacial reactivity under pressure-release conditions, indicating lower sensitivity to pressure fluctuations These findings establish a qualitative relationship between interfacial topology and localized combustion behavior, providing mechanistic guidance for the design of high performance aluminized composite propellants.
| Original language | English |
|---|---|
| Article number | 178363 |
| Journal | Chemical Engineering Journal |
| Volume | 543 |
| DOIs | |
| Publication status | Published - 1 Sept 2026 |
| Externally published | Yes |
Keywords
- Combustion
- Interface engineering
- Microunit composite
- Neural network potential
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