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2023
DOI: 10.1039/d2ya00285j
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Recent ReaxFF MD studies on pyrolysis and combustion mechanisms of aviation/aerospace fuels and energetic additives

Abstract: Development of aviation and aerospace fuels requires deep insight into the pyrolysis and combustion mechanisms. However, rapid and complex reactions during fuel combustion make it difficult to accurately describe the...

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Cited by 6 publications
(3 citation statements)
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References 144 publications
(252 reference statements)
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“…ReaxFF MD can be performed several orders of magnitude faster in calculating energies and forces compared to quantum mechanical methods (QM) with close accuracy to the widely used DFT method. 8,9 Particularly, the significant speed-up of ReaxFF MD simulations allowed by the GPU-enabled code of GMD-Reax 10 or other GPU-based code 11 makes it practical to directly perform simulations of the pyrolysis reactions of fuel mixture models with dozens of high carbon number components consisting of n -paraffins, iso-paraffins, cycloparaffins, alkenes, and aromatics. 12–14 The reactions containing full information of reaction sites analyzed with the aid of VARxMD 15 from simulation trajectories demonstrate the advantage of ReaxFF MD in obtaining the dynamic evolution of the complex radical-driven reactions in real fuel pyrolysis, of which applications 2,16 can be found in pyrolysis simulations of many large systems like coal, biomass, polymers, energetics materials, hydrocarbon fuel, etc.…”
Section: Introductionmentioning
confidence: 99%
“…ReaxFF MD can be performed several orders of magnitude faster in calculating energies and forces compared to quantum mechanical methods (QM) with close accuracy to the widely used DFT method. 8,9 Particularly, the significant speed-up of ReaxFF MD simulations allowed by the GPU-enabled code of GMD-Reax 10 or other GPU-based code 11 makes it practical to directly perform simulations of the pyrolysis reactions of fuel mixture models with dozens of high carbon number components consisting of n -paraffins, iso-paraffins, cycloparaffins, alkenes, and aromatics. 12–14 The reactions containing full information of reaction sites analyzed with the aid of VARxMD 15 from simulation trajectories demonstrate the advantage of ReaxFF MD in obtaining the dynamic evolution of the complex radical-driven reactions in real fuel pyrolysis, of which applications 2,16 can be found in pyrolysis simulations of many large systems like coal, biomass, polymers, energetics materials, hydrocarbon fuel, etc.…”
Section: Introductionmentioning
confidence: 99%
“…The reactive molecular dynamics using the first-principles-based ReaxFF force field , (ReaxFF MD) is a promising method to obtain global reactions in fuel pyrolysis. Particularly, large-scale ReaxFF MD simulations are the effective approach in obtaining a more complete reaction scenario from source fuel molecules or starting reactant molecules to important intermediates and final product yields for the pyrolysis of the realistic fuel system. , …”
Section: Introductionmentioning
confidence: 99%
“…According to the present understanding, soot formation occurs by a series of complex physicochemical events such as the formation of gas-phase soot precursors (including, but not limited to, polycyclic aromatic hydrocarbons or PAHs), nucleation of incipient soot particles, growth and maturation of incipient soot particles due to surface reactions, aggregation by coagulation or coalescence, and decay of the particles by fragmentation and oxidation. The inception of soot particles is arguably the least understood phenomenon among these processes, and the exact chemical reaction pathways of soot inception are not completely known yet. Researchers agree that soot formation starts with production of small gas-phase precursor molecules such as acetylene, which leads to PAHs like benzene, pyrene, and coronene. These freshly formed PAHs then combine to form the solid or liquid-like incipient soot particles. These particles then start to grow by surface reactions and coalescence to form larger soot particles. , …”
Section: Introductionmentioning
confidence: 99%