A neural network potential energy surface assisted molecular dynamics study on the pyrolysis behavior of two spiro-hydrocarbons

Spiro-hydrocarbons are potentially a type of novel alternative jet fuel due to their high density and net heat of combustion. In this work, the pyrolysis study of two spiro-hydrocarbons (spiro[cyclopropane-1,6 '-tricyclo[3.2.1.02,4]octane] (C10H14) as Fuel 1 and spiro[bicyclo[2.2.1]heptane-2,1 '-cyclopropane] (C9H14) as Fuel 2) is performed via molecular dynamics (MD) simulations, with a neural network potential energy surface (NNPES), deep potential (DP) model, adopted. The data set for the DP model of each fuel is constructed after 31 and 27 iterations, respectively. The high precision of the DP model is demonstrated, and the temperature transferability of each model is observed. The overall pyrolysis performance is evaluated with the fuel decomposition rate, showing that both fuels have comparable gas-reactivity to commercial aviation fuels, such as JP-10. The reaction networks of initial pyrolysis for Fuels 1 and 2 are constructed, and the contribution of each pathway is discussed. Fuel 1 tends to form an unsaturated six-membered ring structure, while Fuel 2 generates unsaturated open-chain hydrocarbons. Further analyses of the MD results provide time-evolution information on each component in the pyrolysis species pool. Compared to Fuel 1, the initial pyrolysis of Fuel 2 leads to more hydrogen, alkenes, and alkanes, as well as fewer monocyclic aromatic hydrocarbons (MAHs), demonstrating a reduced tendency for afterward coking. This work might contribute to the development of the mechanism of the two spiro-hydrocarbons and guide the research of other similar structural fuels. Neural network molecular dynamics research reveals that the molecular structure of spiro-polycyclic hydrocarbon fuels determines the initial pyrolysis reactions as well as the subsequent combustion performance and coking behavior.