  To effectively simulate the combustion of hydrocarbon-fueled supersonic
engines, such as rocket-based combined cycle (RBCC) engines, a detailed
mechanism for chemistry is usually required but computationally prohibitive. In
order to accelerate chemistry calculation, an artificial neural network (ANN)
based methodology was introduced in this study. This methodology consists of
two different layers: self-organizing map (SOM) and back-propagation neural
network (BPNN). The SOM is for clustering the dataset into subsets to reduce
the nonlinearity, while the BPNN is for regression for each subset. The entire
methodology was subsequently employed to establish a skeleton mechanism of
kerosene combustion with 41 species. The training data was generated by RANS
simulations of the RBCC combustion chamber, and then fed into the SOM-BPNN with
six different topologies (three different SOM topologies and two different BPNN
topologies). By comparing the predicted results of six cases with those of the
conventional ODE solver, it is found that if the topology is properly designed,
high-precision results in terms of ignition, quenching and mass fraction
prediction can be achieved. As for efficiency, 8~ 20 times speedup of the
chemical system integration was achieved, indicating that it has great
potential for application in complex chemical mechanisms for a variety of
fuels.
