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

Artificial neural network based chemical mechanisms for computationally efficient modeling of kerosene combustion