2020/03/01 by Jian An, An, Jian, Guo Qiang He +7
Chemical Engineering · Engineering · #Advanced Combustion Engine Technologies #Combustion and flame dynamics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Heat transfer and supercritical fluids
paper · pdf · doi:10.48550/arxiv.2003.00428
openalex publication_date 2020/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.