Large eddy simulation of spray combustion using flamelet generated manifolds combined with artificial neural networks

In the present work, artificial neural networks (ANN) technique combined with flamelet generated manifolds (FGM) is proposed to mitigate the memory issue of FGM models. A set of ANN models is firstly trained using a 68-species mass fractions in mixture fraction-progress variable space. The ANN prediction accuracy is examined in large eddy simulation (LES) and Reynolds averaged Navier-Stokes (RANS) simulations of spray combustion. It is shown that the present ANN models can properly replicate the FGM table for most of the species mass fractions. The network models with relative error less than 5% are considered in RANS and LES to simulate the Engine Combustion Network (ECN) Spray H flames. Validation of the method is firstly conducted in the framework of RANS. Both non-reacting and reacting cases show the present method predicts very well the trend of spray and combustion process under different ambient temperatures. The results show that FGM-ANN can replicate the ignition delay time (IDT) and lift-off length (LOL) precisely as the conventional FGM method, and the results agree very well with the experiments. With the help of ANN, it is possible to achieve high efficiency and accuracy, with a significantly reduced memory requirement of the FGM models. LES with FGM-ANN is then applied to explore the detailed spray combustion process. Chemical explosive mode analysis (CEMA) approach is used to identify the local combustion modes. It is found that before the spray flame is developed to the steady-state, the high CH2O zone is always associated with ignition mode. However, high CH2O zone together with high OH zone is dominated by the burned mode after the steady-state. The lift-off position is dominated mainly by the diffusion mode.