On-the-fly artificial neural network for chemical kinetics in direct numerical simulations of premixed combustion

In this study, an on-the-fly artificial neural network (ANN) framework has been developed for the tabulation of chemical reaction terms in direct numerical simulations (DNS) of premixed and igniting flames. The procedure does not require any preliminary knowledge to generate samples for ANN training; the whole training process is based on the detailed simulation results and takes place on-the-fly, so that the obtained ANN model is perfectly adapted to the specific problem considered. The framework combines direct integration (DI) and ANN model in an efficient way to overcome the extrapolation issue of the monolithic ANN model. Auto-ignition processes as well as the characteristics of established flames can be very well predicted using the ANN model. In the final simulations, involving a case with 3D turbulent hot-spot ignition, and a flame propagating in a turbulent flow, the developed procedure reduces the computational times by a factor of almost 5, while keeping the error for all species below 1 % compared to the standard, monolithic DI solution.