A machine learning methodology for improving the accuracy of laminar flame simulations with reduced chemical kinetics mechanisms

The focus of the present work is to investigate a new methodology for the rapid generation of laminar flame speed lookup tables, to be used replacing correlation laws in internal combustion engine simulations. Current production engines run mostly under the thickened wrinkled flame combustion regime, which allows the application of a flamelet modelling approach, which requires the a-priori evaluation of the laminar flame speed and thickness. The use of correlation laws, derived from experimental data, has the advantage to be extremely fast to compute, but displays a lack of precision in conditions far from the experimental reference, as it usually happens for ICE applications. On the other hand, the detailed chemical simulation of a freely propagating adiabatic flame, performed for a sufficiently refined grid of reference points, to be interpolated during runtime, might require hundreds of hours of computing. The use of a reduced chemical mechanism can potentially cut by orders of magnitude the required time, but on the other hand it will decrease accuracy. In the present work, the potential of integrating machine learning algorithms and neural networks in the workflow with different approaches was valuated, leveraging the potential of new and optimized software libraries, to reduce simulation times while maintaining a high level of accuracy, with respect to the results obtained with the complete scheme.