Machine Learning for Turbulent Combustion Modelling

Combustion modelling is an incredibly complex field, combining computational fluid dynamics, heat and mass transfer and chemical kinetics. The solution of the chemical kinetics takes up the majority of the time in a simulation. To try to alleviate this, artificial neural networks can be trained to emulate the evolution of the chemical species in time. Artificial neural networks are capable of modelling highly non-linear functions, such as the evolution of chemical species in time. Therefore, this project aims to apply artificial neural networks to model the temporal evolution of chemical species, and then apply it to an industrial situation.