Application of Machine learning for turbulent combustion modelling

Application of machine learning to reacting flows is gaining popularity. The use of a Deep Neural Network (DNN) trained on MILD (Moderate, Intense and Low Dilution) combustion data to stratified turbulent flames is explored to estimate sub-grid joint Filtered Density Functions (FDFs) of mixture fraction and reaction progress variable. This objective is achieved by using log of filtered mixture fraction normalised by its stoichiometric value rather than the mixture fraction itself as one the input to the DNN. A good agreement of the estimated FDFs and DNS values is observed for a range of stratified flame conditions despite the use of MILD combustion DNS data for the training phase. Also, filtered reaction rates calculated using FDFs from DNN agree well with DNS data and the root-mean-squared Error is observed to be below 3%.