Modelling filtered reaction rate in turbulent premixed flames using feature importance analysis, gene expression programming and tiny artificial neural networks

Abstract

Two different machine learning (ML) approaches, Symbolic regression, more precisely Gene Expression Programming (GEP) and Tiny Artificial Neural Networks (TANN), have been used for deducing a model for the filtered reaction rate in the context of Large Eddy Simulation (LES) of premixed combustion. The models have been trained on data generated from a large range of filter widths and for different turbulence intensities, using a simple chemistry Direct Numerical Simulation (DNS) database of statistically planar turbulent premixed flames. Feature importance analysis has been applied to identify the important input variables for modelling filtered reaction rate in order to enable an efficient optimization process as well as moderate inference times. The resulting models show excellent correlation and good quantitative agreement with the filtered reaction rates extracted from explicitly filtered DNS data. The genetic optimization results in a mathematical expression that can be easily implemented, analysed and documented. Also, the TANN has a small number of trainable parameters that can be shared with the research community. The present results demonstrate that ML algorithms can be successfully used for modelling turbulent premixed flames in the context of Large Eddy Simulation. Both approaches can fulfil the requirements of transparency and have inference times that are acceptable in comparison to algebraic models.