Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

With the increase in computational power in the last decade and the forthcoming Exascale
supercomputers, a new horizon in computational modelling and simulation is envisioned in …

This study introduces the gradient boosted decision tree (GBDT) as a machine learning
approach to circumvent the need for a direct integration of the typically stiff system of …

The numerical integration of the differential equations describing chemical kinetics
consumes the majority of computational time in combustion simulations that involve direct …

In this work, a deep neural network is presented which is trained on flamelet/progress
variable (FPV) tables and validated in a combustion large eddy simulation (LES) of the …

Turbulent combustion modeling often faces a trade-off between the so-called flamelet-like
models and PDF-like models. Flamelet-like models, are characterized by a choice of a …

Flamelet-based reduced manifold tabulation is very useful to save computing time compared
to simulations of turbulent flames with detailed kinetics. However, conventional tabulation …

Deep artificial neural networks (ANNs) are used for modeling sub-grid scale mixing
quantities such as the filtered density function (FDF) of the mixture fraction and the …

Motivated by the need to reduce computational costs, look-up tables are widely used in
numerical simulations of laminar and turbulent flames, for the thermodynamics of the …