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 …

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

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

Abstract Conditional Source-term Estimation (CSE) is a turbulence–chemistry interaction
model to simulate reacting flows. This model is similar to the Conditional Moment Closure …

The simulation of turbulent flames fully resolving the smallest flow scales and the thinnest
reaction zones goes along with specific requirements, which are discussed from …

In the present work, artificial neural networks (ANN) technique combined with flamelet
generated manifolds (FGM) is proposed to mitigate the memory issue of FGM models. A set …

Accurate and detailed data are vital for fundamental understanding of turbulent combustion.
However, studies of turbulent combustion often suffer from measurement sparsity or high …

We highlight the critical role of data in develo** sustainable combustion technologies for
industries requiring high-density and localized energy sources. Combustion systems are …

A machine learning algorithm, the deep neural network (DNN) 1, is trained using a
comprehensive direct numerical simulation (DNS) dataset to predict joint filtered density …

This investigation outlines a data-assisted approach that employs random forest classifiers
for local and dynamic submodel assignment in turbulent-combustion simulations. This …