The re-kindled fascination in machine learning (ML), observed over the last few decades,
has also percolated into natural sciences and engineering. ML algorithms are now used in …

This review examines large eddy simulation (LES) models from the perspective of their a
priori statistical characteristics. The most well-known statistical characteristic of an LES …

Interactions of thermodiffusive instabilities and turbulence have been investigated by large-
scale Direct Numerical Simulations (DNS) in this work. Two DNS of turbulent premixed lean …

The propensity of lean premixed hydrogen flames to develop intrinsic instabilities is studied
by means of a series of simulations at different equivalence ratios [0.4–1.0], unburned …

An artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the
subgrid-scale (SGS) stress in large-eddy simulation. An ANN is used to establish a …

The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

Direct numerical simulations of bubbly multiphase flows are used to find closure terms for a
simple model of the average flow, using Neural Networks (NNs). The flow considered …

New procedures are explored for the development of models in the context of large eddy
simulation (LES) of a passive scalar. They rely on the combination of the optimal estimator …

A novel approach to the lum** of species in large chemical kinetic mechanisms is
presented. Species with similar composition and functionalities are lumped into one single …

Data generated by direct numerical simulations (DNS) of bubbly up-flow in a periodic
vertical channel is used to generate closure relationships for a simplified two-fluid model for …