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

Synthetic jets are useful fluid devices with several industrial applications. In this study, we
use the flow fields generated by two synchronously operating synthetic jets and simulated …

This work presents a new application of higher order dynamic mode decomposition
(HODMD) for the analysis of reactive flows. Due to the high complexity of the data analysed …

Abstract Large Eddy Simulations (LES) of turbulent reacting flows carried out with detailed
kinetic mechanisms have a key role for the discovery of the physical and chemical …

In many reacting flow systems, the thermo-chemical state-space is known or assumed to
evolve close to a low-dimensional manifold (LDM). Various approaches are available to …

Data-driven modeling of complex dynamical systems is becoming increasingly popular
across various domains of science and engineering. This is thanks to advances in numerical …

Abstract Dynamic Cell Clustering (DCC), also referred as Cell Agglomeration, is an
optimisation technique used to reduce the cost of finite-rate chemistry in reactive flows. It …

Nonequilibrium thermochemical processes such as vibrational excitation, chemical
dissociation, and ionization are critical to accurately characterize hypersonic flows. The …

This chapter describes the use of machine learning (ML) algorithms with the linear-eddy
mixing (LEM) based tabulation for modeling of subgrid turbulencechemistry interaction. The …

Numerical simulation of turbulent flames is a computationally challenging task. This remains
true even with the current advances in numerical algorithms and highperformance …