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

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 …

As data-intensive techniques proliferate across many scientific disciplines, new criteria for
more objective interpretation and a priori evaluation are required to reconcile data-driven …

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 …

Combustion and energy conversion play critical roles in all facets of environmental and
technological applications, including the utilization of sustainable energy sources for power …

A low-dimensional representation of thermochemical scalars based on cokurtosis principal
component analysis (CoK-PCA) has been shown to effectively capture stiff chemical …

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

In this work, three-dimensional direct numerical simulations of lean turbulent premixed
flames are conducted using a recently developed skeletal reaction mechanism to investigate …

This thesis deals with the acceleration of chemical kinetics calculations in CFD simulations
by relying of machine learning methods. The principle is to replace the resolution of the …

A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor
topological quality of a low-dimensional data projection. This includes behavior such as …