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

The use of machine learning algorithms to predict behaviors of complex systems is booming.
However, the key to an effective use of machine learning tools in multi-physics problems …

We investigate the implementation of principal component (PC) transport to accelerate the
direct numerical simulation (DNS) of turbulent combustion flows. The acceleration is …

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 …

A combustion chemistry acceleration scheme for implementation in reacting flow simulations
is developed based on deep operator nets (DeepONets). The scheme is based on a …

This article presents an innovative open-source software named ModelFLOWs-app, 1
written in Python, which has been created and tested to generate precise and robust hybrid …

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 Principal Component Analysis can be used to reduce the cost of Computational
Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the …

Abstract Machine learning provides a set of new tools for the analysis, reduction and
acceleration of combustion chemistry. The implementation of such tools is not new …

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 …