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

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

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 Measuring the Research Octane Number (RON) and the Motor Octane Number
(MON) at a low price is currently not feasible, thus making the use of predictive methods …

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 …

Fast and accurate evaluation of aerodynamic characteristics is essential for aerodynamic
design optimization because aircraft programs require many years of design and …

This article introduces a novel, fully data-driven method for forming reduced order models
(ROMs) in complex flow databases that consist of a large number of variables. The algorithm …

We present an approach called guaranteed block autoencoder that leverages Tensor
Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid …