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 …

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 …

A new approach for modal decomposition through re-interpretation of unsteady dynamics,
termed time-axis clustering, is developed in this work and is demonstrated on an …

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 …

In this study we use an unsupervised clustering algorithm called Vector Quantization
Principal Component Analysis (VQPCA) to characterize Moderate and Intense Low-oxygen …

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 …

Structural sensitivity is a valuable tool for assessing flow control strategies. However, its
conventional implementation involves significant computational costs due to the need to …