[HTML][HTML] Improving aircraft performance using machine learning: A review

S Le Clainche, E Ferrer, S Gibson, E Cross… - Aerospace Science and …, 2023 - Elsevier
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

[HTML][HTML] The reactor-based perspective on finite-rate chemistry in turbulent reacting flows: A review from traditional to low-emission combustion

A Péquin, MJ Evans, A Chinnici, PR Medwell… - Applications in Energy …, 2023 - Elsevier
In flames, turbulence can either limit or enhance combustion efficiency by means of strain
and mixing. The interactions between turbulent motions and chemistry are crucial to the …

SVD perspectives for augmenting DeepONet flexibility and interpretability

S Venturi, T Casey - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
Deep operator networks (DeepONets) are powerful and flexible architectures that are
attracting attention in multiple fields due to their utility for fast and accurate emulation of …

Cost function for low-dimensional manifold topology assessment

K Zdybał, E Armstrong, JC Sutherland, A Parente - Scientific Reports, 2022 - nature.com
In reduced-order modeling, complex systems that exhibit high state-space dimensionality
are described and evolved using a small number of parameters. These parameters can be …

A methodology for estimating hypersonic engine performance by coupling supersonic reactive flow simulations with machine learning techniques

AC Ispir, BH Saracoglu, T Magin… - Aerospace Science and …, 2023 - Elsevier
We propose a methodology used to estimate the performance of hypersonic engines by
coupling some machine learning methods with a generated CFD database and one …

Manifold-informed state vector subset for reduced-order modeling

K Zdybał, JC Sutherland, A Parente - Proceedings of the Combustion …, 2023 - Elsevier
Reduced-order models (ROMs) for turbulent combustion rely on identifying a small number
of parameters that can effectively describe the complexity of reacting flows. With the advent …

Reduced-order modeling of supersonic fuel–air mixing in a multi-strut injection scramjet engine using machine learning techniques

AC Ispir, K Zdybał, BH Saracoglu, T Magin, A Parente… - Acta Astronautica, 2023 - Elsevier
Dual-mode ramjet/scramjet engines promise extended flight speed range and are the
commonly preferred air-breathing propulsion system from within the family of hypersonic …

[HTML][HTML] PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

K Zdybał, E Armstrong, A Parente, JC Sutherland - SoftwareX, 2023 - Elsevier
We describe an update to our open-source Python package, PCAfold, designed to help
researchers generate, analyze and improve low-dimensional data manifolds. In the current …

Advancing reacting flow simulations with data-driven models

K Zdybał, G D'Alessio, G Aversano, MR Malik… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …

Local manifold learning and its link to domain-based physics knowledge

K Zdybał, G D'Alessio, A Attili, A Coussement… - Applications in Energy …, 2023 - Elsevier
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 …