[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
Advancing reacting flow simulations with data-driven models
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 …
However, the key to an effective use of machine learning tools in multi-physics problems …
A methodology for estimating hypersonic engine performance by coupling supersonic reactive flow simulations with machine learning techniques
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 …
coupling some machine learning methods with a generated CFD database and one …
Reduced-order modeling of supersonic fuel–air mixing in a multi-strut injection scramjet engine using machine learning techniques
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 …
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
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 …
researchers generate, analyze and improve low-dimensional data manifolds. In the current …
Local manifold learning and its link to domain-based physics knowledge
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 …
evolve close to a low-dimensional manifold (LDM). Various approaches are available to …
A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
For turbulent reacting flow systems, identification of low-dimensional representations of the
thermo-chemical state space is vitally important, primarily to significantly reduce the …
thermo-chemical state space is vitally important, primarily to significantly reduce the …
Low-dimensional representation of intermittent geophysical turbulence with high-order statistics-informed neural networks (H-SiNN)
We present a novel machine learning approach to reduce the dimensionality of state
variables in stratified turbulent flows governed by the Navier–Stokes equations in the …
variables in stratified turbulent flows governed by the Navier–Stokes equations in the …
Data-driven models and digital twins for sustainable combustion technologies
A Parente, N Swaminathan - Iscience, 2024 - cell.com
We highlight the critical role of data in developing sustainable combustion technologies for
industries requiring high-density and localized energy sources. Combustion systems are …
industries requiring high-density and localized energy sources. Combustion systems are …
[PDF][PDF] Reduced-order modeling of reacting flows using data-driven approaches
Data-driven modeling of complex dynamical systems is becoming increasingly popular
across various domains of science and engineering. This is thanks to advances in numerical …
across various domains of science and engineering. This is thanks to advances in numerical …