[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 …
[HTML][HTML] The reactor-based perspective on finite-rate chemistry in turbulent reacting flows: A review from traditional to low-emission combustion
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 …
and mixing. The interactions between turbulent motions and chemistry are crucial to the …
SVD perspectives for augmenting DeepONet flexibility and interpretability
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 …
attracting attention in multiple fields due to their utility for fast and accurate emulation of …
Cost function for low-dimensional manifold topology assessment
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 …
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
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 …
Manifold-informed state vector subset for reduced-order modeling
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 …
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
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 …
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 …
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 …