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 …
[HTML][HTML] ModelFLOWs-app: data-driven post-processing and reduced order modelling tools
A Hetherington, A Corrochano… - Computer Physics …, 2024 - Elsevier
This article presents an innovative open-source software named ModelFLOWs-app, 1
written in Python, which has been created and tested to generate precise and robust hybrid …
written in Python, which has been created and tested to generate precise and robust hybrid …
[HTML][HTML] Higher order dynamic mode decomposition to model reacting flows
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 …
(HODMD) for the analysis of reactive flows. Due to the high complexity of the data analysed …
Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows
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 …
Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the …
Predicting octane numbers relying on principal component analysis and artificial neural network
S Tipler, G D'Alessio, Q Van Haute, A Parente… - Computers & Chemical …, 2022 - Elsevier
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 …
(MON) at a low price is currently not feasible, thus making the use of predictive methods …
Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows
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 …
kinetic mechanisms have a key role for the discovery of the physical and chemical …
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 …
Impact of the partitioning method on multidimensional adaptive-chemistry simulations
The large number of species included in the detailed kinetic mechanisms represents a
serious challenge for numerical simulations of reactive flows, as it can lead to large CPU …
serious challenge for numerical simulations of reactive flows, as it can lead to large CPU …
Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations
The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the
context of the Sample-Partitioning Adaptive Reduced Chemistry approach was investigated …
context of the Sample-Partitioning Adaptive Reduced Chemistry approach was investigated …
Interpretation and characterization of MILD combustion data using unsupervised clustering informed by physics-based, domain expertise
In this study we use an unsupervised clustering algorithm called Vector Quantization
Principal Component Analysis (VQPCA) to characterize Moderate and Intense Low-oxygen …
Principal Component Analysis (VQPCA) to characterize Moderate and Intense Low-oxygen …