Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
[HTML][HTML] Identifying key features in reactive flows: A tutorial on combining dimensionality reduction, unsupervised clustering, and feature correlation
This study examines the capabilities of a data-driven workflow for automated key feature
identification in reactive flows. The proposed approach aims at expediting the analysis of …
identification in reactive flows. The proposed approach aims at expediting the analysis of …
[HTML][HTML] Combustion modeling using Principal Component Analysis: A posteriori validation on Sandia flames D, E and F
The present work shows the first application of the PC-transport approach in the context of
Large Eddy Simulation (LES) of turbulent combustion. Detailed kinetic mechanisms …
Large Eddy Simulation (LES) of turbulent combustion. Detailed kinetic mechanisms …
Adaptive chemistry via pre-partitioning of composition space and mechanism reduction
Numerical simulations of multi-dimensional laminar flames with complex kinetic
mechanisms are computationally very demanding, because of the large number of species …
mechanisms are computationally very demanding, because of the large number of species …
Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure
The present work advances the PC-transport approach in the context of Large Eddy
Simulation (LES) of turbulent combustion. Accurate modeling of combustion systems …
Simulation (LES) of turbulent combustion. Accurate modeling of combustion systems …
[HTML][HTML] Application of reduced-order models based on PCA & Kriging for the development of digital twins of reacting flow applications
Detailed numerical simulations of detailed combustion systems require substantial
computational resources, which limit their use for optimization and uncertainty quantification …
computational resources, which limit their use for optimization and uncertainty quantification …
Principal component analysis coupled with nonlinear regression for chemistry reduction
Large kinetic mechanisms are required in order to accurately model combustion systems. If
no parameterization of the thermo-chemical state-space is used, solution of the species …
no parameterization of the thermo-chemical state-space is used, solution of the species …
Deep learning for presumed probability density function models
In this work, we use machine learning (ML) techniques to develop presumed probability
density function (PDF) models for large eddy simulations (LES) of reacting flows. The joint …
density function (PDF) models for large eddy simulations (LES) of reacting flows. The joint …
Dimensionality reduction for visualizing industrial chemical process data
This paper explores dimensionality reduction (DR) approaches for visualizing high
dimensional data in chemical processes. Visualization provides powerful insight and …
dimensional data in chemical processes. Visualization provides powerful insight and …
[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 …