Recent developments in DNS of turbulent combustion
P Domingo, L Vervisch - Proceedings of the Combustion Institute, 2023 - Elsevier
The simulation of turbulent flames fully resolving the smallest flow scales and the thinnest
reaction zones goes along with specific requirements, which are discussed from …
reaction zones goes along with specific requirements, which are discussed from …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
[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 …
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics
Abstract Machine learning has long been considered a black box for predicting combustion
chemical kinetics due to the extremely large number of parameters and the lack of …
chemical kinetics due to the extremely large number of parameters and the lack of …
Model identification in reactor-based combustion closures using sparse symbolic regression
Abstract In Large Eddy Simulations (LES) of combustion, the accuracy of predictions might
be heavily affected by deficiencies in traditional/simplified closure models, especially when …
be heavily affected by deficiencies in traditional/simplified closure models, especially when …
Laminar flame speed modeling for low carbon fuels using methods of machine learning
S Shahpouri, A Norouzi, C Hayduk, A Fandakov… - Fuel, 2023 - Elsevier
Abstract Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods are
designed to accurately predict Laminar Flame Speed (LFS) over the entire engine operating …
designed to accurately predict Laminar Flame Speed (LFS) over the entire engine operating …
BLASTNet: A call for community-involved big data in combustion machine learning
Many state-of-the-art machine learning (ML) fields rely on large datasets and massive deep
learning models (with O (10 9) trainable parameters) to predict target variables accurately …
learning models (with O (10 9) trainable parameters) to predict target variables accurately …
A hybrid physical data informed DNN in axial displacement prediction of immersed tunnel joint
Due to complex interactions between immersed tunnel and surrounding environment, it is
difficult to apply theoretical analysis for axial displacement (DIS) of immersion joints. To …
difficult to apply theoretical analysis for axial displacement (DIS) of immersion joints. To …
Data-driven discovery of heat release rate markers for premixed NH3/H2/air flames using physics-informed machine learning
The spatial distribution of heat release rate (HRR) is important for flame front identification.
However, direct measurement of HRR is impossible using the current experimental …
However, direct measurement of HRR is impossible using the current experimental …
[HTML][HTML] SGS Reaction rate modelling for MILD combustion based on machine-learning combustion mode classification: Development and a priori study
A neural network (NN) aided model is proposed for the filtered reaction rate in moderate or
intense low-oxygen dilution (MILD) combustion. The framework of the present model is …
intense low-oxygen dilution (MILD) combustion. The framework of the present model is …