Combustion, chemistry, and carbon neutrality
K Kohse-Höinghaus - Chemical Reviews, 2023 - ACS Publications
Combustion is a reactive oxidation process that releases energy bound in chemical
compounds used as fuels─ energy that is needed for power generation, transportation …
compounds used as fuels─ energy that is needed for power generation, transportation …
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 …
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] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …
chemical phenomena in time and length scales, including complex chemical reactions and …
ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers
Solving for detailed chemical kinetics remains one of the major bottlenecks for
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
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 …
Machine learning tabulation of thermochemistry in turbulent combustion: An approach based on hybrid flamelet/random data and multiple multilayer perceptrons
T Ding, T Readshaw, S Rigopoulos, WP Jones - Combustion and Flame, 2021 - Elsevier
A new machine learning methodology is proposed for speeding up thermochemistry
computations in simulations of turbulent combustion. The approach is suited to a range of …
computations in simulations of turbulent combustion. The approach is suited to a range of …
DeepFlame: A deep learning empowered open-source platform for reacting flow simulations
Recent developments in deep learning have brought many inspirations for the scientific
computing community and it is perceived as a promising method in accelerating the …
computing community and it is perceived as a promising method in accelerating the …
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 …
[HTML][HTML] Numerical analysis of the fractal-fractional diffusion model of ignition in the combustion process
M Partohaghighi, M Mortezaee, A Akgül… - Alexandria Engineering …, 2024 - Elsevier
The study employs the fractal-fractional operator to derive a distinct variant of the fractal-
fractional diffusion equation. To address this challenge, a novel operational matrix technique …
fractional diffusion equation. To address this challenge, a novel operational matrix technique …