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 …
Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions
A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (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 …
Effect of assisted hydrogen on combustion and emission characteristics of a diesel engine fueled with biodiesel
Z Zhang, J Lv, G Xie, S Wang, Y Ye, G Huang, D Tan - Energy, 2022 - Elsevier
In this paper, the effects of hydrogen assisted biodiesel with different fatty acid methyl esters
(FAMEs) proportion on cylinder pressure, cylinder temperature, indicated thermal efficiency …
(FAMEs) proportion on cylinder pressure, cylinder temperature, indicated thermal efficiency …
Numerical investigation on selecting appropriate piston bowl geometry and compression ratio for gasoline-fuelled homogeneous charge compression ignited light …
AV Kale, A Krishnasamy - Energy, 2023 - Elsevier
The gasoline-fuelled homogeneous charge compression ignition (HCCI) combustion is a
promising approach to mitigate the significant limitations of the traditional compression …
promising approach to mitigate the significant limitations of the traditional compression …
A novel automated SuperLearner using a genetic algorithm-based hyperparameter optimization
Industrial revolution 4.0 has pushed industries worldwide to use machine learning (ML)
models to address real-world engineering problems. The industry generally faces two main …
models to address real-world engineering problems. The industry generally faces two main …
On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine
Q Huang, J Liu, C Ulishney… - International Journal of …, 2022 - journals.sagepub.com
The use of computational models for internal combustion engine development is ubiquitous.
Numerical simulations using simpler to complex physical models can predict engine's …
Numerical simulations using simpler to complex physical models can predict engine's …
DoE-ML guided optimization of an active pre-chamber geometry using CFD
An optimized active pre-chamber geometry was obtained by combining computational fluid
dynamics (CFD) and machine learning (ML). A heavy-duty engine operating with methane …
dynamics (CFD) and machine learning (ML). A heavy-duty engine operating with methane …
Applications of machine learning to the analysis of engine in-cylinder flow and thermal process: A review and outlook
F Zhao, DLS Hung - Applied Thermal Engineering, 2023 - Elsevier
To adequately elucidate the complex in-cylinder flow structures and its underlying effects on
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design
A novel design optimization approach (ActivO) that employs an ensemble of machine
learning algorithms is presented. The proposed approach is a surrogate-based scheme …
learning algorithms is presented. The proposed approach is a surrogate-based scheme …