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] Low-speed pre-ignition and super-knock in boosted spark-ignition engines: A review
The introduction of downsized, turbocharged Gasoline Direct Injection (GDI) engines in the
automotive market has led to a rapid increase in research on Low-speed Pre-ignition (LSPI) …
automotive market has led to a rapid increase in research on Low-speed Pre-ignition (LSPI) …
Knock probability determination employing convolutional neural network and IGTD algorithm
M Hosseini, I Chitsaz - Energy, 2023 - Elsevier
This study presents a novel method based on the convolutional neural network to evaluate
knock probability. In this way, lots of data sets are extracted from the real driving conditions …
knock probability. In this way, lots of data sets are extracted from the real driving conditions …
[HTML][HTML] Robust key parameter identification of dedicated hybrid engine performance indicators via K-fold filter collaborated feature selection
Dedicated hybrid engine technology using auxiliary electronic components has been proven
as an energy-saving solution to public concerns about energy consumption and carbon …
as an energy-saving solution to public concerns about energy consumption and carbon …
Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data
In this study, deep learning (DL) model is used to predict brake power (BP) of GX35-OHC 4-
stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline+ …
stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline+ …
A method of predicting wear and damage of pantograph sliding strips based on artificial neural networks
The impact of the pantograph of a rail vehicle on the overhead contact line depends on
many factors. Among other things, the type of pantograph, ie, the material of the sliding strip …
many factors. Among other things, the type of pantograph, ie, the material of the sliding strip …
Deep learning for knock occurrence prediction in SI engines
H Tajima, T Tomidokoro, T Yokomori - Energies, 2022 - mdpi.com
This research aims to predict knock occurrences by deep learning using in-cylinder pressure
history from experiments and to elucidate the period in pressure history that is most …
history from experiments and to elucidate the period in pressure history that is most …
Optimization and prediction of a novel preignition in hydrogen direct injection engines through experimentation and the Random forest algorithms
Z Liang, F Xie, Z Guo, Z Wang, H Dou, B Wang… - Energy Conversion and …, 2024 - Elsevier
The potential use of hydrogen in automotive internal combustion engines could aid in the
energy transition and contribute to achieving carbon neutrality. This paper has investigated …
energy transition and contribute to achieving carbon neutrality. This paper has investigated …
Using deep learning to diagnose preignition in turbocharged spark-ignited engines
Internal combustion engines of today are expected to reduce their greenhouse gas
emissions to comply with global climate change mitigation targets. This can be achieved …
emissions to comply with global climate change mitigation targets. This can be achieved …
[HTML][HTML] Characteristic time scale as optimal input in Machine Learning algorithms: Homogeneous autoignition
Considering temporally evolving processes, the search for optimal input selection in
Machine Learning (ML) algorithms is extended here beyond (i) the readily available …
Machine Learning (ML) algorithms is extended here beyond (i) the readily available …