.png){kind=icon}
Return
Machine learning for combustion
.png){kind=icon}
OA
AI
DOI：10.1016/j.egyai.2021.100128.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc. The key to study combustion is to improve the combustion efficiency with minimum emission of pollutants. Machine learning facilitates datadriven techniques for handling large amounts of combustion data, either obtained through experiments or simulations under multiple spatiotemporal scales, thereby finding the hidden patterns underlying these data and promoting combustion research. This work presents an overview of studies on the applications of machine learning in combustion science fields over the past several decades. We introduce the fundamentals of machine learning and its usage in aiding chemical reactions, combustion modeling, combustion measurement, engine performance prediction and optimization, and fuel design. The opportunities and limitations of using machine learning in combustion studies are also discussed. This paper aims to provide readers with a portrait of what and how machine learning can be used in combustion research and to inspire researchers in their ongoing studies. Machine learning techniques are rapidly advancing in this era of big data, and there is high potential for exploring the combination between machine learning and combustion research and achieving remarkable results.
Keywords:
Machine learning
Data -driven
Combustion modeling
Combustion diagnostic
Fuel
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
895
Citations:
3.1K
Organization
Cited Papers
ShareSaveShareSaveArtificial neural network models to predict density, dynamic viscosity, and cetane number of biodiesel.png)
FUEL
FUELIF7.5
PREAI
ShareSaveShareSave
View more