Summary of Combustion Condition Identification Using a Decision Tree Based Machine Learning Algorithm Applied to a Model Can Combustor with High Shear Swirl Injector, by Pk Archhith et al.
Combustion Condition Identification using a Decision Tree based Machine Learning Algorithm Applied to a Model Can Combustor with High Shear Swirl Injector
by PK Archhith, SK Thirumalaikumaran, Balasundaram Mohan, Saptharshi Basu
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a machine learning approach to classify combustion conditions in gas turbine engines. A decision tree-based algorithm is used to analyze acoustic pressure and high-speed flame imaging data from a single can combustor fueled by methane. The model accurately predicts stable and unstable operations within a studied parameter range, providing insights into combustion dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a machine learning model that helps predict whether gas turbine engines will run smoothly or experience instability. They analyzed data from a special type of fuel injector and used it to train the model. The model can tell if the engine is running well or not just by looking at acoustic pressure and flame images. This could help make gas turbines more efficient and reduce emissions. |
Keywords
» Artificial intelligence » Decision tree » Machine learning
