Hanuschkin, Alexander ; Schober, Steffen ; Bode, Johannes ; Schorr, Jürgen ; Böhm, Benjamin ; Krüger, Christian ; Peters, Steven (2019)
Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance.
In: International Journal of Engine Research, 22 (1)
doi: 10.1177/1468087419833269
Article, Bibliographie
This is the latest version of this item.
Abstract
Cycle-to-cycle variations in an optically accessible four-stroke direct injection spark-ignition gasoline engine are investigated using high-speed scanning particle image velocimetry and in-cylinder pressure measurements. Particle image velocimetry allows to measure in-cylinder flow fields at high spatial and temporal resolution. Binary classifiers are used to predict combustion cycles of high indicated mean effective pressure based on in-cylinder flow features and engineered tumble features obtained during the intake and the compression stroke. Basic in-cylinder flow features of the mid-cylinder plane are sufficient to predict combustion cycles of high indicated mean effective pressure as early as 180 degree crank angle before the top dead center at 0 degree crank angle. Engineered characteristic tumble features derived from the flow field are not superior to the basic flow features. The results are independent of the tested machine learning method (multilayer perceptron and boosted decision trees) and robust to hyper-parameter selection.
| Item Type: | Article |
|---|---|
| Erschienen: | 2019 |
| Creators: | Hanuschkin, Alexander ; Schober, Steffen ; Bode, Johannes ; Schorr, Jürgen ; Böhm, Benjamin ; Krüger, Christian ; Peters, Steven |
| Type of entry: | Bibliographie |
| Title: | Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance |
| Language: | English |
| Date: | 7 September 2019 |
| Publisher: | Sage Publications |
| Journal or Publication Title: | International Journal of Engine Research |
| Volume of the journal: | 22 |
| Issue Number: | 1 |
| DOI: | 10.1177/1468087419833269 |
| Corresponding Links: | |
| Abstract: | Cycle-to-cycle variations in an optically accessible four-stroke direct injection spark-ignition gasoline engine are investigated using high-speed scanning particle image velocimetry and in-cylinder pressure measurements. Particle image velocimetry allows to measure in-cylinder flow fields at high spatial and temporal resolution. Binary classifiers are used to predict combustion cycles of high indicated mean effective pressure based on in-cylinder flow features and engineered tumble features obtained during the intake and the compression stroke. Basic in-cylinder flow features of the mid-cylinder plane are sufficient to predict combustion cycles of high indicated mean effective pressure as early as 180 degree crank angle before the top dead center at 0 degree crank angle. Engineered characteristic tumble features derived from the flow field are not superior to the basic flow features. The results are independent of the tested machine learning method (multilayer perceptron and boosted decision trees) and robust to hyper-parameter selection. |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD) |
| Date Deposited: | 08 Sep 2022 05:19 |
| Last Modified: | 22 May 2024 06:26 |
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| Suche nach Titel in: | TUfind oder in Google |
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Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance. (deposited 21 May 2024 09:07)
- Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance. (deposited 08 Sep 2022 05:19) [Currently Displayed]
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