Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance. (January 2021)

• Record Type: Journal Article
• Authors: Hanuschkin, Alexander; Schober, Steffen; Bode, Johannes; Schorr, Jürgen; Böhm, Benjamin; Krüger, Christian; Peters, Steven
• Is Part Of: International journal of engine research. Volume 22:Number 1(2021)
• Page Start: 257
• Page End: 272
• Publication Date: 2021-01
• Subjects: Gasoline combustion engine -- cycle-to-cycle variations -- high-speed scanning particle image velocimetry -- binary classifier -- feature importance -- neural network; Engines -- Periodicals; 629.25
• DOI: 10.1177/1468087419833269
• Languages: English
• ISSNs: 1468-0874

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.