K-means clustering-driven detection of time-resolved vortex patterns and cyclic variations inside a direct injection engine

•K-means clustering method is applied to reveal transient vortex behavior.•Proposed method can cluster vortices into zones with flow features retained.•Vortex misrecognition due to temporal averaging is mitigated with K-means approach.•New K-means clustering capabilities are unveiled to quantify cyc...

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Veröffentlicht in:	Applied thermal engineering Jg. 180; S. 115810
Hauptverfasser:	Zhao, Fengnian, Hung, David L.S., Wu, Shengqi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 05.11.2020 Elsevier BV
Schlagworte:	Aerodynamics Algorithms Cluster analysis Clustering Cyclic variations Cylinders Diesel engines Fluid dynamics Fluid flow Fuel-air mixing Heat transfer Internal combustion engines K-means clustering Pattern recognition Thermodynamic efficiency Time-resolved vortex pattern detection Vector quantization Vortices Time-resolved vortex pattern detection Cyclic variations K-means clustering
ISSN:	1359-4311, 1873-5606
Online-Zugang:	Volltext
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