K-means clustering-driven detection of time-resolved vortex patterns and cyclic variations inside a direct injection engine. (5th November 2020)

• Record Type: Journal Article
• Authors: Zhao, Fengnian; Hung, David L.S.; Wu, Shengqi
• Is Part Of: Applied thermal engineering. Volume 180(2020)
• Publication Date: 2020-11-05
• Subjects: K-means clustering -- Time-resolved vortex pattern detection -- Cyclic variations; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2020.115810
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: 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 cyclic flow variations. Abstract: Time-resolved vortices are indispensable rotational flow structures which impact fuel-air mixing, combustion, and thermal efficiency of internal combustion engine. In-cylinder flow fields are largely stochastic, therefore capturing full spatial and temporal details of in-cylinder vortex behavior not only requires enormous datasets, but also time-efficient analysis methods. The loss of transient vortex information often becomes problematic due to temporal averaging of spatial vortex features. In this study, a two-step vortex pattern detection and quantification scheme with a novel application of K-means algorithm was implemented to investigate the transient vortex dynamics and cyclic variations inside an optical direct injection engine. A vortex pattern recognition method was first implemented to locate the vortices which appeared intermittently in each cycle. Then, the K-means algorithm was applied to cluster the vortex zone characteristics of consecutive engine cycles. Results show that vortex detection based on K-means algorithm can efficiently and accurately cluster the vortex centers into different zones and quantify their cyclic variations. It is also capable of minimizing the issue of vortex misidentification due to temporal averaging. This work demonstrates the application of K-means algorithm to detect the dynamic nature of vortex motions and provides a quantitative analysis on flow vortex characteristics and cyclic variations.