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Effects of Outlier Flow Field on the Characteristics of In-Cylinder Coherent Structures Identified by POD-Based Conditional Averaging and Quadruple POD

[+] Author Affiliations
Rui Gao, Li Shen, Kwee-Yan Teh, Penghui Ge, Fengnian Zhao, David L. S. Hung

Shanghai Jiao Tong University, Shanghai, China

Paper No. ICEF2018-9561, pp. V001T03A004; 11 pages
doi:10.1115/ICEF2018-9561
From:
  • ASME 2018 Internal Combustion Engine Division Fall Technical Conference
  • Volume 1: Large Bore Engines; Fuels; Advanced Combustion
  • San Diego, California, USA, November 4–7, 2018
  • Conference Sponsors: Internal Combustion Engine Division
  • ISBN: 978-0-7918-5198-2
  • Copyright © 2018 by ASME

abstract

Proper Orthogonal Decomposition (POD) offers an approach to quantify cycle-to-cycle variation (CCV) of the flow field inside the internal combustion engine cylinder. POD decomposes instantaneous flow fields (also called snapshots) into a series of orthonormal flow patterns (called POD modes) and the corresponding mode coefficients. The POD modes are rank-ordered by decreasing kinetic energy content, and the low-order, high-energy modes are interpreted as constituting the large-scale coherent flow structure that varies from engine cycle to engine cycle. Various POD-based analysis techniques have thus been proposed to characterize engine flow field CCV using these low-order modes. The validity of such POD-based analyses rests, as a matter of course, on the reliability of the underlying POD results (modes and coefficients). Yet a POD mode can be disproportionately skewed by a single outlier snapshot within a large data set, and an algorithm exists to define and identify such outliers. In this paper, the effects of a candidate outlier snapshot on the results of POD-based conditional averaging and quadruple POD analyses are examined for two sets of crank angle-resolved flow fields on the mid-tumble plane of an optical engine cylinder recorded by high-speed particle image velocimetry. The results with and without the candidate outlier are compared and contrasted. In the case of POD-based conditional averaging, the presence of the outlier scrambles the composition of snapshot subsets that define large-scale flow pattern variations, and thus substantially alters the coherent flow structures that are identified; for quadruple POD, the shape of coherent structures as well as the number of modes to define them are not significantly affected by the outlier.

Copyright © 2018 by ASME

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