Separation of unsteady scales in a mixing layer using empirical mode decomposition

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Abstract

Empirical mode decomposition has been shown to be a valuable data analysis tool in a wide array of technical fields, and it has recently emerged as a useful technique for studying complex fluid flows. This technique is useful for separating relevant unsteady spatiotemporal scales from a signal through an adaptive, iterative data-processing algorithm. Traditionally, empirical mode decomposition has been used to study time-dependent single-dimensional signals, though it can also be useful for studying multidimensional flowfield data. For the current study, unsteady velocity measurements were acquired across the initial development region of a turbulent-plane mixing layer using time-resolved particle image velocimetry. Empirical mode decomposition was applied to the resulting velocity field measurements to decompose the flowfield into several unsteady modes. Single-dimensional empirical mode decomposition was used to understand the time-frequency behavior of the flow associated with the formation and pairing process of large-scale vortical structures. The resulting spectrogramclearly displayed a periodic influence of large-scale vortex structures produced by the Kelvin-Helmholtz instability, which also exhibited a phase relationship with the vortex pairing mechanism. A multidimensional empirical mode decomposition analysis was also conducted, which allowed the unsteady velocity field to be decomposed into scale-based modes with dependence on x, y, and t dimensions. This analysis allowed the influence of small-scale turbulent structures within the flowfield to be separated from those associated with large-scale vortical structures produced by the Kelvin-Helmholtz instability.

Original languageEnglish (US)
Pages (from-to)419-434
Number of pages16
JournalAIAA journal
Volume55
Issue number2
DOIs
StatePublished - Jan 1 2017

ASJC Scopus subject areas

  • Aerospace Engineering

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