Unraveling flow patterns through nonlinear manifold learning

Flavia Tauro, Salvatore Grimaldi, Maurizio Porfiri

Research output: Contribution to journalArticle

Abstract

From climatology to biofluidics, the characterization of complex flows relies on computationally expensive kinematic and kinetic measurements. In addition, such big data are difficult to handle in real time, thereby hampering advancements in the area of flow control and distributed sensing. Here, we propose a novel framework for unsupervised characterization of flow patterns through nonlinear manifold learning. Specifically, we apply the isometric feature mapping (Isomap) to experimental video data of the wake past a circular cylinder from steady to turbulent flows. Without direct velocity measurements, we show that manifold topology is intrinsically related to flow regime and that Isomap global coordinates can unravel salient flow features.

Original languageEnglish (US)
Article numbere91131
JournalPloS one
Volume9
Issue number3
DOIs
StatePublished - Mar 10 2014

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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