Application of the dynamic mode decomposition to experimental data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

219 Scopus citations

Abstract

The dynamic mode decomposition (DMD) is a data-decomposition technique that allows the extraction of dynamically relevant flow features from time-resolved experimental (or numerical) data. It is based on a sequence of snapshots from measurements that are subsequently processed by an iterative Krylov technique. The eigenvalues and eigenvectors of a low-dimensional representation of an approximate inter-snapshot map then produce flow information that describes the dynamic processes contained in the data sequence. This decomposition technique applies equally to particle-image velocimetry data and image-based flow visualizations and is demonstrated on data from a numerical simulation of a flame based on a variable-density jet and on experimental data from a laminar axisymmetric water jet. In both cases, the dominant frequencies are detected and the associated spatial structures are identified. © 2011 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationExperiments in Fluids
Pages1123-1130
Number of pages8
DOIs
StatePublished - Apr 1 2011
Externally publishedYes

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Mechanics of Materials
  • Computational Mechanics
  • Fluid Flow and Transfer Processes

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