Application of the dynamic mode decomposition to experimental data

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.

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Acknowledgments

Support from the Agence Nationale de la Recherche (ANR) through their “chaires d’excellence” program and from the Alexander-von-Humboldt Foundation is gratefully acknowledged.

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Correspondence to Peter J. Schmid.

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Schmid, P.J. Application of the dynamic mode decomposition to experimental data. Exp Fluids 50, 1123–1130 (2011). https://doi.org/10.1007/s00348-010-0911-3

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Keywords

  • Shear Layer
  • Proper Orthogonal Decomposition
  • Proper Orthogonal Decomposition Mode
  • Dynamic Mode Decomposition
  • Arnoldi Method