Application of the dynamic mode decomposition to experimental data

Application of the dynamic mode decomposition to experimental data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Experiments in Fluids Springer Journals

Application of the dynamic mode decomposition to experimental data

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Publisher
Springer-Verlag
Copyright
Copyright © 2011 by Springer-Verlag
Subject
Engineering; Engineering Thermodynamics, Heat and Mass Transfer; Engineering Fluid Dynamics; Fluid- and Aerodynamics
ISSN
0723-4864
eISSN
1432-1114
D.O.I.
10.1007/s00348-010-0911-3
Publisher site
See Article on Publisher Site

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.

Journal

Experiments in FluidsSpringer Journals

Published: Feb 2, 2011

References

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