Application of the dynamic mode decomposition to experimental data |
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Authors: | Peter J Schmid |
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Institution: | (1) Laboratoire d’Hydrodynamique (LadHyX), Ecole Polytechnique, Palaiseau, 91128, France |
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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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Keywords: | |
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