Applications of the dynamic mode decomposition

P. J. Schmid; L. Li; M. P. Juniper; O. Pust

doi:10.1007/s00162-010-0203-9

Applications of the dynamic mode decomposition

P. J. Schmid, L. Li, M. P. Juniper, O. Pust

Research output: Contribution to journal › Article › peer-review

481 Scopus citations

Abstract

The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and harmonically forced jet. The algorithm relies on the reconstruction of a low-dimensional inter-snapshot map from the available flow field data. The spectral decomposition of this map results in an eigenvalue and eigenvector representation (referred to as dynamic modes) of the underlying fluid behavior contained in the processed flow fields. This dynamic mode decomposition allows the breakdown of a fluid process into dynamically revelant and coherent structures and thus aids in the characterization and quantification of physical mechanisms in fluid flow. © 2010 Springer-Verlag.

Original language	English (US)
Pages (from-to)	249-259
Number of pages	11
Journal	Theoretical and Computational Fluid Dynamics
Volume	25
Issue number	1-4
DOIs	https://doi.org/10.1007/s00162-010-0203-9
State	Published - Jun 1 2011
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

ASJC Scopus subject areas

Computational Mechanics
General Engineering
Fluid Flow and Transfer Processes
Condensed Matter Physics

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10.1007/s00162-010-0203-9

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AU - Schmid, P. J.

AU - Li, L.

AU - Juniper, M. P.

AU - Pust, O.

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Y1 - 2011/6/1

N2 - The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and harmonically forced jet. The algorithm relies on the reconstruction of a low-dimensional inter-snapshot map from the available flow field data. The spectral decomposition of this map results in an eigenvalue and eigenvector representation (referred to as dynamic modes) of the underlying fluid behavior contained in the processed flow fields. This dynamic mode decomposition allows the breakdown of a fluid process into dynamically revelant and coherent structures and thus aids in the characterization and quantification of physical mechanisms in fluid flow. © 2010 Springer-Verlag.

AB - The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and harmonically forced jet. The algorithm relies on the reconstruction of a low-dimensional inter-snapshot map from the available flow field data. The spectral decomposition of this map results in an eigenvalue and eigenvector representation (referred to as dynamic modes) of the underlying fluid behavior contained in the processed flow fields. This dynamic mode decomposition allows the breakdown of a fluid process into dynamically revelant and coherent structures and thus aids in the characterization and quantification of physical mechanisms in fluid flow. © 2010 Springer-Verlag.

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DO - 10.1007/s00162-010-0203-9

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ER -

Applications of the dynamic mode decomposition

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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