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The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Dynamic mode decomposition of numerical and experimental data

Open access
SCHMID, PETER J.·2010·Journal of Fluid Mechanics

Abstract

The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment. The extracted dynamic modes, which can be interpreted as a generalization of global stability modes, can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system of significantly fewer degrees of freedom. The concentration on subdomains of the flow field where relevant dynamics is expected allows the dissection of a complex flow into regions of localized instability phenomena and further illustrates the flexibility of the method, as does the description of the dynamics within a spatial framework. Demonstrations of the method are presented consisting of a plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane and a jet passing between two cylinders.

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Crossref · 10.1017/s0022112010001217

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Metadata

Authors
SCHMID, P. J.
Venue
Journal of Fluid Mechanics
Published
2010-07-01
Volume
656
Pages
5-28
ISSN
  • 0022-1120 (ISSN-L)
  • 1469-7645
OpenAlex
W2014356541
Cited by
5,915
References
2
FWCI
35.58
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Dynamic mode decomposition of numerical and experimental data. https://app.cochat.ai/papers/10.1017/s0022112010001217
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SCHMID, P. J. (2010). Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics. https://doi.org/10.1017/s0022112010001217

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.