Applications of the dynamic mode decomposition

Schmid, Peter J.; Li, Larry K.B.; Juniper, Matthew P.; Pust, O.

doi:10.1007/s00162-010-0203-9

Cited by 515 publications

(273 citation statements)

References 21 publications

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“…The Dynamic Mode Decomposition (DMD), which uses experimental or numerical data to reconstruct a low-dimensional inter-snapshot map, which is subsequently utilized to break down a physical process in dynamically relevant modes and coherent structures, is examined in the context of time-resolved PIV measurements of forced and unforced jets [32]. The Eigensystem Realization Algorithm (ERA) is shown to produce equivalent reduced-order models as the balanced POD technique [22], which is in turn a computationally tractable simpliflcation of the more general balanced truncation technique, but without recourse to data from solutions to adjoint equations, and thus applicable to experimental data.…”

Section: Progress Made In the Last Decadementioning

confidence: 99%

Special issue on global flow instability and control

Theofilis

Colonius

2011

Theor. Comput. Fluid Dyn.

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This special issue is intended to provide a snapshot of current research in the área of "Global Flow Instability and Control". The original papers, and to a certain extent the topic itself, are intimately linked with the series of symposia by the same ñame that were held in Crete, Greece, between 2001 and 2009. As members of the organizing committees of the Crete symposia, we invited all past participants to contribute, and all papers were reviewed following the strict standards of the journal. This preface gives a brief historical account of events that have shaped ideas in the fleld over the past decade, followed by a synopsis of the papers published herein. Keyword Global flow instability and control The Crete meetingsIn July 1998, Peter Duck and Anatoly Ruban organized the EUROMECH 384 CoUoquium on "Steady and Unsteady SeparatedFlows" in Manchester, with Sir James Lighthill as the keynote speaker. The proceedings of this colloquium were published as a Research Frontiers issue, volume 358, of the Philosophical Transactions of the Royal Society London journal. The preface of that issue contains the colourful hand-written transparencies of Sir James, which were bound to be the last scientiflc document written by this towering fluid mechanics figure. A summary versión of another set of hand-written colourful drawings, corresponding to the hour-long presentation of Uwe Dallmann on flow topology [6], was included as the last chapter of the three-part paper by Theofilis, Hein and Dallmann [39] and provided a qualitative description of the first quantitative discovery, via a partial-differential-equation (PDE)-based eigenvalue problem (EVP) solution, of what is now commonly called global mode of a laminar separation bubble. One of the highlights in the prematurely ended scientiflc career of Uwe Dallmann was the enthusiastic appreciation of his flow topology work by Sir James, which is only natural, given that the first appearance of the association between flow separation and flow topology was the work of Lighthill himself in the classic textbook of Rosenhead [19].

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Section: Progress Made In the Last Decadementioning

confidence: 99%

Special issue on global flow instability and control

Theofilis

Colonius

2011

Theor. Comput. Fluid Dyn.

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“…The input data for DMD [6][7][8] have to be presented in the form of sequence of snapshorts and are set by a matrix V with the size n×m, where…”

Section: R T T Snapshots Methodsmentioning

confidence: 99%

Implementation of Pod and DMD Methods in Apache Spark Framework for Simulation of Unsteady Turbulent Flow in the Model Combustor

Kalugin¹,

Strijhak²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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“…DMD was originally introduced in the area of computational fluid dynamics (CFD) [28], specifically for analysing the sequential image data generated by nonlinear complex fluid flows [25][26][27]34]. The DMD decomposes a given image sequence into several images, called dynamic modes.…”

Section: Motivation: Dynamic Mode Decomposition (Dmd)mentioning

confidence: 99%

“…The second is a singular value decomposition (SVD)-based approach that is more robust to noise in the data and to numerical errors [25].…”

Section: Dynamic Mode Decomposition (Dmd)mentioning

confidence: 99%