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  Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition

Yang, R., Zhang, X., Reiter, P., Lohse, D., Shishkina, O., & Linkmann, M. (2022). Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition. GAMM-Mitteilungen, e202200003. doi:10.1002/gamm.202200003.

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 Creators:
Yang, Rui1, Author           
Zhang, Xuan1, Author           
Reiter, Philipp1, Author           
Lohse, Detlef2, Author           
Shishkina, Olga1, Author           
Linkmann, Moritz3, Author
Affiliations:
1Laboratory for Fluid Physics, Pattern Formation and Biocomplexity, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063287              
2Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063285              
3External Organizations, ou_persistent22              

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 Abstract: Streaming Dynamic Mode Decomposition (sDMD) is a low-storage versionof dynamic mode decomposition (DMD), a data-driven method to extractspatiotemporal flow patterns. Streaming DMD avoids storing the entire datasequence in memory by approximating the dynamic modes through incremen-tal updates with new available data. In this paper, we use sDMD to identify andextract dominant spatiotemporal structures of different turbulent flows, requir-ing the analysis of large datasets. First, the efficiency and accuracy of sDMDare compared to the classical DMD, using a publicly available test dataset thatconsists of velocity field snapshots obtained by direct numerical simulation of awake flow behind a cylinder. Streaming DMD not only reliably reproduces themost important dynamical features of the flow; our calculations also highlightitsadvantageintermsoftherequiredcomputationalresources.Wesubsequentlyuse sDMD to analyse three different turbulent flows that all show some degreeof large-scale coherence: rapidly rotating Rayleigh–Bénard convection, horizon-tal convection and the asymptotic suction boundary layer (ASBL). Structures ofdifferent frequencies and spatial extent can be clearly separated, and the promi-nent features of the dynamics are captured with just a few dynamic modes. Insummary, we demonstrate that sDMD is a powerful tool for the identification ofspatiotemporal structures in a wide range of turbulent flows.

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Language(s): eng - English
 Dates: 2022-01-12
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1002/gamm.202200003
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Title: GAMM-Mitteilungen
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: e202200003 Start / End Page: - Identifier: ISSN: 0936-7195
ISSN: 1522-2608