Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition-Reference-Cited by-同舟云学术

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

Published:2022-01-12 Issue:1 Volume:45 Page:
ISSN:0936-7195
Container-title:GAMM-Mitteilungen
language:en
Short-container-title:GAMM-Mitteilungen

Author:

Yang Rui¹²,Zhang Xuan¹,Reiter Philipp¹,Lohse Detlef¹²,Shishkina Olga¹,Linkmann Moritz³

Affiliation:

1. Max Planck Institute for Dynamics and Self‐Organisation Göttingen Germany

2. Physics of Fluids Group, Max Planck Center for Complex Fluid Dynamics, MESA+ Institute and J.M. Burgers Center for Fluid Dynamics University of Twente AE Enschede The Netherlands

3. School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh Edinburgh UK

Publisher

Wiley

Subject

Applied Mathematics,General Physics and Astronomy,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/gamm.202200003

Reference65 articles.

1. Heat transfer and large scale dynamics in turbulent Rayleigh-Bénard convection

2. A parallel and streaming Dynamic Mode Decomposition algorithm with finite precision error analysis for large data

3. Koopman-mode decomposition of the cylinder wake

4. Low-dimensional model of turbulent mixed convection in a complex domain

5. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows

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