Dynamic Mode Decomposition of Numerical and Experimental Data¶
Authors: Peter J. Schmid, Jörn Sesterhenn
Published: 2008 (Journal Paper)
Source: Journal of Fluid Mechanics
Algorithm: DMD
DOI: 10.1017/S0022112010001217
Summary¶
Introduces Dynamic Mode Decomposition (DMD), a data-driven algorithm for creating dynamic models from numerical simulations and experimental data, decomposing the observed trajectory data into modes each associated with a single frequency and growth/decay rate. Naturally allows for model reduction based on quantitative measures of mode importance. Originally proposed for fluid flow data, but relevant much more broadly to any kind of dynamical system, especially those observable with stochastic noise present and those with many states.
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.
Links¶
Primary
Standard
Alternate
Tags¶
-
Dynamic mode decomposition
-
DMD
-
Fluid dynamics
-
Data-driven methods
-
System identification
-
Model reduction