Spectral analysis of nonlinear flows

Clarence W. Rowley, Igor Mezić, Shervin Bagheri, Philipp Schlatter, Dan S. Henningson

Journal of Fluid Mechanics · 2009 · 2.3K citations · 11 references

Concepts

TL;DR

Koopman modes generalize global eigenmodes of linearized systems by possessing temporal frequencies and growth rates, offering an alternative to proper orthogonal decomposition and reducing to a discrete temporal Fourier transform for periodic data. The study introduces a technique that decomposes complex nonlinear flows into Koopman modes derived from spectral analysis of the Koopman operator. Koopman modes are extracted from data via a variant of the Arnoldi method, which is equivalent to dynamic mode decomposition, enabling direct computation from numerical or experimental observations. Applied to a jet‑in‑crossflow example, the method successfully captures dominant frequencies and reveals the corresponding spatial structures.

Abstract

We present a technique for describing the global behaviour of complex nonlinear flows by decomposing the flow into modes determined from spectral analysis of the Koopman operator, an infinite-dimensional linear operator associated with the full nonlinear system. These modes, referred to as Koopman modes, are associated with a particular observable, and may be determined directly from data (either numerical or experimental) using a variant of a standard Arnoldi method. They have an associated temporal frequency and growth rate and may be viewed as a nonlinear generalization of global eigenmodes of a linearized system. They provide an alternative to proper orthogonal decomposition, and in the case of periodic data the Koopman modes reduce to a discrete temporal Fourier transform. The Arnoldi method used for computations is identical to the dynamic mode decomposition recently proposed by Schmid & Sesterhenn ( Sixty-First Annual Meeting of the APS Division of Fluid Dynamics , 2008), so dynamic mode decomposition can be thought of as an algorithm for finding Koopman modes. We illustrate the method on an example of a jet in crossflow, and show that the method captures the dominant frequencies and elucidates the associated spatial structures.

References

11