Spectral proper orthogonal decomposition
Moritz Sieber, Kilian Oberleithner, Christian Oliver Paschereit

TL;DR
Spectral Proper Orthogonal Decomposition (SPOD) is a new method that enhances the extraction of coherent structures from fluid flow data, especially when these structures occur at low energies or multiple frequencies, surpassing traditional methods.
Contribution
The paper introduces SPOD, a novel technique combining POD and spectral constraints, enabling better separation of multi-frequency and low-energy flow structures in fluid dynamics data.
Findings
SPOD effectively separates coherent structures across multiple frequencies.
SPOD outperforms traditional POD and DMD in complex flow scenarios.
Application to various experimental flows demonstrates its robustness.
Abstract
The identification of coherent structures from experimental or numerical data is an essential task when conducting research in fluid dynamics. This typically involves the construction of an empirical mode base that appropriately captures the dominant flow structures. The most prominent candidates are the energy-ranked proper orthogonal decomposition (POD) and the frequency ranked Fourier decomposition and dynamic mode decomposition (DMD). However, these methods fail when the relevant coherent structures occur at low energies or at multiple frequencies, which is often the case. To overcome the deficit of these "rigid" approaches, we propose a new method termed Spectral Proper Orthogonal Decomposition (SPOD). It is based on classical POD and it can be applied to spatially and temporally resolved data. The new method involves an additional temporal constraint that enables a clear…
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