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Guide to Spectral Proper Orthogonal Decomposition

AIAA Journal · 2020 · Vol. 58(3) · pp. 1023–1033
Oliver T. SchmidtTim Colonius

Abstract

This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, or structures, in flow data. A specific algorithm based on estimating the cross-spectral density tensor with Welch's method is presented, and guidance is provided on selecting data sampling parameters and understanding tradeoffs among them in terms of bias, variability, aliasing, and leakage. Practical implementation issues, including dealing with large datasets, are discussed and illustrated with examples involving experimental and computational turbulent flow data.

Model Reduction and Neural NetworksControl Systems and IdentificationProbabilistic and Robust Engineering DesignProper orthogonal decompositionDecompositionComputer scienceMathematicsPhysicsMechanicsTurbulence

Funding

  • Office of Naval Research
Citations
558
FWCI
30.46
field-weighted impact
References
27
Percentile
100%
vs. same field & year
Citations per year
References
Random data: Analysis and measurement procedures
Mechanical Systems and Signal Processing · 1987 · 2,366 citations
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory · 1990 · 6,394 citations
Dynamic mode decomposition of numerical and experimental data
Journal of Fluid Mechanics · 2010 · 5,551 citations
Modal Analysis of Fluid Flows: An Overview
AIAA Journal · 2017 · 1,780 citations
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