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Streaming Spectral Proper Orthogonal Decomposition

version 1.0.0 (10.4 MB) by Oliver Schmidt
A low-memory streaming algorithm for spectral proper orthogonal decomposition (SPOD) of stationary random data

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Updated 11 Jan 2019

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A streaming algorithm to compute the spectral proper orthogonal decomposition (SPOD) of stationary random processes. As new data becomes available, an incremental update of the truncated eigenbasis of the estimated cross-spectral density (CSD) matrix is performed. The algorithm requires access to only one temporal snapshot of the data at a time and converges orthogonal sets of SPOD modes at discrete frequencies that are optimally ranked in terms of energy. The algorithm’s low memory requirement enables real-time deployment and allows for the convergence of second-order statistics from arbitrarily long streams of data.

A detailed description of the algorithm and the example (high-fidelity numerical simulation data of a turbulent jet) can be found in:
Schmidt, O. T., and A. Towne. “An Efficient Streaming Algorithm for Spectral Proper Orthogonal Decomposition.” Computer Physics Communications, Nov. 2018, https://doi.org/10.1016/j.cpc.2018.11.009

Cite As

Schmidt, Oliver T., and Aaron Towne. “An Efficient Streaming Algorithm for Spectral Proper Orthogonal Decomposition.” Computer Physics Communications, Elsevier BV, Nov. 2018, doi:10.1016/j.cpc.2018.11.009.

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MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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