Streaming Spectral Proper Orthogonal Decomposition

A low-memory streaming algorithm for spectral proper orthogonal decomposition (SPOD) of stationary random data

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