An efficient streaming algorithm for spectral proper orthogonal decomposition

Abstract

A streaming algorithm to compute the spectral proper orthogonal decomposition (SPOD) of stationary random processes is presented. As new data becomes available, an incremental update of the truncated eigenbasis of the estimated cross-spectral density (CSD) matrix is performed. The algorithm converges orthogonal sets of SPOD modes at discrete frequencies that are optimally ranked in terms of energy. We define measures of error and convergence, and demonstrate the algorithm's performance on two datasets. The first example is that of a high-fidelity numerical simulation of a turbulent jet, and the second optical flow data obtained from high-speed camera recordings of a stepped spillway experiment. For both cases, the most energetic SPOD modes are reliably converged. The algorithm's low memory requirement enable real-time deployment and allow for the convergence of second-order statistics from arbitrarily long streams of data.