Multiscale proper orthogonal decomposition (mPOD) of TR-PIV data—a case study on stationary and transient cylinder wake flows

Abstract

Data-driven decompositions of particle image velocimetry (PIV) measurements are widely used for a variety of purposes, including the detection of coherent features (e.g. vortical structures), filtering operations (e.g. outlier removal or random noise mitigation), data reduction and compression. This work presents the application of a novel decomposition method, referred to as multiscale proper orthogonal decomposition (mPOD, Mendez et al 2019) to time-resolved PIV (TR-PIV) measurement. This method combines multiresolution analysis and standard proper orthogonal decomposition (POD) to achieve a compromise between decomposition convergence and spectral purity of the resulting modes. The selected test case is the flow past a cylinder in both stationary and transient conditions, producing a frequency-varying Karman vortex street. The results of the mPOD are compared to the standard POD, the discrete Fourier transform and the dynamic mode decomposition. The mPOD is evaluated in terms of decomposition convergence and time-frequency localization of its modes. The multiscale modal analysis allows for revealing beat phenomena in the stationary cylinder wake, due to the three-dimensional nature of the flow, and to correctly identify the transition from various stationary regimes in the transient test case.