Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition. Issue 1 (12th January 2022) - Yang, Rui; Zhang, Xuan; Reiter, Philipp; Lohse, Detlef; Shishkina, Olga; Linkmann, Moritz
Abstract: Streaming Dynamic Mode Decomposition (sDMD) is a low‐storage version of dynamic mode decomposition (DMD), a data‐driven method to extract spatiotemporal flow patterns. Streaming DMD avoids storing the entire data sequence in memory by approximating the dynamic modes through incremental updates with new available data. In this paper, we use sDMD to identify and extract dominant spatiotemporal structures of different turbulent flows, requiring the analysis of large datasets. First, the efficiency and accuracy of sDMD are compared to the classical DMD, using a publicly available test dataset that consists of velocity field snapshots obtained by direct numerical simulation of a wake flow behind a cylinder. Streaming DMD not only reliably reproduces the most important dynamical features of the flow; our calculations also highlight its advantage in terms of the required computational resources. We subsequently use sDMD to analyse three different turbulent flows that all show some degree of large‐scale coherence: rapidly rotating Rayleigh–Bénard convection, horizontal convection and the asymptotic suction boundary layer (ASBL). Structures of different frequencies and spatial extent can be clearly separated, and the prominent features of the dynamics are captured with just a few dynamic modes. In summary, we demonstrate that sDMD is a powerful tool for the identification of spatiotemporal structures in a wide range of turbulent flows.