Feature Extraction from Turbulent Channel Flow of Moderate Reynolds Number via Composite DMD Analysis

Abstract

In this contribution, we described a Dynamic Mode Decomposition (DMD) analysis of a turbulent channel flow database at a moderate friction Reynolds number Reτ ≈ 950. More specifically, a composite-based DMD analysis was conducted, employing hybrid snapshots assembled by skin friction Cf (tk ) and either instantaneous Reynolds stress or streamwise velocity fluctuation fields. The DMD modes thus obtained were sorted according to its relevance to the Cf : less than 2% of the modes suffice to reconstruct accurately either the streamwise velocity or the Reynolds stress profiles near the wall. Furthermore, we aim to extend our preliminary work on the analysis of the turbulent database, by considering snapshots encompassing a larger spatial subdomain and covering a longer temporal span. However, this study involved data matrices significantly larger than that one, which the memory footprint of this problem exceeds a typical workstation. Accordingly, we have resorted to the parallel, memory distributed DMD algorithm as a reinforcement. With this enhanced composite DMD algorithm, flow features of moderate and even large turbulent channel problems could be identified and characterized.