Recursive dynamic mode decomposition of a transient cylinder wake

Abstract

A novel data-driven modal decomposition of fluid flow is proposed comprising key features of POD and DMD. The first mode is the normalized real or imaginary part of the DMD mode which minimizes the time-averaged residual. The N-th mode is defined recursively in an analogous manner based on the residual of an expansion using the first N-1 modes. The resulting recursive DMD (RDMD) modes are orthogonal by construction, retain pure frequency content and aim at low residual. RDMD is applied to transient cylinder wake data and is benchmarked against POD and optimized DMD (Chen et al. 2012) for the same snapshot sequence. Unlike POD modes, RDMD structures are shown to have pure frequency content while retaining a residual of comparable order as POD. In contrast to DMD with exponentially growing or decaying oscillatory amplitudes, RDMD clearly identifies initial, maximum and final fluctuation levels. Intriguingly, RDMD outperforms both POD and DMD in the limit cycle resolution from the same snaphots. RDMD is proposed as an attractive alternative to POD and DMD for empirical Galerkin models, with nonlinear transient dynamics as a niche application.