Visualization and Selection of Dynamic Mode Decomposition Components for Unsteady Flow
This work provides improved methods for analyzing unsteady flow data using DMD, benefiting researchers and engineers working with fluid dynamics simulations and experiments by offering clearer insights and better component selection.
The paper addresses drawbacks in the conventional use of Dynamic Mode Decomposition (DMD) components for unsteady flow analysis. By adjusting the components and providing new representations, the authors improve the description of flow, remove redundancies, and clarify component interplay, enabling two clustering methods for physically relevant flow segmentation and component selection.
Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data. Conceptually, DMD performs a low-dimensional spectral decomposition of the data into the following components: The modes, called DMD modes, encode the spatial contribution of the decomposition, whereas the DMD amplitudes specify their impact. Each associated eigenvalue, referred to as DMD eigenvalue, characterizes the frequency and growth rate of the DMD mode. In this paper, we demonstrate how the components of DMD can be utilized to obtain temporal and spatial information from time-dependent flow fields. We begin with the theoretical background of DMD and its application to unsteady flow. Next, we examine the conventional process with DMD mathematically and put it in relationship to the discrete Fourier transform. Our analysis shows that the current use of DMD components has several drawbacks. To resolve these problems we adjust the components and provide new and meaningful insights into the decomposition: We show that our improved components describe the flow more adequately. Moreover, we remove redundancies in the decomposition and clarify the interplay between components, allowing users to understand the impact of components. These new representations ,which respect the spatio-temporal character of DMD, enable two clustering methods that segment the flow into physically relevant sections and can therefore be used for the selection of DMD components. With a number of typical examples, we demonstrate that the combination of these techniques allow new insights with DMD for unsteady flow.