We present the first general metric for attractor overlap (MAO) facilitating an unsupervised comparison of flow data sets. The starting point is two or more attractors, i.e., ensembles of states representing different operating conditions. The proposed metric generalizes the standard Hilbert-space distance between two snapshots to snapshot ensembles of two attractors. A reduced-order analysis for big data and many attractors is enabled by coarse-graining the snapshots into representative clusters with corresponding centroids and population probabilities. For a large number of attractors, MAO is augmented by proximity maps for the snapshots, the centroids, and the attractors, giving scientifically interpretable visual access to the closeness of the states. The coherent structures belonging to the overlap and disjoint states between these attractors are distilled by few representative centroids.We employ MAO for two quite different actuated flow configurations: a two-dimensional wake with vortices in a narrow frequency range and three-dimensional wall turbulence with broadband spectrum. In the first application, seven control laws are applied to the fluidic pinball, i.e., the two-dimensional flow around three circular cylinders whose centers form an equilateral triangle pointing in the upstream direction. These seven operating conditions comprise unforced shedding, boat tailing, base bleed, high-and low-frequency forcing as well as two opposing Magnus effects. In the second example, MAO is applied to three-dimensional simulation data from an open-loop drag reduction study of a turbulent boundary layer. The actuation mechanisms of 38 spanwise traveling transversal surface waves are investigated. MAO compares and classifies these actuated flows in agreement with physical intuition. For ‡ Present address : 2 R. Ishar and others instance, the first feature coordinate of the attractor proximity map correlates with drag for the fluidic pinball and for the turbulent boundary layer. MAO has a large spectrum of potential applications ranging from a quantitative comparison between numerical simulations and experimental particle-image velocimetry data to the analysis of simulations representing a myriad of different operating conditions.

We introduce a novel data-driven reduced-order modeling approach, a Cluster-Based Network Model (CBNM). Starting point is a set of time-resolved snapshots associated with one or multiple control laws. These snapshots are coarse-grained into dozens of centroids using k-means++ clustering. The dynamics is modelled in a network between these centroids comprising the transition probability and corresponding transit time. The transition parameters depend on the control law. CBNM is successfully applied to an actuated turbulent boundary layer flow. The results show that CBNM is an attractive alternative to POD models as the model is human interpretable and dynamically robust by construction.