Recurrence-based identification of coherent motion from very-sparse Lagrangian particle trajectories

The identification of coherent motion is a fundamental issue for the characterization and control of fluid flows. So far, several techniques have been proposed to extract coherent motion from a (turbulent) flow, either adopting an Eulerian or a Lagrangian framework. In a large variety of cases, e.g. atmospheric or oceanic flows, the Lagrangian viewpoint represents a more suitable choice for the spatio-temporal investigation of the flow. However, in practice, only a limited number of tracers is typically available, leading to (very-)sparse Lagrangian datasets. In this work, we exploit a recurrence network strategy to identify coherent motion from very-sparse particle trajectories. Specifically, a network is built on a single tracer trajectory, adopting a distance-based criterion of recurrence. The proposed methodology is objective (i.e., frame-independent), applicable to 2D and 3D data, and can be used for broken trajectories (as typically obtained in experiments via particle tracking tools). The recurrence-based approach is tested on a set of numerical and experimental test cases, showing its potential in identifying coherent motion for very-sparse particle trajectories.