Vortex identification methods based on temporal signal-processing of time-resolved PIV data

The lack of a universally accepted mathematical definition of a vortex structure has led to a considerable number of Eulerian criteria to identify coherent structures. Most are derived from the instantaneous local velocity gradient tensor and its derivatives and require appropriate thresholds to extract the boundaries of the structures. Notwithstanding their great potential for studying coherent structures, most criteria are not frame-independent and they lack a clear physical meaning. The Lyapunov exponent, a popular tool in dynamical system theory, appears as a promising alternative. This Lagrangian criterion does not suffer from the drawbacks of the Eulerian criteria and is constructed on a simple physical interpretation that includes information on the history of the flow. However, since the computation of the Lyapunov exponent involves the knowledge of fluid particle trajectories, experimental applications are currently restricted to laminar flows and two-dimensional turbulence, provided that velocity fields are time-resolved. In this work, we explore temporal post-treatment methods to extract vortical structures developing in a flow through a smooth axisymmetric constriction. Data from planar time-resolved Particle image velocimetry, measuring two or three components of the velocity vectors, are transformed via the Taylor hypothesis to quasi-instantaneous three-dimensional velocity field and are interpreted in terms of the discrete wavelet decomposition, the finite-time Lyapunov exponent, and the linear stochastic estimation. It appears that these methods can concurrently provide very rich and complementary scalar fields representing the effects of the vortical structures and their interactions in the flow.