Automated critical point identification for PIV data using multimodal particle swarm optimization

A hybrid sequential niche algorithm is used for the automated identification of critical points of velocity fields. This method combines an adaptive sequential niche technique with deterministic local optimization to detect critical points: focus, node and saddle points. A particle swarm algorithm performs a global search whereas vortex core identification functions compute the precise location as the extremum of the corresponding function. Once a critical point is found, a rectangular niche is constructed around the point. The particle swarm then proceeds to explore different regions of the velocity field. The process advances sequentially, avoiding areas near previously found critical points by blocking niches obtained from previous steps. The niche size is automatically adjusted each time a search enters inside an existing niche. Vortex core functions are used for critical point identification and calculating its precise location inside each niche. The procedure is validated on particle image velocimetry data obtained with two types of flows, an impinging jet flow and a flow downstream of a model building. The hybrid algorithm proved to be very efficient and robust for automated detection and identification of critical points. It can be used as a first step for studying the time‐dependent dynamic behavior of instantaneous velocity fields by tracking topological critical points. This is the first study that uses a multi‐modal particle swarm algorithm for critical point identification. Copyright © 2011 John Wiley & Sons, Ltd.