A model-based validation framework for PIV and PTV

The utility of particle image velocimetry (PIV) for measurement of velocity fields in many fluid flows is well established. This has created interest in overcoming difficulties with the technique when applied to increasingly larger fields of view where there exists a significant range of velocities and spatial velocity gradients are large. In this regard, a major deficiency with standard cross-correlation PIV is the inherent link between the density of vectors in the measurement field and the maximum measurable displacement. Several schemes exist to reduce this link. These iterative hierarchical/multiresolution schemes are strongly dependent on vector validation routines to remove spurious vectors. Here the design of a new framework for vector validation is described. This framework is general enough for use with both regular and irregularly spaced vector fields to make it applicable to particle image velocimetry (PIV), particle tracking velocimetry (PTV), and hybrid methods. It is based on the determination of a smoothed displacement field that robustly characterizes the measured field such that invalid vectors are attenuated more than valid vectors. In this particular study a thin-plate spline model is incorporated within an iterative regularized weighted least-squares routine to find a smoothed version of the displacement field that maintains pertinent velocity gradient information. The utility of the methodology is demonstrated for a complex flow profile containing four vortices where the valid displacement ranges from ?1/4 to +1/4 of the area of interest (AOI) dimension. Results indicate that this validation strategy can discriminate between valid and invalid vectors remarkably well over a range of parameter settings. In the example presented a flow field with significant velocity gradients and having a high number of invalid vectors (25%) is accurately validated.