A Particle Based Approach For Improved Resolution PIV And TOMO-PIV Based On The Coherent Point Drift And The Affine Least-Squares Transformation

Particle Image Velocimetry (PIV) is a widely used method for flow diagnostics, but there is still potential for improvement, particularly in terms of velocity gradient estimation and computational cost when considering three-dimensional problems. This paper presents a framework that combines a Particle Tracking Velocity (PTV) approach with local gradient-based Eulerian reconstruction to improve PIV performance. The approach uses the Coherent Point Drift (CPD) method for particle pairing and introduces the Affine Least-Squares Transformation (ALST) for local deformation gradient estimation. The CPD method consists of pairing particles whose positions at two successive instants have been obtained from the images. The ALST estimates local deformation gradients, allowing the Eularian reconstruction of the velocity field. The effectiveness of the proposed method compared to traditional PIV algorithms is demonstrated by synthetic test cases in both 2D and 3D configurations. In 2D cases, the CPD+ALST approach outperforms standard PIV methods, especially in capturing local velocity gradients. In 3D cases, comparisons with TOMO-PIV show the improved performance of CPD+ALST in the context of locally large velocity gradients. However, the linear nature of ALST makes it less efficient than quadratic binning techniques. The study demonstrates the potential of CPD+ALST to improve velocity field reconstruction in complex flows.