An improved particle image velocimetry method for efficient flow analyses

This study proposes a spatiotemporally Adaptive Search Area (SA) size selecting algorithm for Particle image velocimetry (PIV), ASAPiv. The presented method releases the constraint of conventionally used static SAs, offering a significant computational performance increase through optimizing the dimensions of the SA according to the local flow conditions in a transient manner. The algorithm is implemented as a part of a new PTV framework, developed within the MATLAB environment. The most relevant steps of PIV and the related methods are reviewed, starting from image pre-processing up to the post-processing of raw Ply results. The performance of the proposed algorithm and the PIV tool in general is demonstrated through three examples of different nature, including a synthetic image sequence, a conventional, laser illuminated PIV case and a large-scale, field application. The dynamic alteration of the SAs is found to be consistent with the prevailing flow conditions, while the accuracy of NV in general is maintained. Total calculation times with static and dynamic SAs are compared. The benchmark cases highlight the relevance of adaptive SAs in cases of spatiotemporally varied flow conditions, where significant (up to 900%) computational performance increase is achieved. In case of unidirectional, steady flow conditions the method offers moderate speed-up compared to the employment of static SA sizes.