Adaptive PIV algorithm based on seeding density and velocity information

An adaptive particle image velocimetry (PIV) processing algorithm to increase local spatial resolution and obtain accurate results is presented. Adaptive sampling criterion is based on seeding density and the analysis of velocity information. The proposed methodology places more measurement points in the region where high seeding density exists or flow parameters vary drastically. This methodology is effective in PIV image processing, especially in non-optimal PIV experimental conditions. The working principle of the adaptive processing method in this paper is to generate spring force on the bias of the deviation between the ideal and actual distances of measurement points. Under artificial force, the sampling points are moved to desirable places to satisfy sampling density function. Sampling density function is important in processing and ingeniously conceived based on the combination of vorticity and velocity gradients. The viability of the method is elaborated through synthetic and experimental tests. The synthetic test verifies the effectiveness of the adaptive processing on the basis of flow information. The bias and random error can be reduced by 7.3% and 4.0%, respectively. The result of the experimental test shows that the sampling points and the interrogation window size can be arranged according to the spatial distribution of seeding density and the actual flow features. Compared with iterative window deformation method, spatial resolution is locally enhanced, and the robustness and reliability of the result are increased, particularly in poor seeding regions.