A Patch-Based Flow Field Reconstruction Method for Particle Image Velocimetry Data of Multistage Centrifugal Pumps

Abstract

Particle image velocimetry (PIV) technology, which performs the full-field velocity measurement on the laser plane, plays a crucial role in the study of complex flow structures in centrifugal pumps. In particle image cross-correlation analysis, the flow field could be corrupted with outliers, due to the background Gaussian noise of imaging, insufficient illumination caused by optical obstruction, and particle slip caused by centrifugal forces, etc. Here we propose a patch-based flow field reconstruction (PFFR) method for PIV data of centrifugal pump. Particularly, inspired by the fact that natural images contain a large number of mutually similar patches at different locations, the instantaneous PIV data with symmetric property are segmented to multiple patches. Flow field reconstruction is achieved by low-rank sparse decomposition, which exploiting the information of similar flow characteristics present in patches. To evaluate the effectiveness of PFFR, we illustrated PFFR on large eddy simulation (LES) vorticity field and experimental data of centrifugal pump, and three other data analysis methods were performed. We have demonstrated that for the instantaneous flow field with outliers, PFFR has faithful reconstruction ability to improve the reliability of data. When the outliers account for 20% of the total flow vectors, the average normalized root mean square error of PFFR-reconstructed data is 0.143, which is lower than three other data methods by 21.9% ~ 48.1%. The structural similarity is 0.702, which is higher than three other data methods by 2.1% ~ 9%.