Application of proper orthogonal decomposition in particle image velocimetry in ocean engineering

Abstract

Particle image velocimetry (PIV) plays a vital role in flow measurement. Eliminating the effects of overexposure and shadow in particle images is an urgent problem to be solved in the detailed flow measurement of ocean engineering. Seek a robust denoising method for bad test environments, and apply it to the PIV experiments to achieve dual denoising at the image preprocessing and data postprocessing. This study verified the superiority of the Proper Orthogonal Decomposition (POD) filter in pre- and postprocessing compared with traditional mathematical filters. Subsequently, the POD filter is applied to a practical project: the wake field measurement of bulk carrier. Regarding preprocessing, the POD filter is a valuable tool for objectively filtering typical high-frequency spatial noise from the energy perspective based on preserving the original information of particles to the greatest extent, effectively alleviating the impact of overexposure and shadow areas. Regarding postprocessing, the POD filter can fill the data gap by considering the surrounding flow characteristics. At the same time, the flow's spatial distribution and time evolution characteristics can be obtained by POD. The flow reconstructed by selecting the appropriate order model can clearly show the evolution characteristics of large-scale coherent structures, which may provide a deep understanding of complex flow field mechanisms.