Adaptive large-scale particle image velocimetry method for physical model experiments of flood propagation with complex flow patterns

Abstract

This work presents an Adaptive Large Scale Particle Image Velocimetry method (ALSPIV), which measures surface velocity of a physical model experiment with complex flow pattern. In the experiment, the eleven low-cost, high-resolution surveillance cameras are adopted to achieve a large-scale flow field in real time. The lenses of them are shined perpendicularly on water surface to minimize perspective distortion. A camera calibration method is also designed to enhance measurement accuracy. In addition, an adaptive cross-correlation algorithm can contribute to cross-correlation and a lower signal-to-noise ratio. Finally, an experimental method is employed to verify the ALSPIV method’s accuracy, and the surface velocity distributions under three different steady flows are measured to demonstrate its applicability. Results show that the method is demonstrated to be a low-cost and automatic flow diagnostic tool and an accurate means of measuring surface velocities in the physical model experiments of flood propagation with complex flow patterns.