TiFA: A new LSPIV Post-Processing algorithm for river surface velocity measurement under low tracer density conditions

Abstract

The incapability of processing river surface flow velocities under low tracer density conditions is one of the limitations of the traditional Large-Scale Particle Image Velocimetry (LSPIV). This study developed a new LSPIV post-processing algorithm, Time Frequency Analysis (TiFA), to overcome such a limitation, enhance computational efficiency, and improve the accuracy of derived velocities. TiFA investigates the temporal joint distribution pattern of two velocity components at each location. By assuming that the valid velocities follow a quasi-normal distribution in the velocity time series, TiFA can quickly and accurately separate the valid velocities from background noise and outliers. The performance of TiFA was evaluated by comparing with other algorithms including Traditional LSPIV, Ensemble Correlation (EC), Large-Scale Particle Tracking Velocimetry (LSPTV), and traditional LSPIV pre-processed with Seeding Density Index (SDI) in an experimental hydraulic model and two field cases. TiFA showed the highest overall accuracy and lowest computation cost in data analysis, especially under low tracer density conditions. In addition, TiFA can automatically filter out velocity data from low-quality zones such as no-tracer zones and surface glare zones. TiFA also showed its ability in processing turbulent flow. In summary, TiFA demonstrated its great potential and competence of measuring river surface velocity under relatively low tracer density conditions, making it a valuable candidate for future applications.