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

Hou, Jingming; Yang, Lu; Wang, Xinhong; Chai, Jie; Li, Xuan; Shao, Junpeng; Du, Ying’en; Bai, Guohua

doi:10.1016/j.measurement.2022.111309

Cited by 2 publications

(1 citation statement)

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“…LSPIV provides detailed information about velocity vector angles and magnitudes, capturing the spatial variability along the river from the video data [5,6]. In engineering demonstrations, LSPIV has shown promising results, with an error value of 10% when compared to micro-propeller ADCP and numerical simulations [7]. Additionally, LSPIV measurements exhibit good agreement with ADCP velocities, with a deviation of ±10% [8].…”

Section: Introductionmentioning

confidence: 98%

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

Wijaya,

Liu,

Suharyanto

et al. 2023

Water

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The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

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