Abstract The two-dimensional particle image velocimetry (PIV) data are inevitably contaminated by noise due to various imperfections in instrumentation or algorithm, based on which the well-established vortex identification methods often yield noise or incomplete vortex structure with a jagged boundary. To make up this deficiency, a novel method was proposed in this paper and the efficiency of the new method was demonstrated by its applications in extracting the two-dimensional spanwise vortex structures from 2D PIV data in open-channel flows. The new method takes up a single vortex structure by combining model matching and vorticity filtering, and successfully locates the vortex core and draws a streamlined vortex boundary. The new method shows promise as being more effective than commonly used schemes in open-channel flow applications.

中文翻译：

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一种从明渠流中二维粒子图像测速数据中提取展向涡旋的新方法

摘要 二维粒子图像测速（PIV）数据由于仪器或算法的各种不完善而不可避免地受到噪声的污染，基于此的完善的涡流识别方法经常产生噪声或不完整的具有锯齿状边界的涡流结构。为了弥补这一不足，本文提出了一种新方法，该方法在从明渠流中的二维 PIV 数据中提取二维展向涡结构的应用证明了该方法的有效性。新方法将模型匹配和涡量滤波相结合，占用单个涡结构，成功定位涡核，绘制流线型涡边界。新方法显示出比开放通道流应用中常用的方案更有效的前景。