PIV速度场坏矢量的本征正交分解处理技术

介绍了一种针对粒子图像测速(PIV)基于本征正交分解(POD)的速度场后处理技术.该技术改变了现在后处理技术将速度场坏矢量识别和修正分开实现的局面,通过迭代方法有效地实现了速度场坏点统一的识别和修复算法.算法利用POD分解的低阶模态信息重构出可以用于坏矢量识别的参考速度场,利用该参考速度场对全流场进行坏点识别并完成修正.通过对一套光滑的PIV速度场数据引入高斯分布的随机误差,测试验证了该POD方法的优越性.在坏矢量识别方面新方法较归一化中值检验有更高的正确性,能识别大面积出现的坏矢量区域.在坏矢量修补的插值算法中,新方法的计算效率又高于传统Gappy POD方法,且计算精度优于常见的矢量场内插数学方法.特别是在数据缺失的大连通区域,该方法对物理流场有很好的预测效果.

On the Proper Orthogonal Decomposition Technique for PIV Post-Processing

A new technique for data post-processing of particle image velocimetry (PIV) based on proper orthogonal decomposition (POD) is introduced in this paper. This technique changes the situation of current data post-processing that separately achieves the identification of spurious vector in velocity field and its correction. Through iterative method, the unification of identification of dead pixels in velocity field and its repairment algorithm is effectively achieved. A reference velocity filed is reconstructed from the lower order mode of POD for identification of spurious vectors. Based on the reference velocity field, the identification and repairment of dead pixels are achieved in the whole field. Through introducing Gaussian distribution of random error for velocity field data of smooth PIV, a test measurement verifies the superiority of the proposed POD method. Results show that for spurious vector identification, the new method presents better performance than that of normalized median test, including the identification of spurious vector in a large area; while for interpolation algorithm of spurious vector repairement, it presents higher calculation efficiency than that of traditional Gappy POD and higher accuracy than that of common vector field interpolation methods. Especially, this method can provide good prediction for original flow field in a large connected and area, where the data are missing.