一种应用于水流的纹影特性光流测速算法

A schlieren motion estimation method for water flow velocimetry

本论文基于光流优化算法,发展了一种适用于水流的纹影特性光流测速算法。在存在密度梯度的条件下,纹影图像的亮度反映了流场折射率的一阶导数,结合纹影亮度方程和流体连续性方程推导了适用于水的物理约束条件,采用二阶散度–旋度正则化作为空间平滑约束条件,基于两个约束条件构建了能量方程,通过变分法对能量方程进行最小化求解获得速度场。以热羽流为例,使用该算法对浮力羽流纹影图像进行了计算,并与互相关算法和传统光流算法的结果进行了比较。结果显示:本研究提出的算法能更好地体现流动特性,得到更高的空间分辨率。该方法基于纹影图像,无需在流场中添加示踪粒子,对流场无干扰,具有结构简单、使用方便等优点。

In the present study,a schlieren motion estimation algorithm is proposed for two-dimensional seedless water flow measurement.A physical constraint is derived combining the schlieren intensity and continuity equations.The space smoothness constraint adopts the second-order div-curl regularizer.Based on the two constraints,the global cost function is derived and minimized to resolve the velocity field using the variation method.As an example,a buoyant plume in the water tank is tested using a Z-type schlieren imaging system.The sample images are calculated using a correlation algorithm in PIVlab,an optimized optical flow algorithm,and the newly proposed schlieren motion estimation algorithm.The results show that the new algorithm can resolve more details of the flow field with higher spatial resolution,while the velocity gradient is consistent with the continuity characteristics.It also features with good robustness without obvious outlier mistakes.The schlieren setup is simple and cost effective in setup.The proposed algorithm has shown great potentials for velocity measurement in more complex configurations.