粒子图像测速技术作为一种新的流场测速方法能够在不干扰流场的情况下获得整个流场的速度信息。粒子图像测速技术最关键的步骤在于粒子匹配。针对粒子密度分布不均匀、流场不同等实际情况,提出了混合算法,即结合互相关和松弛算法能够更准确地搜索粒子,进而对粒子进行匹配。对3种匹配算法的匹配概率进行比较分析,发现混合算法能更准确地分析粒子的运动状态,减少错误矢量的产生;另外,对松弛算法进行改进,通过优化筛选加权因子发现改进的松弛算法在运行速度上相比原始算法有了较大提高,匹配率与原始算法基本一致。

As a new method of flow velocity measurement, particle image velocimetry (PIV) could obtain velocity information of the whole flow field without disturbing the flow field. The most critical step in PIV is particle matching. A hybrid algorithm combining cross-correlation algorithm and relaxation algorithm is proposed in view of the actual conditions of uneven distribution of particle density and different flow fields. The hybrid algorithm could search the particles more accurately so as to match the particles. The matching probabilities of three matching algorithms are compared and results suggest that the hybrid algorithm can analyze the motion state of particles more accurately and reduce the generation of error vectors. In addition, the relaxation algorithm is improved in this paper. By optimizing weighting factor, the running speed of the improved relaxation algorithm has greatly improved compared with the original algorithm, while the matching rate is basically consistent with the original algorithm.