基于粒子图像分割的混合 PIV-PTV 算法

Particle image velocimetry (PIV) has become a major measurement tool in the field of aerodynamics due to its characteristic of non-intrusive field measurement. The velocity field distribution of complex flows is often non-uniform and it is difficult to uniformly seed tracer particles, which makes accurate PIV measurements difficult. Therefore, when applying the PIV cross-correlation algorithm to deal with the particle-sparse area, it is necessary to use a larger query window to reduce the uncertainty of measurement, at expense of low spatial resolution. On the other hand, particle tracking velocimetry (PTV) tracks the cross-frame displacement of a single tracer particle, which has a higher spatial resolution than PIV, but it is difficult to apply it to dense areas with high particle concentrations. To solve this problem, a hybrid PIV-PTV velocity measurement technique based on particle image segmentation is developed. Firstly, the local concentration field of the tracer particles is calculated, which is based on the Voronoi polygon. Then the particles are binary classified by the set concentration threshold, and the support vector machine based on Gaussian kernel function is used to find the optimal classification boundary, so as to realize the division of the particle dense area and sparse area of the particle image. Finally, both PIV and PTV were used to calculate the velocity fields in the two regions respectively, and the two results were combined into a complete output of the velocity field. The results show that the above method can automatically divide the particle sparse and dense regions in the particle image, and can effectively improve the spatial resolution of the velocity field measurement. When the method is applied the near-wall measurement of the turbulent boundary layer at Mach 6, it effectively overcomes the problem that the tracer particles are difficult to enter the near-wall region of the boundary layer due to the strong shear of high speed flow, and significantly improve the analytical ability of the near-wall flow.