| Velocity measurement of fluid flows in an entire area rather than at a single point has been achieved thanks to the fast development in computer science and image processing technique. It could analyze the information of flow motion by capturing images of different time which are captured by laser beam. Particle image velocimetry technique has been widely used in various fields, providing a powerful tool for the transient flow field visualization. In order to analyze the flow field of particle image, the main works are as follows:1. This thesis introduces the basic concept, application background and basic methods about particle image velocimetry. At present, there are two main velocity measurement methods, namely, PIV(Particle Image Velocimetry) and PTV(Particle Tracking Velocimetry). In this thesis, the composition of current particle tracking velocimetry system is introduced. Algorithms about the main steps of particle tracking velocimetry, including particle identification, location, matching and post-processing are analyzed and studied.2. PIV method employs a locally windowed cross-correlation between two successive images to determine the displacement of particle pattern. The local velocity estimation tends to be biased towards the velocity of the largest and brightest particles within the pattern. PTV, unlike PIV, relies on the direct tracking of individual particles between successive images. PTV performs more accurately and effectively about flow movement at particle location. By using PIV and PTV on the same two standard particle images respectively, it can be learned that PTV performs more accurately about local flow information at particle location. So PTV that tracks single particles directly is the research emphasis in this thesis. Particle identification is the precondition of the effective subsequent processing of PTV. With the fact that the particle area always shows a characteristic of the local maximum brightness, a local maxima finding method is combined to particle recognition in uneven illumination, noisy image. This method looks for local brightness maximum points to determine the candidate particle coordinates. Contrasted with the segmentation method, this method has higher rate of recognition and keeps the information of particle size. Particle matching algorithm is the core step of the PTV method. The thesis emphasizes an optimal neighbor matching algorithm based on joint probability distribution. By dividing the whole combinations to small sub-networks, the complexity of calculating is reduced. Some artificial particle images of two representative flow fields in different densities are synthesized. Then the experiments on synthetic images prove the effectiveness of the improved optimal neighbor algorithm.3. In the end, a particle tracking velocimetry software system is developed integrated the above methods and it is applied on the measurement of flame flow. By adjusting the parameters of the methods, transient displacement vector diagram of the whole flame flow field is obtained. The data provide information for subsequent calculations of flame flow field.The research of particle image tracking is in rapidly developing stage now. The content of this thesis also has defects. How to improve the quality of images, identify particles accurately, match particles effectively and confirm the matching vector effectively to get the real flow velocity vector according to the characteristics of different flow field still need further research. |
