The Study On Matching Technology Of Three Dimension Particle Image Velocimetry

Measurement of fluid information is very important in researches of fluid characters.In contrast with the touching single point measurement methods,techniques of particle image velocimetry(PIV) can be used to obtain instantaneous information over the entire field of view,while leaving the fluid flow undisturbed.It has thus gained a reputation as one of the most promising measurement techniques,and has been used in a wide range of research.According to the characters of particle images,matching methods of PIV systems have been studied based on binocular vision theory.When investigating the matching methods in image plane,according to the characters of fluid and tracing particles,the particle images and the matching structures of particles in the CCD(Charge Coupled Device) camera plane have been analyzed.Firstly,distributions of tracing particles are not well-proportioned in the fluid field and the complex fluid field may have different region features.In order to improve the analysis capabilities,the fixed interrogation window technique of PIV is modified.The current results are averaged as estimations of next process.The size of the interrogation window is selected iteratively according to the correlation degree and the distributions of particles.Secondly,fluid is continuous and there exists high correlation among adjacent particles. A modified PIV method based on Kohonen self-organized mapping(SOM) neural network is presented.First,the results of cross-correlation are used to build networks.Second,tracking method is used to select matching points.The new PIV algorithm based on SOM network can reduce the dependence on particle density,intensity distribution and interrogation window size.Thirdly,false matching in particle tracking velocimetry(PTV) and low-pass feature of PIV will increase processing errors.To overcome the two problems,cell segmentation theory (CST) is presented in this paper according to the clustering characters of particle images. Furthermore,the processing model based on CST is described as:First,the interrogation fields are divided into different local spaces named as cells,and these cells continue to segment into sub-cells according to the correlation degree and neighbor degree.Second,these sub-cells compete against each other and the final victorious one attends competition in other fields.Third,the velocity vector field is gotten according to the position alteration of the victorious cell. While studying 3D matching methods in space,according to vision theory,the extracting methods of valid data in images and the matching methods have been analyzed.Firstly,the rigid motion hypothesis in PIV is studied because of the overlap problem of perspective particle images.The movable particle measurement constraints(MPMC) and the structure constraint based on three points are presented.MPMC is based on the nearest assumption and the adjacent particles with certain numbers are regarded as objects to match. The motion feature points and the motion feature lines are defined in MPMC.The motion feature lines with the same movement have the same feature point,which can be used to detect particle movements.Secondly,a new 3D measurement method based on binocular vision is proposed.Using the proposed method,problems of spatial stereo matching and information representation can be solved.A 3D space is first segmented and aligned to many 2D planes.And then 2D-PIV and PTV technologies are applied to different image frames which include the information of vision disparity.Thirdly,the 3D particle image matching method and optimal design are proposed based on genetic algorithm(GA).Disparity is aligned to 1-D data array and encoded.And then the sum of square difference(SSD) method or the sum of absolute difference(SAD) method is applied to evaluate the results.A modified genetic algorithm is employed to stereo matching. First,the crossover and mutation methods are modified;second,the sequences are made chaotic;third,the uniqueness is detected based on iterative algorithm.Fourthly,three matching structures,which are suitable for binocular PIV system with volume light,are presented and analyzed.Meanwhile,according to the characteristics of matching structures,a new matching method of data in two CCD images is proposed.Furthermore,based on the aforementioned matching techniques,the binocular PIV system is constructed.The post-processing method for 2D-PIV is modified in order to fit for the proposed 3D-PIV matching structure.In this paper,lots of particle images are tested and the errors are analyzed.These experiments demonstrate the effectiveness and practicability of the proposed methods and 3D-PIV system.