基于维诺图匹配的粒子跟踪测速法
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摘要
针对密集颗粒流速度场分布的测量问题,提出了基于维诺图匹配的粒子跟踪测速法。首先,通过对图像粒子进行维诺图构建,给出面积相似度筛选匹配粒子的条件;其次,引入Delaunay三角网搜索结构,通过计算维诺多边形的形状相似度来匹配粒子;再次,研究了去除错误匹配粒子矢量的方法和匹配算法中的关键参数;最后,通过模拟二维旋转流场运动以及二维转盘中的颗粒流实验对算法进行了测试。结果表明:维诺图匹配的匹配准确率高于DT-PTV并且在处理密集粒子匹配效果上更好;维诺图匹配算法适用于测量密集颗粒流速度场分布,颗粒匹配准确率高达99%,并由得到的颗粒流速度场分布验证了算法的有效性。
Based on the method of optical measurement,a particle tracking velocimetry algorithm based on Voronoi diagram matching is proposed to solve the problem of velocity field distribution of dense particle flow.First,Voronoi diagram is constructed by the location of particles,and then the conditions for particles matching with the area similarity are given.Then,the Delaunay triangulation search structure is introduced,and the shape similarity of the Voronoi polygons is calculated to match the particles.Then,the removal error matching particle vector method and matching algorithm of the key parameters are investigated.Finally,the algorithm was tested by simulating two-dimensional rotating flow field and the particle flow experiment in the two-dimensional turntable.The results show that firstly the matching accuracy of the Voronoi diagram matching algorithm is higher than that of the DT-PTV that is only using the area similarity,and Voronoi diagram matching algorithm has a better matching effect on dealing with the dense particles.Secondly,the Voronoi diagram matching algorithm is suitable for measuring the velocity field distribution of dense particle flow.Particle matching accuracy is as high as 99%,and based on the particle flow velocity field distribution,the effectiveness of the method is verified.
Based on the method of optical measurement,a particle tracking velocimetry algorithm based on Voronoi diagram matching is proposed to solve the problem of velocity field distribution of dense particle flow.First,Voronoi diagram is constructed by the location of particles,and then the conditions for particles matching with the area similarity are given.Then,the Delaunay triangulation search structure is introduced,and the shape similarity of the Voronoi polygons is calculated to match the particles.Then,the removal error matching particle vector method and matching algorithm of the key parameters are investigated.Finally,the algorithm was tested by simulating two-dimensional rotating flow field and the particle flow experiment in the two-dimensional turntable.The results show that firstly the matching accuracy of the Voronoi diagram matching algorithm is higher than that of the DT-PTV that is only using the area similarity,and Voronoi diagram matching algorithm has a better matching effect on dealing with the dense particles.Secondly,the Voronoi diagram matching algorithm is suitable for measuring the velocity field distribution of dense particle flow.Particle matching accuracy is as high as 99%,and based on the particle flow velocity field distribution,the effectiveness of the method is verified.
引文
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[12]FORTUNE S.Voronoi diagrams and delaunay triangulations[M].Computing in Euclidean Geometry,2015:193-233.
[13]DING S G,NIE X L,QIAO H,et al.A fast algorithm of convex hull vertices selection for online classification[J].IEEE Transactions on Neural Networks and Learning Systems,2017,29(4):792-806.
[14]贾攀,王元,张洋.基于Delaunay网格技术的松弛迭代粒子追踪算法[J].空气动力学学报,2012,30(6):792-797.
[15]欧阳鸿武,张新,黄誓成,等.转鼓中颗粒流动层厚度及其影响因素[J].粉末冶金材料科学与工程,2008,13(2):84-90.
[16]DU PONT S C,FISCHER R,GONDRET P,et al.Instantaneous velocity profiles during granular avalanches[J].Physical Review Letters,2005,94(4):048003.
[17]JAIN N T,OTTINO J M,LUEPTOW R M.An experimental study of the flowing granular layer in a rotating tumbler[J].Physics of Fluids,2002,14(2):572-582.
[2]雷煜华.火焰流场粒子跟踪测速系统[D].成都:西安电子科技大学,2014.
[3]ORPE A V,KHAKHAR D V.Rheology of surface granular flows[J].Journal of Fluid Mechanics,2007,571:1-32.
[4]FU S J,BIWOLE P H,MATHIS C.Numerical and experimental comparison of 3DParticle Tracking Velocimetry(PTV)and Particle Image Velocimetry(PIV)accuracy for indoor airflow study[J].Building and Environment,2016,100:40-49.
[5]王甜,房红兵,黄海龙,等.一种改进的粒子图像测速混合算法研究[J].长江科学院院报,2017,34(7):144-148.
[6]杨福胜,张早校,王斯民,等.粒子追踪测速(PTV)技术及其在多相流测试中的应用[J].流体机械,2014(2):37-42.
[7]胡涛.PTV匹配算法的对比分析[D].北京:清华大学,2010.
[8]阮晓东,宋向群,山本富士夫.基于BICC算法的PIV技术[J].实验力学,1998,13(4):514-519.
[9]周云龙,宋连壮,周红娟.基于粒子群优化Hopfield网络匹配的稀相颗粒速度测量[J].化工学报,2011,62(2):348-354.
[10]SONG X,YAMAMOTO F,IGUCHI M,et al.A new tracking algorithm of PIV and removal of spurious vectors using Delaunay tessellation[J].Experiments in Fluids,1999,26(4):371-380.
[11]ZHANG Y,WANG Y,JIA P.Improving the Delaunay tessellation particle tracking algorithm in the threedimensional field[J].Measurement,2014,49:1-14.
[12]FORTUNE S.Voronoi diagrams and delaunay triangulations[M].Computing in Euclidean Geometry,2015:193-233.
[13]DING S G,NIE X L,QIAO H,et al.A fast algorithm of convex hull vertices selection for online classification[J].IEEE Transactions on Neural Networks and Learning Systems,2017,29(4):792-806.
[14]贾攀,王元,张洋.基于Delaunay网格技术的松弛迭代粒子追踪算法[J].空气动力学学报,2012,30(6):792-797.
[15]欧阳鸿武,张新,黄誓成,等.转鼓中颗粒流动层厚度及其影响因素[J].粉末冶金材料科学与工程,2008,13(2):84-90.
[16]DU PONT S C,FISCHER R,GONDRET P,et al.Instantaneous velocity profiles during granular avalanches[J].Physical Review Letters,2005,94(4):048003.
[17]JAIN N T,OTTINO J M,LUEPTOW R M.An experimental study of the flowing granular layer in a rotating tumbler[J].Physics of Fluids,2002,14(2):572-582.