Please wait a minute...
Chin. Phys. B, 2020, Vol. 29(1): 014207    DOI: 10.1088/1674-1056/ab5936
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS Prev   Next  

A novel particle tracking velocimetry method for complex granular flow field

Bi-De Wang(王必得)1, Jian Song(宋健)1, Ran Li(李然)2, Ren Han(韩韧)1, Gang Zheng(郑刚)2, Hui Yang(杨晖)1
1 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2 School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Abstract  Particle tracking velocimetry (PTV) is one of the most commonly applied granular flow velocity measurement methods. However, traditional PTV methods may have issues such as high mismatching rates and a narrow measurement range when measuring granular flows with large bulk density and high-speed contrast. In this study, a novel PTV method is introduced to solve these problems using an optical flow matching algorithm with two further processing steps. The first step involves displacement correction, which is used to solve the mismatching problem in the case of high stacking density. The other step is trajectory splicing, which is used to solve the problem of a measurement range reduction in the case of high-speed contrast The hopper flow experimental results demonstrate superior performance of this proposed method in controlling the number of mismatched particles and better measuring efficiency in comparison with the traditional PTV method.
Keywords:  particle tracking velocimetry      optical flow      displacement correction      trajectory splicing  
Received:  28 September 2019      Revised:  16 October 2019      Accepted manuscript online: 
PACS:  42.30.-d (Imaging and optical processing)  
  47.11.-j (Computational methods in fluid dynamics)  
  47.57.Gc (Granular flow)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11572201 and 91634202).
Corresponding Authors:  Hui Yang     E-mail:  yanghui@usst.edu.cn

Cite this article: 

Bi-De Wang(王必得), Jian Song(宋健), Ran Li(李然), Ren Han(韩韧), Gang Zheng(郑刚), Hui Yang(杨晖) A novel particle tracking velocimetry method for complex granular flow field 2020 Chin. Phys. B 29 014207

[1] Gong J M, Yang H, Lin S H, Li R and Zivkovic V 2018 Powder Technol. 324 76
[2] Schaeper M and Damaschke N 2017 Meas. Sci. Technol. 28 055008
[3] Sharp K V and Adrian R J 2001 AICHE J. 47 766
[4] Jensen A and Pedersen G K 2004 Meas. Sci. Technol. 15 2275
[5] Shi S, Ding J, Atkinson C, Soria J and New T H 2018 Exp. Fluids 59 46
[6] Clauser J, Knieps M S, Büsen M, Ding A, Schmitz-Rode T, Steinseifer U, Arens J and Cattaneo G 2018 Ann. Biomed. Eng. 46 841
[7] Bolanos-Jimenez R, Rossi M, Fernandez Rivas D F, Kähler C J and Marin A 2017 J. Fluid Mech. 820 529
[8] Felix-Felix J R, Salinas-Tapia H, Bautista-Capetillo C, Garcia-Aragon J, Burguete J and Playan E 2017 Irrig. Sci. 35 515
[9] Ouellette N T, Xu H and Bodenschatz E 2006 Exp. Fluids 40 301
[10] Maas H G, Gruen A and Papantoniou D 1993 Exp. Fluids 15 133
[11] Fu S J, Biwole P H, Mathis C and Maissa P 2018 Indoor Built Environ. 27 528
[12] Baek S J and Lee S J 1996 Exp. Fluids 22 23
[13] Ferrari G and Poletto M 2002 Powder Technol. 123 242
[14] Balevicius R, Kacianauskas R, Mroz Z and Sielamowicz I 2011 Adv. Powder Technol. 22 226
[15] Ma L D, Yang G H, Zhang S, Lin P, Tian Y and Yang L 2018 Acta Phys. Sin. 67 044501 (in Chinese)
[16] Zhang S, Lin P, Yang G H, Wan J F, Tian Y and Yang L 2019 Chin. Phys. B 28 018101
[17] Wang Z W, Yang X K, Xu Y and Yu S Y 2009 Pattern Recognit. Lett. 30 407
[18] Horn B K P and Schunck B G 1981 Artif. Intell. 17 185
[19] Ahmine Y, Caron G, Mouaddib E and Chouireb F 2019 Image Vis. Comput. 88 1
[20] Liu Y, Xi D G, Li Z L and Hong Y 2015 J. Hydrol. 529 354
[21] Zhang D J, Xie N, Liang S and Jia J Y 2016 Pattern Recognit. Lett. 76 49
[22] Pinto A M, Costa P G, Correia M V, Matos A C and Moreira A P 2017 Robot. Auton. Syst. 87 1
[23] Ohmi K and Li H Y 2000 Meas. Sci. Technol. 11 603
[24] Masuda N, Ito T, Kayama K, Kono H, Satake S, Kunugi T and Sato K 2006 Opt. Express 14 587
[25] Qiao Y J, Tang Y C and Li J S 2013 International Conference on Measurement Information and Control August 16-18, 2013, Harbin, China, p. 1408
[26] Cruz-Santos W, Lopez-Garcia L and Redondo-Galvan A 2015 Opt. Eng. 54 054102
[27] Barron J L, Fleet D J and Beauchemin S S 1994 Int. J. Comput. Vis. 12 43
[28] Bouguet J Y 2001 Intel Corporation 5 1
[29] Cao S X, Jiang J, Zhang G J and Yuan Y 2013 Int. J. Remote Sens. 34 2301
[1] Near-field multiple super-resolution imaging from Mikaelian lens to generalized Maxwell's fish-eye lens
Yangyang Zhou(周杨阳) and Huanyang Chen(陈焕阳). Chin. Phys. B, 2022, 31(10): 104205.
[2] Deep-learning-based cryptanalysis of two types of nonlinear optical cryptosystems
Xiao-Gang Wang(汪小刚) and Hao-Yu Wei(魏浩宇). Chin. Phys. B, 2022, 31(9): 094202.
[3] Spatially modulated scene illumination for intensity-compensated two-dimensional array photon-counting LiDAR imaging
Jiaheng Xie(谢佳衡), Zijing Zhang(张子静), Mingwei Huang(黄明维),Jiahuan Li(李家欢), Fan Jia(贾凡), and Yuan Zhao(赵远). Chin. Phys. B, 2022, 31(9): 090701.
[4] Imaging a periodic moving/state-changed object with Hadamard-based computational ghost imaging
Hui Guo(郭辉), Le Wang(王乐), and Sheng-Mei Zhao(赵生妹). Chin. Phys. B, 2022, 31(8): 084201.
[5] An apodized cubic phase mask used in a wavefront coding system to extend the depth of field
Lina Zhu(朱丽娜), Fei Li(李飞), Zeyu Huang(黄泽宇), and Tingyu Zhao(赵廷玉). Chin. Phys. B, 2022, 31(5): 054217.
[6] Deep learning facilitated whole live cell fast super-resolution imaging
Yun-Qing Tang(唐云青), Cai-Wei Zhou(周才微), Hui-Wen Hao(蒿慧文), and Yu-Jie Sun(孙育杰). Chin. Phys. B, 2022, 31(4): 048705.
[7] Color-image encryption scheme based on channel fusion and spherical diffraction
Jun Wang(王君), Yuan-Xi Zhang(张沅熙), Fan Wang(王凡), Ren-Jie Ni(倪仁杰), and Yu-Heng Hu(胡玉衡). Chin. Phys. B, 2022, 31(3): 034205.
[8] Ghost imaging-based optical cryptosystem for multiple images using integral property of the Fourier transform
Yi Kang(康祎), Leihong Zhang(张雷洪), Hualong Ye(叶华龙), Dawei Zhang(张大伟), and Songlin Zhuang(庄松林). Chin. Phys. B, 2021, 30(12): 124207.
[9] Refocusing and locating effect of fluorescence scattering field
Jian-Gong Cui(崔建功), Ya-Xin Yu(余亚鑫), Xiao-Xia Chu(楚晓霞), Rong-Yu Zhao(赵荣宇), Min Zhu(祝敏), Fan Meng(孟凡), and Wen-Dong Zhang(张文栋). Chin. Phys. B, 2021, 30(12): 124210.
[10] Possibility to break through limitation of measurement range in dual-wavelength digital holography
Tuo Li(李拓), Wen-Xiu Lei(雷文秀), Xin-Kai Sun(孙鑫凯), Jun Dong(董军), Ye Tao(陶冶), and Yi-Shi Shi(史祎诗). Chin. Phys. B, 2021, 30(9): 094201.
[11] Impact of the spatial coherence on self-interference digital holography
Xingbing Chao(潮兴兵), Yuan Gao(高源), Jianping Ding(丁剑平), and Hui-Tian Wang(王慧田). Chin. Phys. B, 2021, 30(8): 084212.
[12] Single pixel imaging based on semi-continuous wavelet transform
Chao Gao(高超), Xiaoqian Wang(王晓茜), Shuang Wang(王爽), Lidan Gou(苟立丹), Yuling Feng(冯玉玲), Guangyong Jin(金光勇), and Zhihai Yao(姚治海). Chin. Phys. B, 2021, 30(7): 074201.
[13] Real time high accuracy phase contrast imaging with parallel acquisition speckle tracking
Zhe Hu(胡哲), Wen-Qiang Hua(滑文强), and Jie Wang(王 劼). Chin. Phys. B, 2021, 30(6): 064201.
[14] High speed ghost imaging based on a heuristic algorithm and deep learning
Yi-Yi Huang(黄祎祎), Chen Ou-Yang(欧阳琛), Ke Fang(方可), Yu-Feng Dong(董玉峰), Jie Zhang(张杰), Li-Ming Chen(陈黎明), and Ling-An Wu(吴令安). Chin. Phys. B, 2021, 30(6): 064202.
[15] Handwritten digit recognition based on ghost imaging with deep learning
Xing He(何行), Sheng-Mei Zhao(赵生妹), and Le Wang(王乐). Chin. Phys. B, 2021, 30(5): 054201.
No Suggested Reading articles found!