Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning

ZHU Haoran; GAO Qi; WANG Hongping; LIAO Xiangwei; ZHAO Liang; WEI Runjie; WANG Jinjun

doi:10.11729/syltlx20200141

Volume 35 Issue 3

Jun. 2021

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2021 > 35(3): 88-93

ZHU Haoran, GAO Qi, WANG Hongping, et al. Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning[J]. Journal of Experiments in Fluid Mechanics, 2021, 35(3): 88-93. doi: 10.11729/syltlx20200141

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Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning

doi: 10.11729/syltlx20200141

ZHU Haoran^1
,,
GAO Qi^{1
,
,},
WANG Hongping²,
LIAO Xiangwei^{3, 4},
ZHAO Liang^{3, 4},
WEI Runjie⁵,
WANG Jinjun⁶

1.
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
2.
Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
3.
Metallurgical Technology Research Institute, Iron and Steel Research Institute, Anshan iron and steel group, Anshan Liaoning 114009, China
4.
State Key Laboratory of Metal Material for Marine Equipment and Application, Anshan Liaoning 114009, China
5.
MicroVec. Inc., Beijing 100083, China
6.
Key Laboratory of Fluid Mechanics Ministry of Education, Beihang University, Beijing 100191, China

Received Date: 2020-11-10
Rev Recd Date: 2021-01-10
Publish Date: 2021-06-25

Abstract

Abstract

Three-dimensional particle reconstruction with limited two-dimensional projections is an underdetermined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by optimization methods. In order to obtain a better quality particle field for Tomographic PIV, in the current work, a practical particle reconstruction method based on convolutional neural network (CNN) is proposed. The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution from any traditional algebraic reconstruction technique (ART) based methods. Compared with available ART-based algorithms, the novel technique makes significant improvements in terms of reconstruction quality. It can effectively eliminate ghost particles and restore the shape of particles more accurately, and is at least an order of magnitude faster with dense particle concentration.
- machine learning,
- particle reconstruction,
- Tomographic PIV,
- convolutional neural network,
- refactoring quality

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References(28)

References

[1]	ELSINGA G E, SCARANO F, WIENEKE B, et al. Tomographic particle image velocimetry[J]. Experiments in Fluids, 2006, 41(6): 933-947. doi: 10.1007/s00348-006-0212-z
[2]	SCARANO F. Tomographic PIV: principles and practice[J]. Measurement Science and Technology, 2013, 24(1): 012001. doi: 10.1088/0957-0233/24/1/012001
[3]	GAO Q, WANG H P, SHEN G X. Review on development of volumetric particle image velocimetry[J]. Chinese Science Bulletin, 2013, 58(36): 4541-4556. doi: 10.1007/s11434-013-6081-y
[4]	DISCETTI S, NATALE A, ASTARITA T. Spatial filtering improved tomographic PIV[J]. Experiments in Fluids, 2013, 54(4): 1-13. doi: 10.1007/s00348-013-1505-7
[5]	WORTH N A, NICKELS T B. Acceleration ofTomo-PIV by estimating the initial volume intensity distribution[J]. Experiments in Fluids, 2008, 45(5): 847-856. doi: 10.1007/s00348-008-0504-6
[6]	ATKINSON C, SORIA J. An efficient simultaneous reconstruction technique for tomographic particle image velocimetry[J]. Experiments in Fluids, 2009, 47(4-5): 553-568. doi: 10.1007/s00348-009-0728-0
[7]	ELSINGA G E, TOKGOZ S. Ghost hunting-an assessment of ghost particle detection and removal methods for tomographic-PIV[J]. Measurement Science and Technology, 2014, 25(8): 084004. doi: 10.1088/0957-0233/25/8/084004
[8]	DE SILVA C M, BAIDYA R, MARUSIC I. EnhancingTomo-PIV reconstruction quality by reducing ghost particles[J]. Measurement Science and Technology, 2013, 24(2): 024010. doi: 10.1088/0957-0233/24/2/024010
[9]	SCHANZ D, SCHRÖDER A, GESEMANN S. 'Shake The Box'-a 4D PTV algorithm: Accurate and ghostless reconstruction of Lagrangian tracks in densely seeded flows[C]//Proc of the 17th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 2014.
[10]	SCHANZ D, GESEMANN S, SCHRÖDER A. Shake-The-Box: Lagrangian particle tracking at high particle image densities[J]. Experiments in Fluids, 2016, 57(5): 1-27. doi: 10.1007/s00348-016-2157-1
[11]	WIENEKE B. Iterative reconstruction of volumetric particle distribution[J]. Measurement Science and Technology, 2013, 24(2): 024008. doi: 10.1088/0957-0233/24/2/024008
[12]	LYNCH K P, SCARANO F. An efficient and accurate approach to MTE-MART for time-resolved tomographic PIV[J]. Experiments in Fluids, 2015, 56(3): 1-16. doi: 10.1007/s00348-015-1934-6
[13]	NOVARA M, BATENBURG K J, SCARANO F. Motion tracking-enhanced MART for tomographic PIV[J]. Measurement Science and Technology, 2010, 21(3): 035401. doi: 10.1088/0957-0233/21/3/035401
[14]	WANG H P, GAO Q, WEI R J, et al. Intensity-enhanced MART for tomographic PIV[J]. Experiments in Fluids, 2016, 57(5): 1-19. doi: 10.1007/s00348-016-2176-y
[15]	GESEMANN S, SCHANZ D, SCHRÖDER A, et al. Recasting Tomo-PIV reconstruction as constrained and L1-regularized nonlinear least squares problem[C]//Proc of the 15th International Symposium on Applications of Laser Techniques to Fluid Mechanics. 2010.
[16]	YE Z J, GAO Q, WANG H P, et al. Dual-basis reconstruction techniques for tomographic PIV[J]. Science China Technological Sciences, 2015, 58(11): 1963-1970. doi: 10.1007/s11431-015-5909-x
[17]	BAJPAYEE A, TECHET A H. Fast volume reconstruction for 3D PIV[J]. Experiments in Fluids, 2017, 58(8): 1-4. doi: 10.1007/s00348-017-2373-3
[18]	BEN SALAH R, ALATA O, TREMBLAIS B, et al. Tomographic reconstruction of 3D objects using marked point process framework[J]. Journal of Mathematical Imaging and Vision, 2018, 60(7): 1132-1149. doi: 10.1007/s10851-018-0800-6
[19]	CAI S Z, ZHOU S C, XU C, et al. Dense motion estimation of particle images via a convolutional neural network[J]. Experiments in Fluids, 2019, 60(4): 1-16. doi: 10.1007/s00348-019-2717-2
[20]	CAI S Z, LIANG J M, GAO Q, et al. Particle image velocimetry based on a deep learning motion estimator[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 3538-3554. doi: 10.1109/TIM.2019.2932649
[21]	LAGEMANN C, LAGEMANN K, SCHRÖDER W, et al. Deep artificial neural network architectures in PIV applications[C]//Proc of the 13th International Symposium on Particle Image Velocimetry. 2019.
[22]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386
[23]	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. doi: 10.1109/TPAMI.2017.2699184
[24]	QIAN N. On the momentum term in gradient descent learning algorithms[J]. Neural Networks, 1999, 12(1): 145-151. doi: 10.1016/S0893-6080(98)00116-6
[25]	TIELEMAN T, HINTON G. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude[R]. COURSERA: Neural Netw Mach Learn, 2012, 4(2): 26-31.
[26]	KINGMA D, BA J. Adam: a method for stochastic optimization[C]//Proc of the 3rd International Conference on Learning Representations (ICLR). 2015.
[27]	IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. Computer Science, 2015, arXiv: 1502.03167. http://dl.acm.org/citation.cfm?id=3045118.3045167
[28]	ABADI M, BARHAM P, CHEN J, et al. Tensorflow: A system for large-scale machine learning[C]//Proc of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 2016.