Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements

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• 作者：Afshin Abbasi Hoseini (1)
  Zahra Zavareh (2)
  Fredrik Lundell (3)
  Helge I. Anderson (1)
  
• 刊名：Experiments in Fluids
• 出版年：2014
• 出版时间：April 2014
• 年：2014
• 卷：55
• 期：4
• 全文大小：1,192 KB
• 参考文献：1. Andersson HI, Zhao L, Barri M (2012) Torque-coupling and particle鈥搕urbulence interactions. J Fluid Mech 696:319鈥?29 CrossRef
  2. Ashforth-Frost S, Fontama VN, Jambunathan K, Hartle SL (1995) The role of neural networks in fluid mechanics and heat transfer. In: Instrumentation and measurement technology conference, 1995. IMTC/95. Proceedings of integrating intelligent instrumentation and control, IEEE, 24鈥?6 April 1995, p 6. doi:10.1109/IMTC.1995.515093
  3. Baek S, Lee S (1996) A new two-frame particle tracking algorithm using match probability. Exp Fluids 22(1):23鈥?2 CrossRef
  4. Balachandar S, Eaton JK (2010) Turbulent dispersed multiphase flow. Annu Rev Fluid Mech 42:111鈥?33 CrossRef
  5. Blair D, Dufresne E (2008) Particle tracking code in matlab. http://physics.georgetown.edu/matlab/
  6. Cardwell ND, Vlachos PP, Thole KA (2011) A multi-parametric particle-pairing algorithm for particle tracking in single and multiphase flows. Meas Sci Technol 22(10):105406 CrossRef
  7. Carlsson A, H氓kansson K, Kvick M, Lundell F, S枚derberg LD (2011) Evaluation of steerable filter for detection of fibers in flowing suspensions. Exp Fluids 51(4):987鈥?96 CrossRef
  8. Cowen EA, Monismith SG (1997) A hybrid digital particle tracking velocimetry technique. Exp Fluids 22(3):199鈥?11. doi:10.1007/s003480050038 CrossRef
  9. Faller WE, Schreck SJ (1997) Unsteady fluid mechanics applications of neural networks. J Aircr 34(1):48鈥?5 CrossRef
  10. Fausett LV (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Englewood Cliffs
  11. Grant I, Pan X (1997) The use of neural techniques in PIV and PTV. Meas Sci Technol 8(12):1399 CrossRef
  12. Hassan Y, Canaan R (1991) Full-field bubbly flow velocity measurements using a multiframe particle tracking technique. Exp Fluids 12(1鈥?):49鈥?0
  13. Hout R, Sabban L, Cohen A (2013) The use of high-speed PIV and holographic cinematography in the study of fiber suspension flows. Acta Mech 1鈥?8. doi:10.1007/s00707-013-0917-z
  14. Jacob M, Unser M (2004) Design of steerable filters for feature detection using canny-like criteria. IEEE Trans Pattern Anal Mach Intell 26(8):1007鈥?019. doi:10.1109/TPAMI.2004.44 CrossRef
  15. Kaga A, Yamaguchi K, Kondo A, Inoue Y, Yamaguchi T, Kamoi S (1997) Flow field estimation using PIV-data and fluid dynamic equations. In: Proceedings of PIV-Fukui鈥?7, pp 131鈥?36
  16. Keane R, Adrian R, Zhang Y (1995) Super-resolution particle imaging velocimetry. Meas Sci Technol 6(6):754 CrossRef
  17. Khalitov D, Longmire E (2002) Simultaneous two-phase PIV by two-parameter phase discrimination. Exp Fluids 32(2):252鈥?68 CrossRef
  18. Kiger K, Pan C (2000) PIV technique for the simultaneous measurement of dilute two-phase flows. J Fluids Eng 122(4):811鈥?18 CrossRef
  19. Knaak M, Rothlubbers C, Orglmeister R (1997) A Hopfield neural network for flow field computation based on particle image velocimetry/particle tracking velocimetry image sequences. In: International conference on neural networks, vol 41, 9鈥?2 Jun 1997, pp 48鈥?2. doi:10.1109/ICNN.1997.611633
  20. Kobayashi T, Saga T, Segawa S (1989) Multipoint velocity measurement for unsteady flow field by digital image processing. Flow Vis V, Hemisph 197鈥?02
  21. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464鈥?480 CrossRef
  22. Kvick M, H氓kansson KO, Lundell F, S枚derberg LD, Prahl Wittberg L (2010) Streak formation and fibre orientation in near wall turbulent fibre suspension flow. ERCOFTAC bulletin 84
  23. Labont茅 G (1999) A new neural network for particle-tracking velocimetry. Exp Fluids 26(4):340鈥?46. doi:10.1007/s003480050297 CrossRef
  24. Labont茅 G (2001) Neural network reconstruction of fluid flows from tracer-particle displacements. Exp Fluids 30(4):399鈥?09. doi:10.1007/s003480000217 CrossRef
  25. Lindken R, Merzkirch W (2002) A novel PIV technique for measurements in multiphase flows and its application to two-phase bubbly flows. Exp Fluids 33(6):814鈥?25. doi:10.1007/s00348-002-0500-1 CrossRef
  26. Lundell F, S枚derberg LD, Alfredsson PH (2011) Fluid mechanics of papermaking. Annu Rev Fluid Mech 43:195鈥?17 CrossRef
  27. Malik NA, Dracos T, Papantoniou DA (1993) Particle tracking velocimetry in three-dimensional flows. Exp Fluids 15(4鈥?):279鈥?94. doi:10.1007/BF00223406
  28. Marchioli C, Soldati A (2013) Rotation statistics of fibers in wall shear turbulence. Acta Mech 224(10):2311鈥?329. doi:10.1007/s00707-013-0933-z CrossRef
  29. Marchioli C, Fantoni M, Soldati A (2010) Orientation, distribution, and deposition of elongated, inertial fibers in turbulent channel flow. Phys Fluids 22(3):033301 CrossRef
  30. Mortensen P, Andersson H, Gillissen J, Boersma B (2008) Dynamics of prolate ellipsoidal particles in a turbulent channel flow. Phys Fluids 20:093302 CrossRef
  31. Nishino K, Kasagi N, Hirata M (1989) Three-dimensional particle tracking velocimetry based on automated digital image processing. J Fluids Eng 111(4):384鈥?91 CrossRef
  32. Ohmi K (2008) SOM-Based particle matching algorithm for 3D particle tracking velocimetry. Appl Math Comput 205(2):890鈥?98. doi:10.1016/j.amc.2008.05.101
  33. Ohmi K, Li HY (2000) Particle-tracking velocimetry with new algorithms. Meas Sci Technol 11(6):603 CrossRef
  34. Ohmi K, Sapkota (2006) A particle tracking velocimetry using cellular neural network. In: International joint conference on neural networks (IJCNN), pp 3963鈥?969. doi:10.1109/IJCNN.2006.246917
  35. Ohmi K, Joshi B, Panday S (2009) A SOM based stereo pair matching algorithm for 3-D particle tracking velocimetry. In: Huang D-S, Jo K-H, Lee H-H, Kang H-J, Bevilacqua V (eds) Emerging intelligent computing technology and applications with aspects of artificial intelligence, 5th international conference on intelligent computing, ICIC 2009 Ulsan, South Korea, September 16鈥?9 2009 Proceedings, vol 5755. Springer-Verlag Berlin, Heidelberg, pp 11鈥?0. ISSN 0302-9743
  36. Okamoto K, Hassan Y, Schmidl W (1995) New tracking algorithm for particle image velocimetry. Exp Fluids 19(5):342鈥?47 CrossRef
  37. Paris A, Eaton J (1999) PIV measurements in a particle-laden channel flow. In: Proceedings of the 3rd ASME/JSME joint fluids engineering conference, San Fransisco, pp FEDSM99鈥?863
  38. Parsheh M, Brown ML, Aidun CK (2005) On the orientation of stiff fibres suspended in turbulent flow in a planar contraction. J Fluid Mech 545:245鈥?69. doi:10.1017/S0022112005006968 CrossRef
  39. Paschkewitz JS, Dubief Y, Dimitropoulos CD, Shaqfeh ESG, Moin P (2004) Numerical simulation of turbulent drag reduction using rigid fibres. J Fluid Mech 518:281鈥?17. doi:10.1017/s0022112004001144 CrossRef
  40. Pereira F, St眉er H, Graff EC, Gharib M (2006) Two-frame 3D particle tracking. Meas Sci Technol 17(7):1680 CrossRef
  41. Poelma C, Westerweel J, Ooms G (2007) Particle-fluid interactions in grid-generated turbulence. J Fluid Mech 589(1):315鈥?51
  42. Takehara K, Adrian R, Etoh G, Christensen K (2000) A Kalman tracker for super-resolution PIV. Exp Fluids 29(1):S034鈥揝041
  
• 作者单位：Afshin Abbasi Hoseini (1)
  Zahra Zavareh (2)
  Fredrik Lundell (3)
  Helge I. Anderson (1)
  
  1. Division of Fluids Engineering, Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
  2. Faculty of Natural Sciences and Technology, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
  3. Linn茅 Flow Centre, KTH Mechanics and Wallenberg Wood Science Centre, Royal Institute of Technology, 100 44, Stockholm, Sweden
  
• ISSN：1432-1114

文摘

A matching algorithm based on self-organizing map (SOM) neural network is proposed for tracking rod-like particles in 2D optical measurements of dispersed two-phase flows. It is verified by both synthetic images of elongated particles mimicking 2D suspension flows and direct numerical simulations-based results of prolate particles dispersed in a turbulent channel flow. Furthermore, the potential benefit of this algorithm is evaluated by applying it to the experimental data of rod-like fibers tracking in wall turbulence. The study of the behavior of elongated particles suspended in turbulent flows has a practical importance and covers a wide range of applications in engineering and science. In experimental approach, particle tracking velocimetry of the dispersed phase has a key role together with particle image velocimetry of the carrier phase to obtain the velocities of both phases. The essential parts of particle tracking are to identify and match corresponding particles correctly in consecutive images. The present study is focused on the development of an algorithm for pairing non-spherical particles that have one major symmetry axis. The novel idea in the algorithm is to take the orientation of the particles into account for matching in addition to their positions. The method used is based on the SOM neural network that finds the most likely matching link in images on the basis of feature extraction and clustering. The fundamental concept is finding corresponding particles in the images with the nearest characteristics: position and orientation. The most effective aspect of this two-frame matching algorithm is that it does not require any preliminary knowledge of neither the flow field nor the particle behavior. Furthermore, using one additional characteristic of the non-spherical particles, namely their orientation, in addition to its coordinate vector, the pairing is improved both for more reliable matching at higher concentrations of dispersed particles and for higher robustness against loss of particle pairs between image frames.