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Particle tracking velocimetry with an ant colony optimization algorithm

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Abstract

A new concept algorithm based on the ant colony optimization is developed for the use in 2-D and 3-D particle tracking velocimetry (PTV). In the particle matching process of PTV, the ant colony optimization is usually aimed at minimization of the sum of the distances between the first-frame and second-frame particles. But this type of minimization often goes unsuccessfully in the regions where the particles are located very close to each other. In order to avoid this flaw, a new type of minimization is attempted using a physical property corresponding to the flow consistency or the quasi-rigidity of particle distribution patterns. Specifically, the ant colony optimization is now aimed at minimization of the sum of the relaxation of neighbor particles. In the present study, the new algorithm is applied to sets of 2-D and 3-D synthetic particle images as well as the experimental images with successful results.

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Abbreviations

ACO:

Ant colony optimization

AS:

Ant system

PIV:

Particle image velocimetry

PTV:

Particle tracking velocimetry

SOM:

Self-organizing maps

TSP:

Traveling salesman problem

References

  • Adrian RJ (2004) Twenty years of particle image velocimetry. In: Proceedings of the 12th international symposium on applications of laser techniques to fluid mechanics, #01-1

  • Baek SJ, Lee SJ (1996) A new two-frame particle tracking algorithm using match probability. Exp Fluids 22–1:23–32

    Article  Google Scholar 

  • Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cyber B 26(2):29–41

    Article  Google Scholar 

  • Grant I, Pan X (1995) An investigation of the performance of multi-layer neural networks applied to the analysis of PIV images. Exp Fluids 19–3:159–166

    Google Scholar 

  • Hassan YA, Canaan RE (1991) Full-field bubbly flow velocity measurements using a multi-frame particle tracking technique. Exp Fluids 12:49–60

    Article  Google Scholar 

  • Hayami H, Oakmoto K, Aramaki S (1997) A trial of benchmark test for PIV (in Japanese). J Visual Soc Jpn 17(S-1):163–166

    Google Scholar 

  • Ishikawa M, Yamamoto F, Murai Y, Iguchi M, Wada A (1997) A novel PIV algorithm using velocity gradient tensor. In: Proceedings of the 2nd international workshop on PIV’97-Fukui. pp 51–56

  • Knaak M, Rothlübbers C, Orglmeister R (1997) A Hopfield neural network for flow field computation based on particle image velocimetry/particle tracking velocimetry image sequences. In: Proceedings of the IEEE international conference on neural networks. pp 48–52

  • Kobayashi T, Saga T, Segawa S (1989) Multipoint velocity measurement for unsteady flow field by digital image processing, flow visualization V, hemisphere. pp 197–202

  • Labonté G (1999) A new neural network for particle tracking velocimetry. Exp Fluids 26–4:340–346

    Google Scholar 

  • Ohmi K (2003) 3-D particle tracking velocimetry using a SOM neural network. In: Proceedings of the 5th international symposium on particle image velocimetry: #3112

  • Ohmi K, Li H (2000) Particle tracking velocimetry with new algorithms. Meas Sci Technol 11–6:603–616

    Article  Google Scholar 

  • Ohmi K, Yoshida N, Huynh TH (2001) Genetic algorithm PIV. In: Proceedings of the 4th international symposium on particle image velocimetry. P1051

  • Ohyama R, Takagi T, Tsukiji T, Nakanishi S, Kaneko K (1993) Particle tracking technique and velocity measurement of visualized flow fields by means of genetic algorithm (in Japanese). J Visual Soc Jpn 13(S1):35–38

    Google Scholar 

  • Okamoto K (1998) Particle cluster tracking algorithm in particle image velocimetry. JSME Int J B 41(1):151–154

    Google Scholar 

  • Okamoto K, Schmidl WD, Hassan YA (1995) Least force technique for the particle tracking algorithm, Flow Visualization VII, Begell House. pp 647–652

  • Okamoto K, Nishio S, Saga T, Kobayashi T (2000a) Standard images for particle image velocimetry. Meas Sci Technol 11:685–691

    Article  Google Scholar 

  • Okamoto K, Nishio S, Kobayashi T, Saga T, Takehara K (2000b) Evaluation of the 3D-PIV standard images (PIV-STD project). J Visualization 3(2):115–124

    Article  Google Scholar 

  • Raffel M, Willert C, Kompenhans J (1998) Particle image velocimetry: a practical guide. Springer, Berlin

    Google Scholar 

  • Takagi T (2007) Study on particle tracking velocimetry using ant colony optimization (in Japanese). J Visual Soc Japan 27(S2):89–90

    Google Scholar 

  • Uemura T, Yamamoto F, Ohmi K (1989) High speed algorithm of image analysis for real time measurement of two-dimensional velocity distribution. Flow Visual ASME FED 85:129–134

    Google Scholar 

  • Wernet MP (1993) Fuzzy logic particle tracking velocimetry. NASA Technical Memorandum: #106194

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Authors and Affiliations

  1. Faculty of Engineering, Osaka Sangyo University, Daito-shi, Osaka, 5748530, Japan

    Kazuo Ohmi, Sanjeeb Prasad Panday & Achyut Sapkota

Authors
  1. Kazuo Ohmi
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  2. Sanjeeb Prasad Panday
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  3. Achyut Sapkota
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Corresponding author

Correspondence to Kazuo Ohmi.

Additional information

The 13th International Symposium on Flow Visualization (2008 ISFV, Nice-France, Paper No. 319). This manuscript is to be considered for the special issue ‘2008 ISFV-13’.

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Ohmi, K., Panday, S.P. & Sapkota, A. Particle tracking velocimetry with an ant colony optimization algorithm. Exp Fluids 48, 589–605 (2010). https://doi.org/10.1007/s00348-009-0815-2

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  • DOI: https://doi.org/10.1007/s00348-009-0815-2

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