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A dynamic masking technique for particle image velocimetry using convolutional autoencoders

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Abstract

Particle image velocimetry is an established technique for optical flow measurement, in which the local fluid velocity is derived from the motion of passive tracer particles. This motion is typically computed by a cross-correlation of small interrogation windows from subsequent image frames. Masking of objects within a velocimetry image is required to avoid correlation bias from interrogation windows overlapping the object. Manual masking efforts quickly become unpractical in the presence of moving or deforming objects and for high-speed recordings. Three methods are proposed to perform automatic dynamic image masking based on convolutional autoencoders, a type of artificial neural network. Promising results are achieved which suggests that neural-network-based PIV masking may be a valuable addition to the existing techniques.

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References

  • Adatrao S, Sciacchitano A (2019) Elimination of unsteady background reflections in PIV images by anisotropic diffusion. Meas Sci Technol 30:035204

    Article  Google Scholar 

  • Adrian RJ, Westerweel J (2011) Particle image velocimetry. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Brucker Ch (2000) Particle image velocimetry and associated techniques. Von Karman Institute for Fluid Dynamics, lecutre series 2000–01

  • Brunton SL, Noack BR, Koumoutsakos P (2020) Machine learning for fluid mechanics. Annu Rev Fluid Mech 52(1):477–508

    Article  Google Scholar 

  • Ergin FG (2017) Dynamic masking techniques for particle image velocimetry. J Therm Sci Technol 37(2):61–74

    Google Scholar 

  • Ergin G, Olofsson J, Watz BO, Gade-Nielsen N (2018) Dynamic masking application examples in two-phase flow PIV measurements. In: 19th International Symposium on Applictions Las Im Tech Fluid Mech, Lisbon, Portugal

  • Gemmell BJ, Colin SP, Costello JH, Sutherland KR (2019) A ctenophore (comb jelly) employs vortex rebound dynamics and outperforms other gelatinous swimmers. R Soc Open Sci. https://doi.org/10.1098/rsos.181615

    Article  Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  • Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233–243

    Article  Google Scholar 

  • Mendez MA, Raiola M, Masullo A, Discetti S, Ianiro A, Theunissen R, Buchlin JM (2017) POD-based background removal for particle image velocimetry. Exp Therm Fluid Sci 80:181–192. https://doi.org/10.1016/j.expthermflusci.2016.08.021

    Article  Google Scholar 

  • Raffel M, Willert CE, Scarano F, Kähler C, Wereley ST, Kompenhans J (2018) Particle image velocimetry. Springer, Berlin

    Book  Google Scholar 

  • Sciacchitano A, Scarano F (2014) Elimination of PIV light reflections via a temporal high pass filter. Meas Sci Technol 25:084009

    Article  Google Scholar 

  • Thielicke W, Stamhuis E (2014) PIVlab—towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB. J Open Res Softw 2(1):30. https://doi.org/10.5334/jors.bl

    Article  Google Scholar 

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Correspondence to Bernhard Vennemann.

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Vennemann, B., Rösgen, T. A dynamic masking technique for particle image velocimetry using convolutional autoencoders. Exp Fluids 61, 168 (2020). https://doi.org/10.1007/s00348-020-02984-w

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  • DOI: https://doi.org/10.1007/s00348-020-02984-w

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