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Techniques for 3D-PIV

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Particle Image Velocimetry

Abstract

This chapter initially provides the reader with an extensive survey of the many methods available to measure the flow velocity in three-dimensional problems. Afterwards, the chapter devotes three main sections to the most common or emerging methods: Tomographic PIV, 3D-PTV techniques and Shake-the-Box. Tomographic PIV is considered the technique of choice at present, and is extensively discussed. Aspects cover hardware components, requirements for illumination of a volume and techniques to increase particles visibility. A discussion is given of the effects of number of cameras and their configuration to deal with densely seeded experiments, where the phenomenon of ghost particles dominates the experimental errors. The theoretical background is given about the techniques used for tomographic reconstruction of 3D particle intensity distribution, followed by a critical evaluation of the accuracy of reconstruction. The discussion of Tomographic PIV closes with the description of the most recent algorithms based on multi-exposure reconstruction and time-resolved data analysis. The second part of the chapter is a new addition to the book and it deals with the fundamental principles of 3D-PTV. Particle detection, triangulation and pairing are the most important operations to perform a successful 3D-PTV evaluation. Hybrid methods based on tomographic reconstruction and individual particle tracking are discussed. The section concludes suggesting the working range of PTV techniques in 3D experiments. The Shake-the-Box technique is an emerging method that performs Lagrangian particle tracking with high potential in terms of computation speed and for the high accuracy of particle motion estimation. Its rapid diffusion among research laboratories justifies a detailed description of its working principles, main features and characteristic performance. The section elaborates on the concepts of Iterative Particle Reconstruction, Optical Transfer Function calibration and Data Assimilation to restore the results on a Cartesian mesh. Algorithms for image sequences as well as for four-frame recordings are detailed.

An overview of the Digital Content to this chapter can be found at [DC9.1].

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Notes

  1. 1.

    The text on trajectory fitting has been contributed by Sebastian Gesemann.

  2. 2.

    The text on Data Assimilation has been contributed by Sebastian Gesemann.

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  1. Institut für Aerodynamik und Strömungstechnik, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Göttingen, Germany

    Markus Raffel & Jürgen Kompenhans

  2. Institut für Antriebstechnik, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Köln, Germany

    Christian E. Willert

  3. Department of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands

    Fulvio Scarano

  4. Institut für Strömungsmechanik und Aerodynamik, Universität der Bundeswehr München, Neubiberg, Germany

    Christian J. Kähler

  5. Department of Mechanical Engineering, Birck Nanotech Center, Purdue University, West Lafayette, USA

    Steven T. Wereley

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Raffel, M., Willert, C.E., Scarano, F., Kähler, C.J., Wereley, S.T., Kompenhans, J. (2018). Techniques for 3D-PIV. In: Particle Image Velocimetry. Springer, Cham. https://doi.org/10.1007/978-3-319-68852-7_9

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