Lagrangian turbulence statistics using 3D-PTV: Realistic virtual experiment assessment

Alex Ruiz, Alex Liberzon, Samik Bhattacharya*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The outcome of a three-dimensional particle tracking velocimetry (3D-PTV) experiment can be significantly improved if the influence of the camera locations, calibration, and light volume can be determined before conducting the experiment. Such a-priory assessment will also reduce the time for troubleshooting a 3D-PTV experiment. In this paper, we report a “virtual experiment” approach. Multiple frames of synthetic particle images are generated using triangulation and optical spread functions based on the Lagrangian trajectories in 3D velocity fields with known spatio-temporal characteristics. We use an analytical 3D Burger's vortex, and the homogeneous isotropic turbulence (HIT) data-set from the turbulence database hosted by Johns Hopkins University (JHU). The 3D particle positions are projected on two-dimensional (2D) images using artificial extrinsic and intrinsic camera matrices, depth of field, and volume illumination. These 2D images served as images taken by a four-camera 3D-PTV system in a virtual experiment. Blind test calibration using OpenPTV, an open source software, reconstructs the camera positions for a simulated experimental set-up and the calibrated set-up is used to create Lagrangian trajectories from the particle image sequences. Finally, we compare the turbulent statistics reconstructed from the virtual experiment with that of the ground truth data. We show that reconstruction from a virtual 3D-PTV experiment reproduces the mean flow and turbulent statistics faithfully; however, the disparity increases for higher order and multi-point statistics. The proposed approach can be used to fine-tune the camera parameters and the light volume in order to increase the fidelity of turbulent flow measurements using 3D-PTV.

Original languageEnglish
Article number102310
JournalFlow Measurement and Instrumentation
Volume89
DOIs
StatePublished - Mar 2023

Funding

FundersFunder number
NSFGRFPGR101724
National Science Foundation

    Keywords

    • Lagrangian statistics
    • Particle tracking
    • Synthetic image generator

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