Abstract

A method is proposed to reconstruct the instantaneous velocity field from time-resolved volumetric particle tracking velocimetry (PTV, e.g., 3D-PTV, tomographic PTV and Shake-the-Box), employing both the instantaneous velocity and the velocity material derivative of the sparse tracer particles. The constraint to the measured temporal derivative of the PTV particle tracks improves the consistency of the reconstructed velocity field. The method is christened as pouring time into space, as it leverages temporal information to increase the spatial resolution of volumetric PTV measurements. This approach becomes relevant in cases where the spatial resolution is limited by the seeding concentration. The method solves an optimization problem to find the vorticity and velocity fields that minimize a cost function, which includes next to instantaneous velocity, also the velocity material derivative. The velocity and its material derivative are related through the vorticity transport equation, and the cost function is minimized using the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm. The procedure is assessed numerically with a simulated PTV experiment in a turbulent boundary layer from a direct numerical simulation (DNS). The experimental validation considers a tomographic particle image velocimetry (PIV) experiment in a similar turbulent boundary layer and the additional case of a jet flow. The proposed technique (‘vortex-in-cell plus’, VIC+) is compared to tomographic PIV analysis (3D iterative cross-correlation), PTV interpolation methods (linear and adaptive Gaussian windowing) and to vortex-in-cell (VIC) interpolation without the material derivative. A visible increase in resolved details in the turbulent structures is obtained with the VIC+ approach, both in numerical simulations and experiments. This results in a more accurate determination of the turbulent stresses distribution in turbulent boundary layer investigations. Data from a jet experiment, where the vortex topology is retrieved with a small number of tracers indicate the potential utilization of VIC+ in low-concentration experiments as for instance occurring in large-scale volumetric PTV measurements.

Highlights

  • The spatial resolution of tomographic particle image velocimetry (PIV) and particle tracking velocimetry (PTV) measurements is directly related to the seeding concentration of the tracer particles

  • The VIC+ method is proposed for reconstruction of instantaneous velocity from time-resolved volumetric PTV measurements, by leveraging the temporal information available by the measurements in form of the velocity material derivative

  • This is christened as pouring time into space. In both the numerical and experimental assessment considering turbulent boundary layer measurements, VIC+ is able to reconstruct turbulent velocity fluctuations at a fraction of the seeding concentration required for tomographic PIV

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Summary

Introduction

The spatial resolution of tomographic particle image velocimetry (PIV) and particle tracking velocimetry (PTV) measurements is directly related to the seeding concentration of the tracer particles. Variational methods, proposed recently in the field of optical flow, apply in addition a regularization based on the Navier–Stokes equations, as discussed in the review paper by Heitz et al (2010) These techniques have not yet dealt with increasing spatial resolution of the instantaneous velocity measurements, in the cases where the spatial resolution is limited by tracer particle seeding concentration, as discussed in the first paragraph. Recently published results of PIV measurements in a turbulent boundary layer give an indication of the issues related to spatial resolution, with the turbulent velocity fluctuations being underestimated of approximately 20 % (Pröbsting et al 2013) This is a direct consequence of the spatial modulation associated with the cross-correlation analysis used for PIV when seeding concentration is limited.

Lagrangian particle tracking
Velocity–vorticity formulation
Optimization procedure
Problem closure
Mesh spacing and radial basis functions
Scaling of the optimization variables
Weighting coefficient α
Convergence criterion
Test case and data processing
Assessment of the results
Experimental assessment
Turbulent boundary layer
Jet flow
Findings
Conclusions
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