Ensemble 3D PTV for high resolution turbulent statistics

This manuscripts presents a study on adverse-pressure-gradient turbulent boundary layers under different Reynolds-number and pressure-gradient conditions. In this work we performed Particle Image Velocimetry (PIV) measurements supplemented with Large-Eddy Simulations in order to have a dataset covering a range of displacement-thickness-based Reynolds-number 2300 34000 and values of the Clauser pressure-gradient parameter β up to 2.4. The spatial resolution limits of PIV for the estimation of turbulence statistics have been overcome via ensemble-based approaches. A comparison between ensemble-correlation and ensemble Particle Tracking Velocimetry was carried out to assess the uncertainty of the two methods. The effects of β, R e and of the pressure-gradient history on turbulence statistics were assessed. A modal analysis via Proper Orthogonal Decomposition was carried out on the flow fields and showed that about 20% of the energy contribution corresponds to the first mode, while 40% of the turbulent kinetic energy corresponds to the first four modes with no appreciable dependence on β and R e within the investigated range. The topology of the spatial modes shows a dependence on the Reynolds number and on the pressure-gradient strength, in line with the results obtained from the analysis of the turbulence statistics. The contribution of the modes to the Reynolds stresses and the turbulence production was assessed using a truncated low-order reconstruction with progressively larger number of modes. It is shown that the outer peaks in the Reynolds-stress profiles are mostly due to large-scale structures in the outer part of the boundary layer.

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“…[49–51], with biased search using PIV as a predictor [52]. The bin is performed on 400 × 4pixels regions.…”

Section: Methodsmentioning

confidence: 99%

Adverse-Pressure-Gradient Effects on Turbulent Boundary Layers: Statistics and Flow-Field Organization

Vila

Örlü

Vinuesa

et al. 2017

Flow Turbulence Combust

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“…Agüera et al [1] first conduct 3D particle detection by a classical 2D particle detection in the images and stereoscopic triangulation, and then solve the temporal matching in two steps. Particles at the first time instant are displaced using a "predictor" motion field obtained by correlation on low-resolution TomoPIV volumes.…”

Section: Related Workmentioning

confidence: 99%

“…We thus here present the compared characteristics of the runs for 3D-PTV and planar PIV, as well as the respective methods for obtaining statistics, and their spatial resolution. Table I sums up associated relevant quantities. For DF-TPTV, which yields scattered vector data, we resort to bin averaging, as traditionally done in PTV methods [1,14,15]. In the present jet flow context, we choose a specific form of bins in order to increase the number of samples.…”

Section: Runs and Averaging Characteristicsmentioning

confidence: 99%

Double-frame tomographic PTV at high seeding densities

et al. 2020

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A novel method performing 3D PTV from double frame multi-camera images is introduced. Particle velocities are estimated by following three steps. Firstly, separate particle reconstructions with a sparsity-based algorithm are performed on a fine grid. Secondly, they are expanded on a coarser grid on which 3D correlation is performed, yielding a predictor displacement field that allows to efficiently match particles at the two time instants. As these particles are still located on a voxel grid, the third, final step achieves particle position refinement to their actual subvoxel position by a global optimization process, also accounting for their intensities. As it strongly leverages on principles from tomographic reconstruction, the technique is termed Double-Frame Tomo-PTV (DF-TPTV). Synthetic tests on a complex turbulent flow show that the method achieves high particle and vector detection efficiency, up to seeding densities of around 0.08 particles per pixel (ppp), while its root-mean-square error of velocity estimation is lower to that of state-of-the-art similar methods. Results from an experimental campaign on a transitional round air jet at Reynolds number 4600 are also presented. During the tests, seeding density varies from 0.06 to 0.03 ppp on average. Associated to an outlier rejection scheme based on temporal statistics, DF-TPTV vector fields truthfully correspond to the instantaneous jet dynamics. Quantitative performance assessment is provided by introducing statistics performed by bin averaging, upon assuming statistical axisymmetry of the jet. Mean and fluctuating axial velocity components in the jet near-field are compared with reference results obtained from planar PIV at higher seeding density, with an interrogation window of size comparable to that of the bins. Results are found to be in excellent agreement with one another, confirming the high performance of DF-TPTV to yield reliable volumetric vector fields at seeding densities usually considered for tomographic PIV processing, or even higher.

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“…To achieve a better accuracy at each phase, the information from one-time step to the following one is interpolated to obtain the information at the exact time when the wings are at a specified location (i.e., phase). Finally, by means of a binning procedure (Agüera et al 2016), the flow results that are originally obtained in a Lagrangian (unstructured) representation are converted into a Eulerian one to obtain a three-dimensional grid that relates to the measured volume. Before the spatial averaging (binning) is conducted, the particle track information is mapped onto a global coordinate system, such that no artefacts are generated at the boundaries of the overlapping conical regions.…”

Section: Particle-tracking Algorithmmentioning

confidence: 99%

Large-scale volumetric flow visualization of the unsteady wake of a flapping-wing micro air vehicle

et al. 2019

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The objective of this experimental investigation is the volumetric visualization of the near wake topology of the vortex structures generated by a flapping-wing micro air vehicle. To achieve the required visualization domain (which in the present experiments amounts to a size of 60,000 cm 3 ), use is made of robotic particle image velocimetry, which implements coaxial illumination and imaging in combination with the use of helium-filled soap bubbles as tracer particles. Particle trajectories are determined via Lagrangian particle tracking and information of different phases throughout the flapping cycle is obtained by means of a phase-averaging procedure applied to the particle tracks. Experiments have been performed at different settings (flow speed, flapping frequency, and body angle) that are representative of actual flight conditions, and the effect of reduced frequency on the wake topology is investigated. Furthermore, experiments have been carried out in both tethered and freeflight conditions, allowing an unprecedented comparison between the aerodynamics of the two conditions. Graphic abstractElectronic supplementary material The online version of this article (https ://doi.org/10.

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Ensemble 3D PTV for high resolution turbulent statistics

Cited by 67 publications

References 26 publications

Adverse-Pressure-Gradient Effects on Turbulent Boundary Layers: Statistics and Flow-Field Organization

Adverse-Pressure-Gradient Effects on Turbulent Boundary Layers: Statistics and Flow-Field Organization

Double-frame tomographic PTV at high seeding densities

Large-scale volumetric flow visualization of the unsteady wake of a flapping-wing micro air vehicle

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