Main results of the 4th International PIV Challenge

Kähler, Christian J.; Astarita, Tommaso; Vlachos, Pavlos P.; Sakakibara, Jun; Hain, Rainer; Discetti, Stefano; Foy, Roderick La; Cierpka, Christian

doi:10.1007/s00348-016-2173-1

Cited by 166 publications

(131 citation statements)

References 76 publications

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“…We follow the test setup of "test case D" of the 4 th International PIV Challenge [19]. The challenge took place in conjunction with the 17th International Symposium on Applications of Laser Techniques to Fluid Mechanics, where participants from industry and academia handed in results of their approaches on provided input data.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately no ground truth data of the challenge is provided, such that we had to generate new data with similar specifications using existing flow-fields from the JHTDB. Following the guidelines in [19], we use the same discretization level as [19] to obtain a volume of 1024×512×352 voxels and read out the ground truth flow fields at each voxel position. The average magnitude of the 3D displacements is 1.9 voxel units, with a maximum of 5.4 voxels.…”

Section: Discussionmentioning

confidence: 99%

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Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations

Lasinger

Vogel

Schindler

2017

2017 IEEE International Conference on Computer Vision (ICCV)

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In experimental fluid dynamics, the flow in a volume of fluid is observed by injecting high-contrast tracer particles and tracking them in multi-view video. Fluid dynamics researchers have developed variants of space-carving to reconstruct the 3D particle distribution at a given time-step, and then use relatively simple local matching to recover the motion over time. On the contrary, estimating the optical flow between two consecutive images is a long-standing standard problem in computer vision, but only little work exists about volumetric 3D flow. Here, we propose a variational method for 3D fluid flow estimation from multi-view data. We start from a 3D version of the standard variational flow model, and investigate different regularization schemes that ensure divergence-free flow fields, to account for the physics of incompressible fluids. Moreover, we propose a semi-dense formulation, to cope with the computational demands of large volumetric datasets. Flow is estimated and regularized at a lower spatial resolution, while the data term is evaluated at full resolution to preserve the discriminative power and geometric precision of the local particle distribution. Extensive experiments reveal that a simple sum of squared differences (SSD) is the most suitable data term for our application. For regularization, an energy whose Euler-Lagrange equations correspond to the stationary Stokes equations leads to the best results. This strictly enforces a divergence-free flow and additionally penalizes the squared gradient of the flow.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations

Lasinger

Vogel

Schindler

2017

2017 IEEE International Conference on Computer Vision (ICCV)

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“…The first test case comprises 600 PIV images of a micro-channel flow driven by 200 bar of pressure, constituting Case A of the 4th International PIV Challenge (Kähler et al 2016). The resulting mask is presented in Fig.…”

Section: Experimental Casementioning

confidence: 99%

Automated mask generation for PIV image analysis based on pixel intensity statistics

Masullo

Theunissen

2017

Exp Fluids

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“…PTV shows clear advantages to PIV in near-wall regions with high velocity gradients as they can provide measurements nearest to the wall (Stanislas et al, 2005 [32]). …”

Section: Particle Tracking Velocimetrymentioning

confidence: 99%

Image analysis techniques for the study of turbulent flows

Ferrari

2017

EPJ Web Conf.

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Abstract. In this paper, a brief review of Digital Image Analysis techniques employed in Fluid Mechanics for the study of turbulent flows is given. Particularly the focus is on the techniques developed by the research teams the Author worked in, that can be considered relatively "low cost" techniques. Digital Image Analysis techniques have the advantage, when compared to the traditional techniques employing physical point probes, to be non-intrusive and quasi-continuous in space, as every pixel on the camera sensor works as a single probe: consequently, they allow to obtain two-dimensional or three-dimensional fields of the measured quantity in less time. Traditionally, the disadvantages are related to the frequency of acquisition, but modern high-speed cameras are typically able to acquire at frequencies from the order of 1 KHz to the order of 1 MHz. Digital Image Analysis techniques can be employed to measure concentration, temperature, position, displacement, velocity, acceleration and pressure fields with similar equipment and setups, and can be consequently considered as a flexible and powerful tool for measurements on turbulent flows.

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Main results of the 4th International PIV Challenge

Cited by 166 publications

References 76 publications

Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations

Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations

Automated mask generation for PIV image analysis based on pixel intensity statistics

Image analysis techniques for the study of turbulent flows

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