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A tomographic PIV system is introduced for the instantaneous measurement of the material acceleration (material derivative of velocity). The system is conceived to operate with short temporal separation (microseconds) and is there...
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A tomographic PIV system is introduced for the instantaneous measurement of the material acceleration (material derivative of velocity). The system is conceived to operate with short temporal separation (microseconds) and is therefore suitable for applications up to the high-speed flow regimes. The method of operation consists of tomographic imaging of a measurement volume using three arrays of four CCD cameras and two double-pulse laser systems. Advantages and shortcomings of the approach with respect to the most commonly used method based on light polarization are discussed. Various approaches are compared to determine the optimal utilization of four-pulse data to measure the material acceleration: Eulerian and Lagrangian schemes are compared to the recently introduced fluid trajectory correlation (FTC) technique from the authors. A synthetic image test case of a translating vortex is used to compare the schemes with and without the presence of noise. The truncation errors and sensitivity to random noise for each scheme are highlighted. A discussion is also given on the dynamic range of the schemes. The four-pulse tomographic system is used to measure the separated wake of an axisymmetric truncated base with afterbody at a Reynolds number of 68 000. The system calibration accuracy and the baseline measurement uncertainty of the velocity are evaluated with a zero-time delay test. A novel criterion is introduced to establish the relative accuracy of the material derivative measurement, based on the curl of the material acceleration field. The results indicate that the four-pulse tomo-PIV approach suits the measurement of the material acceleration using a variety of estimation schemes. In particular, the FTC technique gives the lowest error levels and is well-suited to perform accurate material acceleration measurements.
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A tomographic PIV system composed of 12 cameras has been used to study the object reconstruction accuracy over a wide range of values for the concentration of tracer particles. The relations between particle image density, number ...
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A tomographic PIV system composed of 12 cameras has been used to study the object reconstruction accuracy over a wide range of values for the concentration of tracer particles. The relations between particle image density, number of cameras, reconstruction quality, and velocity field accuracy are determined experimentally. The effect of additional cameras is quantified by the reconstruction signal-to-noise ratio and normalized intensity variance. Furthermore, the variation of the reconstruction quality factor with seeding density and number of cameras is estimated considering the 12 cameras case as reference, which has so far only been investigated using numerical simulations. The accuracy of velocity measurements is investigated by comparing two simultaneous measurements obtained by independent tomographic systems. The measurement error is estimated by the quadratic difference between the velocity measured by the two systems. The importance of the number of cameras and seeding density is investigated. The results yield an optimum source density of 0.5 for a three-camera system and greater than 1.0 for a six-camera system. Doubling the number of cameras returns a broad range for the optimum source density and significantly lower measurement errors.
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The task of image interpolation and re-sampling for particle image velocimetry (PIV) is investigated, which is used for window shifting with sub-pixel accuracy and image or window deformation. A new interpolation scheme based on a...
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The task of image interpolation and re-sampling for particle image velocimetry (PIV) is investigated, which is used for window shifting with sub-pixel accuracy and image or window deformation. A new interpolation scheme based on a Gaussian filter is introduced and compared with commonly used and widely accepted interpolation techniques in terms of the achievable root mean square deviation of the displacement estimates.
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Particle image velocimetry (PIV) is used for measuring velocity distributions in planar cross sections of a fluid flow. The PIV technique is nonintrusive and not affected by variations in the fluid temperature. However, the veloci...
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Particle image velocimetry (PIV) is used for measuring velocity distributions in planar cross sections of a fluid flow. The PIV technique is nonintrusive and not affected by variations in the fluid temperature. However, the velocity range and the accuracy of measurement are dependent on the spatial resolution of the camera. To overcome this limitation, a color digital single-lens reflex (SLR) camera and three illumination colors were used to simultaneously obtain two pairs of time delays. The three illumination colors, namely, red, green, and blue, were generated by diode lasers and had wavelengths of 650, 532, and 445 nm, respectively. Visualization images were obtained by a color digital SLR camera with color (red, green, and blue) pixel image grids. The uncertainty of the three-color PIV technique was evaluated using standard PTV images, and the technique was used to investigate the heat transfer and fluid flow characteristics of the natural convection produced by a vertical grooved panel heated on one side. The new color PTV technique was also used to measure a 3D velocity field by deforming the shape of the light sheet. The results confirmed the feasibility of using multiple cameras to develop a high-resolution 3D PTV system.
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Since the popularity of digital particle image velocimetry technique (DPIV), many PIV image processing algorithms have been proposed. Amongst them, fast Fourier transform (FFT) Cross Correlation, Discrete Window Offset Cross Corre...
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Since the popularity of digital particle image velocimetry technique (DPIV), many PIV image processing algorithms have been proposed. Amongst them, fast Fourier transform (FFT) Cross Correlation, Discrete Window Offset Cross Correlation, Iterative Multigrid Cross Correlation, Iterative Image Deformation Cross Correlation and cross correlation based particle tracking methods are widely used algorithms and have been extensively studied by researchers. All of these algorithms have their advantages and disadvantages in terms of computational load and measurement accuracy. To choose a suitable algorithm, researchers not only need to understand the complex principles of these algorithms, but also need to find out their applicable flow conditions. This could greatly increase work load for PIV users who focus more on flow structure itself instead of PIV algorithms. It is therefore necessary to develop a method which can choose PIV algorithms wisely according to the input PIV images. This paper firstly reviews the development of PIV algorithm with mainly focus on analysing advantages and disadvantages of six widely used algorithms. By using both synthetic and real PIV images, comparative studies are then carried out among these algorithms. The tests give a rate for the performance of the algorithms and provide a parameter to automatically separate pattern match and particle tracking algorithms. Based on qualitative and quantitative analysis, an automated PIV image processing method-SmartPIV is proposed and tested by both synthetic and real PIV images. For all the three test cases, the SmartPIV successfully picks the most suitable algorithm and gives very promising results.
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This study proposes a method that complements Vortex-In-Cell plus (VIC+) (Schneiders and Scarano, Exp Fluids 57:139, 2016), a data assimilation technique that reconstructs a dense flow field from sparse particle tracks. Here, the ...
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This study proposes a method that complements Vortex-In-Cell plus (VIC+) (Schneiders and Scarano, Exp Fluids 57:139, 2016), a data assimilation technique that reconstructs a dense flow field from sparse particle tracks. Here, the focus is on the treatment of boundary conditions. In the VIC+ method, the choice of boundary conditions significantly affects a large part of the inner domain through their role as Dirichlet boundary conditions of the Poisson equations. By nature, there are particle tracks on one side of the boundaries, and often, due to experimental limitations, the track density is low, just close to the boundaries. This lack of data near the boundaries leads to a poor iterative update of the boundary condition for VIC+. Overall, the VIC+ method tends to be sensitive about the specific choice of the initial conditions, including the inner domain and the boundaries. Without prior flow information, a large padded volume has been proposed to achieve stable and reliable convergence, at the cost of a large number of additional unknowns that need to be optimized. The present method pursues the following concepts to resolve the above issues: use of the smallest possible padding size, reconstruction starting with "all zero" initial conditions, and progressive correction of the boundary conditions by considering the continuity law and the Navier-Stokes equation. These physical laws are incorporated as additional terms in the cost function, which so far only contained the disparity between PTV measurements and the VIC+ reconstruction. Here, the Navier-Stokes equation allows an instantaneous pressure field to be optimized simultaneously with the velocity and acceleration fields. Moreover, the scale parameters in VIC+ are redefined to be directly computed from PTV measurement instead of using the initial condition, and new scaling factors for the additional cost function terms are introduced. A coarse-grid approximation is employed in order to both improve reconstruction stability and save computation time. It provides a subsequent finer-grid with its low-resolution result as an initial condition while the interrogation volume slightly shrinks. A numerical assessment is conducted using synthetic PTV data generated from the direct numerical simulation data of forced isotropic turbulence from the Johns Hopkins Turbulence Database. Improved reconstructions, especially near the volume boundary, are achieved while the virtues of VIC+ are preserved. As an experimental assessment, the existing data from a time-resolved water jet is processed. Two reconstruction domains with different sizes are considered to compare the boundary of the smaller domain with the inside of the larger one. Visible enhancements near the boundary of the smaller domain are observed for this new approach in time-varying flow fields despite the limited input from PTV data. [GRAPHICS] .
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A new method is hereby presented to reduce motion blur induced error of time-resolved particle image velocimetry. The Monte-Carlo method (MCM) was applied to synthetic images to quantify the error due to blurred particle images. A...
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A new method is hereby presented to reduce motion blur induced error of time-resolved particle image velocimetry. The Monte-Carlo method (MCM) was applied to synthetic images to quantify the error due to blurred particle images. As the size of the streaks grew, it caused large errors in estimating displacements and increased the frequency of outliers beyond 20% for some cases. The mean displacement error was also about 0.2 - 0.55 px, which is larger than the nominally accepted PIV uncertainty of 0.1 px. A novel deblur filter (i.e., the generator) using a generative adversarial network (GAN) was developed, using 1 million synthetic images. The generator was verified using unlearned data from the MCM. The frequency of outliers, which was originally higher than 20% for the worst case, decreased to about 6%, and the displacement error was reduced to less than 0.3 px. The generator was applied to actual experimental images of a synthetic jet that had image blur and resulted in a substantial reduction of outliers. We also checked the performance of the generator in a uniform channel flow, and found that the deblurred images resulted in less PIV velocity error, and was closer to the results from the sharp images than those from the blurry images. [GRAPHICS] .
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The gas entrainment in a hollow cone spray submitted to variable density is studied experimentally in order to better understand the effect on mixture formation. Particle image velocimetry on fluorescent tracers, associated with a...
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The gas entrainment in a hollow cone spray submitted to variable density is studied experimentally in order to better understand the effect on mixture formation. Particle image velocimetry on fluorescent tracers, associated with a specific processing of the instantaneous velocity fields have been applied to obtain measurement in the close vicinity of the spray edge. In the "quasi-steady" region of the spray, important effect of the ambient density on the mass flow rate of entrained gas ((m) over dot(e)) have been pointed out. The axial evolution of (m) over dot(e) is in good agreement with an integral model that takes the momentum exchange between phases into account.
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Particle image velocimetry (PIV), as a key technique in experimental fluid mechanics, is able to estimate complex velocity field through consecutive input particle images. In this study, an attention-mechanism incorporated deep re...
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Particle image velocimetry (PIV), as a key technique in experimental fluid mechanics, is able to estimate complex velocity field through consecutive input particle images. In this study, an attention-mechanism incorporated deep recurrent network called ARaft-FlowNet has been proposed, on the basis of a previously established Recurrent All-Pairs Field Transforms optical flow model. The attention module is added to improve the network's capability of recognizing tracer particles' motion. Moreover, a parameterized dataset, ParaPIV-Dataset, is generated to explore the influence of particle parameters on deep learning networks, including particle diameter, image particle density, Gaussian noise, and peak intensity. The accuracy and generalizability of the newly proposed model has been evaluated and analyzed comprehensively. The results indicate that ARaft-FlowNet achieves state-of-the-art performance. Compared to previous methods, ARaft-FlowNet shows an accuracy improvement of 62.9%, 10.9%, and 9.4% in cylindrical flow, surface quasi-geostrophic flow, and DNS-turbulence flow. Meanwhile, the proposed model shows the strongest generalization and best capability to deal with complex flow fields with small-scale vortices. Additionally, tests on experimental turbulent jet data reveal that ARaft-FlowNet is able to deal with real PIV images with brightness variations and noise.
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A stereomicroscopic particle image velocimetry (S mu PIV) system has been developed for millimeter scale flows. The S mu PIV system is based on an off-the-shelf stereomicroscope, with magnification between 0.69x and 30x, and a fie...
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A stereomicroscopic particle image velocimetry (S mu PIV) system has been developed for millimeter scale flows. The S mu PIV system is based on an off-the-shelf stereomicroscope, with magnification between 0.69x and 30x, and a field of view between 7.5 x 6 mm and 250 x 200 mu m. Custom calibration targets were devised using printed circuit board technology, and applied at a magnification factor of 1.74, with a field of view of 4.75 x 3.8 mm. Measurement errors were assessed by moving a test block with fixed particles. Total system uncertainty in test block displacement transverse to the optical axis was 0.5% of the field of view, and 3% of the depth of field for motion along the optical axis. Approximately 20% of this uncertainty was due to the calibration target quality and test block procedures.
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