Improvements on the accuracy of derivative estimation from DPIV velocity measurements

Etebari, Ali; Vlachos, Pavlos P.

doi:10.1007/s00348-005-0085-6

Cited by 16 publications

(21 citation statements)

References 20 publications

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“…In removing the final 5% of the total energy in the system, any random noise associated with PIV was removed. A Compact-Richardson finite difference scheme was used to calculate vorticity fields, with low noise amplification and bias error (Etebari and Vlachos 2005). A local vortex identification scheme was then used to determine the location of vortex rings using the k ci criterion (see Zhou et al 1999;Chakraborty et al 2005).…”

Section: Methodsmentioning

confidence: 99%

The decay of confined vortex rings

et al. 2012

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Vortex rings are produced during the ejection of fluid through a nozzle or orifice, which occurs in a wide range of biological conditions such as blood flow through the valves of the heart or through arterial constrictions. Confined vortex ring dynamics, such as these, have not been previously studied despite their occurrence within the biological flow conditions mentioned. In this work, we investigate laminar vortex rings using particle image velocimetry and develop a new semi-empirical model for the evolution of vortex ring circulation subject to confinement. Here we introduce a decay parameter b which exponentially grows with increasing vortex ring confinement ratio, the ratio of the vortex ring diameter (D VR ) to the confinement diameter (D), with the relationship b ¼ 4:38 expð9:5D VR =DÞ; resulting in a corresponding increase in the rate of vortex ring circulation decay. This work enables the prediction of circulation decay rate based on confinement, which is important to understanding naturally occurring confined vortex ring dynamics.

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Section: Methodsmentioning

confidence: 99%

The decay of confined vortex rings

et al. 2012

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“…10) can be expressed by the contributions of the propagation of the velocity error ( u , Eq. 11) (de Kat and van Oudheusden 2012) through the derivative estimators, and the truncation error terms arising from finite resolution of the derivative estimators (Etebari and Vlachos 2005) ( trunc , Eq. 12).…”

Section: Pressure Piv Uncertainty Minimizationmentioning

confidence: 99%

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

McClure

Yarusevych

2017

Exp Fluids

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“…In part, this sensitivity is not surprising when considering the exponential propagation of uncertainty through spatial or temporal derivatives from PIV fields even with minimal error. 9,14,24 As expected, the challenges discussed above, including sparse seeding, high velocity gradients and poor image quality, largely influence WSS measurement uncertainty. However, the results are surprising when considering the small contributions from velocity gradient estimations.…”

Section: Discussionmentioning

confidence: 92%

“…Several options exist for gradient estimators such as high-order finite differencing and analytical fitting methods. 14,17,24,27 Finite differencing methods provide a straightforward technique to determine gradients between gridded vector locations. However, analytical fitting methods such as RBFs can result in a reduction in error and susceptibility to noise in comparison to finite difference approaches.…”

Section: Introductionmentioning

confidence: 99%

Time-Resolved Particle Image Velocimetry Measurements with Wall Shear Stress and Uncertainty Quantification for the FDA Nozzle Model

Raben

Hariharan

Robinson

et al. 2015

Cardiovasc Eng Tech

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We present advanced particle image velocimetry (PIV) processing, post-processing, and uncertainty estimation techniques to support the validation of computational fluid dynamics analyses of medical devices. This work is an extension of a previous FDA-sponsored multi-laboratory study, which used a medical device mimicking geometry referred to as the FDA benchmark nozzle model. Experimental measurements were performed using time-resolved PIV at five overlapping regions of the model for Reynolds numbers in the nozzle throat of 500, 2000, 5000, and 8000. Images included a twofold increase in spatial resolution in comparison to the previous study. Data was processed using ensemble correlation, dynamic range enhancement, and phase correlations to increase signal-to-noise ratios and measurement accuracy, and to resolve flow regions with large velocity ranges and gradients, which is typical of many blood-contacting medical devices. Parameters relevant to device safety, including shear stress at the wall and in bulk flow, were computed using radial basis functions. In addition, in-field spatially resolved pressure distributions, Reynolds stresses, and energy dissipation rates were computed from PIV measurements. Velocity measurement uncertainty was estimated directly from the PIV correlation plane, and uncertainty analysis for wall shear stress at each measurement location was performed using a Monte Carlo model. Local velocity uncertainty varied greatly and depended largely on local conditions such as particle seeding, velocity gradients, and particle displacements. Uncertainty in low velocity regions in the sudden expansion section of the nozzle was greatly reduced by over an order of magnitude when dynamic range enhancement was applied. Wall shear stress uncertainty was dominated by uncertainty contributions from velocity estimations, which were shown to account for 90-99% of the total uncertainty. This study provides advancements in the PIV processing methodologies over the previous work through increased PIV image resolution, use of robust image processing algorithms for near-wall velocity measurements and wall shear stress calculations, and uncertainty analyses for both velocity and wall shear stress measurements. The velocity and shear stress analysis, with spatially distributed uncertainty estimates, highlights the challenges of flow quantification in medical devices and provides potential methods to overcome such challenges.

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Improvements on the accuracy of derivative estimation from DPIV velocity measurements

Abstract: Fig. 2 Random error transmission ratio as a function of D/L Fig. 3 Vorticity bias error (%) as a function of spatial resolution

Cited by 16 publications

References 20 publications

The decay of confined vortex rings

The decay of confined vortex rings

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

Time-Resolved Particle Image Velocimetry Measurements with Wall Shear Stress and Uncertainty Quantification for the FDA Nozzle Model

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