Accurate estimate of turbulent dissipation rate using PIV data

Xu, Duo; Chen, Jun

doi:10.1016/j.expthermflusci.2012.09.006

Cited by 91 publications

(40 citation statements)

References 21 publications

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“…Let us note also that at each point x the turbulent kinetic energy u (x) 2 is at least 5 times the mean flow energy u(x) 2 . From the two-dimensional PIV velocity fields, we can also measure the energy dissipation rate which is estimated using the longitudinal second-order velocity structure function 2 , where r is the spatial variable [10][11][12]. Figure 2(a) shows the variation of D LL (r ) 3/2 /r (normalized by the Kolmogorov constant C K ) as a function of r for different disk frequencies.…”

Section: Experimental Setup and Measurement Techniquesmentioning

confidence: 99%

Fragmentation of magnetic particle aggregates in turbulence

2018

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Particle aggregates are frequently encountered in many natural and industrial environments. We describe here the stationary state of the fragmentation process of inertial scale particle aggregates in turbulence, i.e., when the particles and the aggregates are larger than the Kolmogorov dissipative scale η. For this purpose, we place at the initial time a large aggregate of millimetric, nearly neutrally buoyant magnetic particles in a high-Reynoldsnumber turbulent von Kármán flow. Turbulent fluctuations impose external stresses that tend to fragment the initial and the subsequent aggregates, contrary to the magnetic dipoles that impose torques and forces on the magnets responsible for cohesion. Using video image analyses, we perform the three-dimensional reconstruction of the aggregates and measure their characteristic sizes. The average number of particles inside each aggregate can then be deduced as a function of the intensity of turbulence. Assuming a Kolmogorov inertial scaling law for the turbulent velocity increments, we predict theoretically an aggregate mean size which is in agreement with our experimental results.

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Section: Experimental Setup and Measurement Techniquesmentioning

confidence: 99%

Fragmentation of magnetic particle aggregates in turbulence

2018

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“…In the PIV method a two-dimensional velocity distribution is obtained. However, under the assumption of isotropic turbulence, the missing elements in the z direction can be replaced (Xu and Chen, 2013;Hoque et al, 2015) using the following relationships (3), (4) and (5)…”

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confidence: 99%

Turbulent Energy Dissipation Rate and Turbulence Scales in the Blade Region of a Self-Aspirating Disk Impeller

Stelmach

Musoski

Kuncewicz

et al. 2019

JAFM

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Instantaneous radial and axial velocitieques of water in the tank with a self-aspirating disk impeller operating without gas dispersion were measured by the PIV method. A comparison of mean square velocity pulsations confirmed previous observations that the area in which turbulence is non-isotropic is small and extends about 3 mm above and under the impeller and radially 12,5 mm from the impeller blade tip. Based on velocity measurements, the distributions of energy dissipation rates were determined using the dimensional equation  = C·u' 3 /D and Smagorinsky model. Adoption of the results of the dimensional equation as a reference value allowed us to determine the Smagorinsky constant value. This value appeared to be smaller than the values given in the literature. It has been shown that eddies in a small space near the impeller had sufficient energy to break up gas bubbles flowing out of the impeller. Based on the obtained energy dissipation rate distributions, appropriate turbulence scales were determined.

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“…Therefore, PIV and LSV can only be distinguished by the so called source density, which means the amount of particles in the same place. In a speckle pattern, one has a high density of particles, characterizing the LSV approach, while the low density of particles are commonly linked to the PIV applications in fluids measurements (Adrian, 1991;Volker et alli, 2011;Ertürk et alli, 2013;Xu and Chen, 2013).…”

Section: Introductionmentioning

confidence: 99%

Maps of deformations in a cantilever beam using particle image velocimetry (PIV) and speckle patterns

Braga

Magalhães

Melo

et al. 2015

Rem: Rev. Esc. Minas

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PIV (particle image velocimetry) has been spreading in studies that use the movement of particles to monitor the displacement of an object or the flow of a fluid by means of velocity vectors using optical techniques and second order statistics. PIV is also known as laser speckle velocimetry when associated with speckle patterns. This technique has been used in works involving fluids, in general, building a map of velocity vectors representing the flow under analysis. This paper presents an approach by using PIV associated to speckle patterns for deformation measurements in a cantilever beam (ASTM A36 steel), one of the most common examples used in civil engineering, without the introduction of external particles. Results showed that the difference between PIV associated to speckle patterns and the analytic displacement values is increased along the beam length for a load of 1.96 N as an evidence of sensitivity of the proposed measurement method. This indicates that PIV is also capable for detecting displacement fields associated with laser speckle patterns in solid mechanics generating a map of deformation as an additional option for non-destructive tests.

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Accurate estimate of turbulent dissipation rate using PIV data

Cited by 91 publications

References 21 publications

Fragmentation of magnetic particle aggregates in turbulence

Fragmentation of magnetic particle aggregates in turbulence

Turbulent Energy Dissipation Rate and Turbulence Scales in the Blade Region of a Self-Aspirating Disk Impeller

Maps of deformations in a cantilever beam using particle image velocimetry (PIV) and speckle patterns

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