Phase discrimination method for simultaneous two-phase separation in time-resolved stereo PIV measurements

Cheng, Yafang; Pothos, Stamatios; Díez, F. J.

doi:10.1007/s00348-010-0878-0

Cited by 31 publications

(13 citation statements)

References 30 publications

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“…Kiger and Pan, 2000;Khalitov and Longmire, 2002;Cheng et al, 2010). Fluid velocities were estimated from pre-processed images of the seeding particles with larvae masked out.…”

Section: Image Processing and Analysismentioning

confidence: 99%

Hydrodynamic sensing and behavior by oyster larvae in turbulence and waves

Fuchs

Gerbi

Hunter

et al. 2015

Journal of Experimental Biology

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Hydrodynamic signals from turbulence and waves may provide marine invertebrate larvae with behavioral cues that affect the pathways and energetic costs of larval delivery to adult habitats. Oysters (Crassostrea virginica) live in sheltered estuaries with strong turbulence and small waves, but their larvae can be transported into coastal waters with large waves. These contrasting environments have different ranges of hydrodynamic signals, because turbulence generally produces higher spatial velocity gradients, whereas waves can produce higher temporal velocity gradients. To understand how physical processes affect oyster larval behavior, transport and energetics, we exposed larvae to different combinations of turbulence and waves in flow tanks with (1) wavy turbulence, (2) a seiche and (3) rectilinear accelerations. We quantified behavioral responses of individual larvae to local instantaneous flows using twophase, infrared particle-image velocimetry. Both high dissipation rates and high wave-generated accelerations induced most larvae to swim faster upward. High dissipation rates also induced some rapid, active dives, whereas high accelerations induced only weak active dives. In both turbulence and waves, faster swimming and active diving were achieved through an increase in propulsive force and power output that would carry a high energetic cost. Swimming costs could be offset if larvae reaching surface waters had a higher probability of being transported shoreward by Stokes drift, whereas diving costs could be offset by enhanced settlement or predator avoidance. These complex behaviors suggest that larvae integrate multiple hydrodynamic signals to manage dispersal tradeoffs, spending more energy to raise the probability of successful transport to suitable locations.

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“…Kiger and Pan, 2000;Khalitov and Longmire, 2002;Cheng et al, 2010). Fluid velocities were estimated from pre-processed images of the seeding particles with larvae masked out.…”

Section: Image Processing and Analysismentioning

confidence: 99%

Hydrodynamic sensing and behavior by oyster larvae in turbulence and waves

Fuchs

Gerbi

Hunter

et al. 2015

Journal of Experimental Biology

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“…Before calculating the fluid velocity vectors we equalized the image backgrounds, removed noise and masked out the larvae to obtain good estimates of background fluid flow and to limit error in the calculation of individual larval velocities. The image intensity varied spatially because of IR light attenuation, so we were unable to use standard procedures of subtracting the mean background intensity of each individual image and removing pixel-scale noise with a median filter (Khalitov and Longmire, 2002;Cheng et al, 2010). Instead, we equalized the background by calculating the mean image intensity over each 10min sampling interval and subtracting the mean intensity from each image, repeating for frame 1 and frame 2 images.…”

Section: Image Processingmentioning

confidence: 99%

Active downward propulsion by oyster larvae in turbulence

Fuchs

Hunter

Schmitt

et al. 2012

Journal of Experimental Biology

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SUMMARYOyster larvae (Crassostrea virginica) could enhance their settlement success by moving toward the seafloor in the strong turbulence associated with coastal habitats. We characterized the behavior of individual oyster larvae in grid-generated turbulence by measuring larval velocities and flow velocities simultaneously using infrared particle image velocimetry. We estimated larval behavioral velocities and propulsive forces as functions of the kinetic energy dissipation rate ε, strain rate γ, vorticity ξ and acceleration α. In calm water most larvae had near-zero vertical velocities despite propelling themselves upward (swimming). In stronger turbulence all larvae used more propulsive force, but relative to the larval axis, larvae propelled themselves downward (diving) instead of upward more frequently and more forcefully. Vertical velocity magnitudes of both swimmers and divers increased with turbulence, but the swimming velocity leveled off as larvae were rotated away from their stable, velum-up orientation in strong turbulence. Diving speeds rose steadily with turbulence intensity to several times the terminal fall velocity in still water. Rapid dives may require a switch from ciliary swimming to another propulsive mode such as flapping the velum, which would become energetically efficient at the intermediate Reynolds numbers attained by larvae in strong turbulence. We expected larvae to respond to spatial or temporal velocity gradients, but although the diving frequency changed abruptly at a threshold acceleration, the variation in propulsive force and behavioral velocity was best explained by the dissipation rate. Downward propulsion could enhance oyster larval settlement by raising the probability of larval contact with oyster reef patches.

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“…In order to analyze the turbulent structure in the two-phase flow, simultaneous measurements of tracers (1 μm) and particles (42 μm) have been carried out with the experimental setup of the particle-laden jet. In this study, the phase discrimination technique was applied to get images of the tracers and particles separately as same as Cheng et al [6]. After the phase discrimination process tracer only images and particle only images obtained.…”

Section: B Phase Separationmentioning

confidence: 99%

“…Therefore, both particles captured in same picture, so image processing technique was used for phase separation. For particle detection, images of both the particles and the tracers are separated from the artificial two-phase image by means of the technique employed by Kiger and Pan [5], Cheng et al [6], and Zhang et al [7] This paper, particle dispersion characteristics, mean and fluctuating velocity distributions of particle-laden coaxial jets is investigated experimentally under various mass loading conditions.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of particle distributions in particle-laden coaxial jets

Yoon¹,

Yoon²

2015

13th International Energy Conversion Engineering Conference

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The characteristics of particle distribution and fluid velcotiy were investigated in a particle-laden coaxial jets. Simultaneous measurements were taken of the mean velocity and concentration of the small traces (1 µm) and large particles (42.5 µm) in a particle-laden flow. Velocity characteristics of small tracers and large particles was obtained using particle image velocimetry (PIV) and particle tracking velocimetry (PTV), repectively. Particle concentration was obtained by counting the number of particles in each interrogation cell of the PIV/PTV images. The experimental results reveal the significant influence of the solid particle mass loading on the flow charactersitcs. The gas-phase velocity in the developing region, which is lower than a single-phase velocity, increases as a loading ratio, but there is a higher mean velocity in the developed region due to the momentum exchange between particles and fluid. The deviation difference of particle local mean concentration maximized at the edge of the shear flow region. It was also observed that vortical structure influence on the non-random concentration fluctuations of particle and as particle mass loading increases intensity of vorticity decreased than deviation parameter also decreased. Nomenclature u t = terminal velocity τ s = relaxation time = density of particle = density of fluid = dimeter of particle St0 = Stokes number at the nozzle exit Re p = particle Reynolds number ϕ = particle mass loading ratio D = deviation parameter Rein = Reynolds numder of inner gas flow Reout = Reynolds number of outer gas flow 0 = Drag coefficient at the nozzle exit = gas phase vleocity = particle velocity = particle mean number density = tracer mean number density

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Phase discrimination method for simultaneous two-phase separation in time-resolved stereo PIV measurements

Cited by 31 publications

References 30 publications

Hydrodynamic sensing and behavior by oyster larvae in turbulence and waves

Hydrodynamic sensing and behavior by oyster larvae in turbulence and waves

Active downward propulsion by oyster larvae in turbulence

Experimental investigation of particle distributions in particle-laden coaxial jets

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