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Register a new userParticle and particle pair dispersion in turbulence modeled with spatially and temporally correlated stochastic processes
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In this paper we present a new model for modeling the diffusion and relative dispersion of particles in homogeneous isotropic turbulence. We use an Heisenberg-like Hamiltonian to incorporate spatial correlations between fluid particles, which are modeled by stochastic processes correlated in time. We are able to reproduce the ballistic regime in the mean squared displacement of single particles and the transition to a normal diffusion regime for long times. For the dispersion of particle pairs we find a $t^{2}$-dependence of the mean squared separation at short times and a $t$-dependence for long ones. For intermediate times indications for a Richardson $t^{3}$ law are observed in certain situations. Finally the influence of inertia of real particles on the dispersion is investigated.
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Immiscible fluid displacement in porous media is fundamental for many environmental processes, including infiltration of water in soils, groundwater remediation, enhanced recovery of hydrocarbons and carbon geosequestration. Microstructural heterogeneity, in particular of particle sizes, can significantly impact immiscible displacement. For instance, it may lead to unstable flow and preferential displacement patterns. We present a systematic, quantitative pore-scale study of the impact of spatial correlations in particle sizes on the drainage of a partially-wetting fluid. We perform pore-network simulations with varying flow rates and different degrees of spatial correlation, complemented with microfluidic experiments. Simulated and experimental displacement patterns show that spatial correlation leads to more preferential invasion, with reduced trapping of the defending fluid, especially at low flow rates. Numerically, we find that increasing the correlation length reduces the fluid-fluid interfacial area and the trapping of the defending fluid, and increases the invasion pattern asymmetry and selectivity. Our experiments, conducted for low capillary numbers, support these findings. Our results delineate the significant effect of spatial correlations on fluid displacement in porous media, of relevance to a wide range of natural and engineered processes.
The collision rate of particles suspended in turbulent flow is critical to particle agglomeration and droplet coalescence. The collision kernel can be evaluated by the radial distribution function (RDF) and radial relative velocity (RV) between particles at small separations $r$. Previously, the smallest $r$ was limited to roughly the Kolmogorov length $eta$ due to particle position uncertainty and image overlap. We report a new approach to measure RDF and RV near contact ($r/a: approx$ 2.07, $a$ particle radius) overcoming these limitations. Three-dimensional particle tracking velocimetry using four-pulse Shake-the-Box algorithm recorded short particle tracks with the interpolated midpoints registered as particle positions to avoid image overlap. This strategy further allows removal of mismatched tracks using their characteristic false RV. We measured RDF and RV in a one-meter-diameter isotropic turbulence chamber with Taylor Reynolds number $Re_lambda=324$ with particles of 12-16 $mu$m radius and Stokes number $approx$ 0.7. While at large $r$ the measured RV agrees with the literature, when $r<20eta$ the first moment of negative RV is 10 times higher than direct numerical simulations of non-interacting particles. Likewise, when $r>eta$, RDF scales as $r^{-0.39}$ reflecting RDF scaling for polydisperse particles in the literature , but when $rlessapproxeta$ RDF scales as $r^{-6}$, yielding 1000 times higher near-contact RDF than simulations. Such extreme clustering and relative velocity enhancement can be attributed to particle-particle interactions. Uncertainty analysis substantiates the observed trends. This first-ever simultaneous RDF and RV measurement at small separations provides a clear glimpse into the clustering and relative velocities of particles in turbulence near-contact.
The radial relative velocity between particles suspended in turbulent flow plays a critical role in droplet collision and growth. We present a simple and accurate approach to RV measurement in isotropic turbulence - planar 4-frame particle tracking velocimetry - using routine PIV hardware. This study demonstrates the feasibility of accurately measuring RV using routine hardware and verifies, for the first time, the path-history and inertial filtering effects on particle-pair RV at large particle separations experimentally.
In this paper we numerically investigate the influence of dissipation during particle collisions in an homogeneous turbulent velocity field by coupling a discrete element method to a Lattice-Boltzmann simulation with spectral forcing. We show that even at moderate particle volume fractions the influence of dissipative collisions is important. We also investigate the transition from a regime where the turbulent velocity field significantly influences the spatial distribution of particles to a regime where the distribution is mainly influenced by particle collisions.
We use an extended laser Doppler technique to track optically the velocity of individual particles in a high Reynolds number turbulent flow. The particle sizes are of the order of the Kolmogorov scale and the time resolution, 30 microseconds, resolves the fastest scales of the fluid motion. Particles are tracked for mean durations of the order of 10 Kolmogorov time scales. The fastest scales of the particle motion are resolved and the particle acceleration is measured. For neutrally buoyant particles, our measurement matches the performance of the silicon strip detector technique introduced at Cornell University cite{Voth,MordantCornell}. This reference dynamics is then compared to that of slightly heavier solid particles (density 1.4) and to air bubbles. We observe that the acceleration variance strongly depends on the particle density: bubbles experience higher accelerations than fluid particles, while heavier particles have lower accelerations. We find that the probability distribution functions of accelerations normalized to the variance are very close although the air bubbles have a much faster dynamics.
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