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Oil dispersion under a deep-water plunging breaker of height 0.15 m was studied by coupling the Lagrangian particle tracking code (NEMO3D) with the population balance model (VDROP). The wave hydrodynamics obtained in Part I (Cui et al. in Environ Fluid Mech 2020 (in press)) was used as input. It was observed that droplet inertia and major forces on droplets significantly impacted the transport of oil droplets under wave conditions, and neglecting it caused less entrainment into the water column and horizontal spread of oil plume. For droplets less than 400 microns, the droplet size distribution (DSD) tended to follow a power-law distribution with an exponent close to − 2.3, which was consistent with earlier experimental observations by Delvigne and Sweeney (Oil Chem Pollut 4(4):281–310, 1988). The distribution of large size droplets evolved with time and showed agreement with a power-law distribution having an exponent of − 9.7 about 20 s after the passage of the wave train. Reducing the interfacial tension enhanced droplets breakup and increased the exponent of power-law distribution to − 6.1 for droplets smaller than 400 microns. It was also found that neglecting the vertical gradient of eddy diffusivity led to the accumulation of oil droplets in low eddy diffusivity regions at the bottom part of the wave breaker. The investigation herein could be used to obtain design values for breakers that could be used in oil spill models to predict the oil droplet size distribution.

The dispersion (transport and breakup) of oil droplets under breaking waves is essential for evaluating the impact of oil spills on the environment, and for designing countermeasures. In this part of the work (Part I), the hydrodynamics of a wave breaker in a 1.0 m deep tank obtained experimentally by Rapp and Melville (Philos Trans R Soc Lond A Math Phys Eng Sci 331(1622):735–800, 1990) using the dispersive focusing method was investigated numerically using Reynold-averaged Navier–Stokes (RANS) within the CFD code ANSYS Fluent. The renormalization group (RNG) k–e turbulence closure model was adopted to simulate wave turbulence, and the transient water–air interface was captured using volume of fluid (VOF) method. The simulated surface excursion and velocity fields matched closely the experimental observations during wave breaking. The energy dissipation of the wave crest showed good agreement with recent laboratory work and numerical simulations through evaluating the breaking parameter. The RANS approach was able to reproduce the turbulent kinetic energy field engendered by the breakers and simulated the residual turbulence within about two wave periods after the passage of the wave train. The VOF scheme Compressive provided a better agreement with the observation than the Geo-reconstruct scheme. The approach herein suggests that RANS method coupled with VOF in ANSYS Fluent is capable of capturing the major hydrodynamic forces and turbulence, and thus could be used to predict environmental processes within the breaking waves such as oil droplet formation and transport.

Understanding the size of oil droplets released from a jet in crossflow is crucial for estimating the trajectory of hydrocarbons and the rates of oil biodegradation/dissolution in the water column. We present experimental results of an oil jet with a jet-to-crossflow velocity ratio of 9.3. The oil was released from a vertical pipe 25 mm in diameter with a Reynolds number of 25 000. We measured the size of oil droplets near the top and bottom boundaries of the plume using shadowgraph cameras and we also filmed the whole plume. In parallel, we developed a multifluid large eddy simulation model to simulate the plume and coupled it with our VDROP population balance model to compute the local droplet size. We accounted for the slip velocity of oil droplets in the momentum equation and in the volume fraction equation of oil through the local, mass-weighted average droplet rise velocity. The top and bottom boundaries of the plume were captured well in the simulation. Larger droplets shaped the upper boundary of the plume, and the mean droplet size increased with elevation across the plume, most likely due to the individual rise velocity of droplets. At the same elevation across the plume, the droplet size was smaller at the centre axis as compared with the side boundaries of the plume due to the formation of the counter-rotating vortex pair, which induced upward velocity at the centre axis and downward velocity near the sides of the plume.

The transport of virus-laden particles was investigated numerically in an archetypical supermarket configuration of area 1,200 m2 and ceiling height of 4.5 m. The particles were tracked u...

Theoretical arguments and numerical investigations were conducted to understand the transport of oil droplets under ice. It was found that the boundary layer (BL) in the water under ice produces a downward velocity that reaches up to 0.2% of horizontal current speed, and is, in general, larger than the rise velocity of 70 μm oil droplets. The eddy diffusivity was found to increase with depth and to decrease gradually afterward. Neglecting the gradient of eddy diffusivity when conducting Lagrangian transport of oil droplets would result in an unphysical spatial distribution. When the downward velocity of water was neglected, oil accumulated at the water-ice interface regardless of the attachment efficiency. The lift force was found to scrape off droplets of the ice, especially for droplets ≤ 70 μm. These findings suggest that previous oil spill simulations may have overestimated the number of small droplets (≤70 μm) at the water-ice interface.

The dispersion of oil droplets in rivers was numerically investigated for uniform flow in a hypothetical wide river with a depth of 3.0 m. The river hydrodynamics profile was used in conju...

Knowledge of the droplet size distribution (DSD) of spilled oil is essential for the accurate prediction of oil transport, dissolution, and biodegradation. Breaking waves play important roles in oil droplet formation in oceanic environments. To understand the effects of breaking waves on oil DSD, oil spill experiments were designed and performed in a large-scale wave tank. A plunging breaker with a height of about 0.4 m was produced using the dispersive focusing method within the tank. Oil placed within the breaker resulted in a DSD that was measured using a shadowgraph camera and found to fit a Gaussian distribution (&micro, = 1.2 mm, &sigma, 2 = 0.29 mm2). For droplets smaller than 1500 &micro, m, the number-based DSD matched the DS1988 correlation [1], which gives N(d) ~ d-2.3, but this was N(d) ~ d-9.7 for droplets larger than 1500 &micro, m. An order of magnitude investigation revealed that a Gaussian volume-based DSD results in a number-based DSD that may be approximated by d-b (with b &asymp, 2) for small diameters (relative to the mean), which explains the occurrence of the DS1988 correlation. With the measured wave hydrodynamics, the VDROP model was adopted to simulate the DSD, which closely matched the observed DSD. The present results reduce the empiricism of the DS1988 correlation.

Langmuir supercells (LS) are full-depth Langmuir circulations in unstratified shallow shelves. A Reynolds-averaged Eulerian formulation is developed resolving LS as a secondary component to the wind-driven mean shear current. This formulation is combined with Lagrangian particle tracking to investigate oil droplet entrainment induced by LS as a function of wind stress. Two cases are simulated, one in which 500-μm oil droplets are released into a steady field of LS generated by a wind stress of 0.1 N m−2 and waves of intermediate wavelength λ = 6 H where H = 15 m is the water column depth, significant amplitude of 0.6 m and period of 8 s. In the second case, the 500-μm oil droplets are released into a steady field of LS generated by the same wave forcing but with a weaker wind stress of 0.025 N m−2. It is found that the greater wind stress leads to LS able to spread the droplets throughout upwelling and downwelling limbs of the cells within the first 80 minutes after release. The weaker wind leads to weaker LS that, within the same time after release, limit the dispersion of the droplets to the downwelling limbs of the cells forming Stommel retention zones for a prolonged time.

Understanding the shear breakup in jet flows and the formation of droplets from ligaments is important to determine the final droplet size distribution (DSD). The initial droplet size, which affects the final DSD, is considered to be generated by the shear breakup. Large eddy simulation (LES) was performed to investigate the shear breakup in liquid-liquid jet flows. The explicit Volume of Fluid (VOF) model with the geometric reconstruction scheme was used to capture the oil-water interface. The estimated oil distribution including wave peaks, ligaments, droplets and water streaks were compared to the experiments with a good agreement. The estimated DSD matched with the measurements favorably well. In the simulation, the formation of droplets with a smooth and curved surface from ligaments or sheet-like structures was obtained. Different mechanisms were observed along with the shear layer including the formation of droplets from ligament through the capillary forces, breakage of a droplet into smaller ones and attachment of a droplet to a ligament. The destructive shear forces and resisting surface tension forces were quantified on stretching and retracting ligaments. The influence of internal viscous force was found to be negligible due to low oil viscosity. The critical capillary number was found to be larger than 5.0 for ligaments breaking with the shear breakup. The capillary number was below unity for retracting ligaments. The coalescence of two equal-sized droplets was obtained in the shear breakup region. The shear stress magnitude at the contact region increased more than two folds. The total surface area decreased nearly 20% after the coalescence.

Understanding the trajectory of oil droplets in crossflow jets is important to estimate the pathways of hydrocarbons and to plan countermeasures. We report experimental results of an oil jet with release velocity around 1.5 m/s in a crossflow of 0.3 m/s. The hydrodynamics of the jet obtained with the Large Eddy Simulation (LES) were used to predict the migration of the oil droplets. Two Lagrangian techniques were explored, one with the inertia of the droplet is considered and the other that treats the droplets as massless particles with rising velocities corresponding to their size. We did not note a large difference between the two approaches. The droplets showed stronger segregation in the vertical direction, which renders the usage of a Gaussian distribution approximation in the vertical inapplicable. The dispersion coefficient at each direction was computed for different-sized droplets. The eddy diffusivity computed based on Boussinesq gradient approximation using the LES data was compared with the dispersion coefficients obtained based on Lagrangian tracking. We also found that droplets 500 μ m and larger escape the vortex while smaller ones get trapped within the vortex. A similar outcome was observed using a vortex trapping function based on inward–outward force balancing at the elevation of the vortex core. The counter-rotating vortex pair (CVP) altered the distribution of droplets of 1 mm and smaller significantly, and bimodal concentration distributions with peaks near the CVP vortex cores and minimum concentration near the center plane were obtained in the lateral–horizontal​ direction. Therefore, measurements of the oil droplet size distribution (DSD) in the center plane of crossflow jets could underestimate the number of small droplets in the whole plume.

Experimental results were presented for the release of diesel oil from a one-inch (2.5 cm) vertical pipe in a crossflow at 0.27 m/s. The ratio of jet velocity to crossflow speed was 5.0 and the Reynolds number based on jet velocity and pipe diameter was 7.1 × 10 3 . In the experiments, the plume shape was photographed, and the oil droplets were measured at two vertical locations on the center axis of the plume. Acoustic Doppler velocimetry (ADV) data was also obtained and compared to numerical predictions. The plume was simulated using large eddy simulation (LES), and the mixture multiphase model. The impact of the oil buoyancy was captured by adding a transport term to the volume fraction equation. Using the rise velocity based on d50 (volume-median) droplet size in the lower part of the plume allowed us to capture the lower boundary of the plume, but the estimated upper boundary of the plume penetrated less into the crossflow as compared to the experimental findings. However, using the rise velocity of the d50 at the upper part of the plume allowed one to estimate the upper boundary of the plume. As the droplets are too small to be resolved by the LES, we could not use a systematic approach to allow the multiphase plume to spread to mimic the observations. Based on the simulation results, the interaction between the jet and crossflow yielded small-sized flow structures near the upper boundary of the plume. The wake vortices initiated from the leeward side of the plume showed an alternating vorticity pattern in the wake. The shear layer vortices were induced by Kevin-Helmholtz instabilities mostly on the windward side of the plume. The formation of counter rotating vortex pair (CVP) altered greatly the hydrodynamics of the jet from that of a vertical jet to manifest flow reversals in all directions. The formation of CVP is likely to enhance the mixing of chemicals and droplets within the plume.

Shape Memory polymers (SMPs) is a novel class of smart polymeric materials that have been attracted tremendous scientific interest within the last decades. SMPs have the ability to “remember” their original shape even after undergoing significant deformation into a temporary shape. For most first generation of SMPs, the shape memory effect was accomplished by a thermally induced process, triggered in many different ways, such as heating/cooling, electromagnetic field and infrared light. The transient shape in thermally induced SMPs is due to a glassy phase or a semi-crystalline phase. The thermally induced SMPs which temporary shape is fixed through crystallization is called crystallizable shape memory polymers (CSMPs). For traditional CSMPs, their original shape is predefined and is not able to be reprogrammed. This limits the applications of the CSMPs. Recently, a new class of CSMPs has been developed. These materials can perform a typical thermally induced shape memory cycle, but their original shape can be reprogrammed through exposing to UV light. The shape reprogramming effect is governed by light induced covalent bonds exchange reaction, while the shape memory effect-as typical CSMPs-is due to solid-phase crystallization. In this work, we focus on modeling the mechanical behavior of this new class of structurally dynamic CSMPs. The framework used in developing the model is built upon the theory of multiple natural configurations[1]. The model has been applied to solve a specific boundary condition problem, namely uni-axial tension. Furthermore, we implement our model through Abaqus (commercial finite element package) subroutine UMAT to simulate 3D behavior of this attractive material.Copyright © 2015 by ASME