A series of GOMRI-sponsored experimental and computational studies have discovered, elucidated and quantified the impact of small-scale processes on the dispersion, transport and weathering of crude oil slicks and subsurface plumes. Physical interfacial phenomena occurring at micron-scales include the formation of particle-stabilized emulsions, penetration of particles into oil droplets, formation of compound water-containing oil droplets during plume breakup, and the mechanisms affecting the breakup of oil into micro-droplet by tip streaming resulting from the drastic reduction in interfacial tension upon introduction of dispersant. Efforts aimed at development targeted delivery of surfactants have introduced solvent-free halloysite nanotubes that can be filled with surfactants, and preferentially released at oil-water interface. Buoyant surfactant-based gels, which enhance their encounter rates with oil slicks and adhere to weathered oil have also been developed. Studies of oil-bacteria interactions during early phases of biodegradation and shown how the bacteria, some highly active, attach to the oil-water interfaces and form complex films. Clay-decorated droplets sequester these bacteria and promote the propagation of these biofilm. Long extracellular polymeric substance (EPS) streamers generated by these biofilms form connected networks involving multiple droplets and debris, as well as increase the drag on the oil droplets. At 0.01–10 m scales, the generation of subsurface and airborne crude oil droplets by breaking waves, subsurface plumes and raindrop impact have been quantified. For waves, premixing the oil with dispersant reduces the droplets sizes to micron- and submicron-scales, and changes the slope of their size distribution. Without dispersant, the droplet diameters can be predicted based on the turbulence scales. With dispersant, the droplets are much smaller than the turbulence scales owing to the abovementioned tip-streaming. Aerosolization of oil is caused both by the initial splash and by subsequent bubble bursting, as entrained bubbles rise to the surface. Introduction of dispersant increases the airborne nano-droplet concentration by orders of magnitude, raising health questions. Dispersant injection also reduces the size of droplets in subsurface plumes, affecting the subsequent dispersion of these plume by currents and turbulence. Advancements have also been made in modeling of dissolution of oil in plumes, as well as in applications of Large Eddy Simulations (LES) to model plumes containing oil droplets and gas bubbles. The new multiscale framework, which accounts for the droplet size distribution and mass diffusion, can simulate the near- and far-fields of plumes, and predict the effect of vertical mixing promoted by turbulence on the transport of dispersed oil.

Establishment of the Gulf of Mexico Research Initiative (GoMRI) in the aftermath of the Deepwater Horizon (DWH) blowout has led to a massive increase in the research aimed at characterizing the environmental, economic, and societal impact of oil spills. Once released into the marine environment, the physical and chemical properties of oil change rapidly in a series of processes generally characterized as weathering. While not a complete list, these processes include: (a) evaporation and dissolution of light oil components (Stevens, Thibodeaux et al. 2015, Gros, Socolofsky et al. 2016) (b) breakup of oil patches into subsurface droplets, which are subsequently transported/dispersed by subsurface current and turbulence or as aerosols by wind (NRC 2005, Li, Lee et al. 2008, Pan, Zhao et al. 2017, Zhao, Gao et al. 2017), (c) emulsification of the oil involving formation of stable dense microdroplet suspensions, which change the mechanical properties of the oil and affect its dispersion (Eley, Hey et al. 1988, Bredholt, Josefsen et al. 1998, Belore, K et al. 2011), (d) photo-oxidation by sunlight (Garrett, Pickering et al. 1998, Radović, Aeppli et al. 2014, Ward, Sharpless et al. 2018), and biodegradation (Hazen, Dubinsky et al. 2010, Valentine, Kessler et al. 2010, Atlas and Hazen 2011, Kostka, Prakash et al. 2011, Valentine, Mezić et al. 2012) by interaction with marine life at varying scales starting from microbes, and (e) formation and settling of oil-particle aggregates (OPA) (Fitzpatrick, Boufadel et al. 2015, Zhao, Boufadel et al. 2016, Zhao, Boufadel et al. 2017), which presumably involves interactions with exopolymeric substances (EPS)(Brakstad, Nordtug et al. 2015) produced by microbes. Understanding these processes is essential for predicting the fate of oil after a spill, a critical step in making informed decisions about proper response options aimed at mitigating its adverse effects. These options primarily include mechanical confinement by booms, spraying or injection of dispersants, in-situ burning (Allen and Ferek 1993, Buist, Ross et al. 1994, Buist, McCourt et al. 1998, Mullin and Champ 2003, Dickins, Brandvik et al. 2008, Allen, Jaeger et al. 2011, Fingas 2011), and monitoring the progress of the oil without active participation.

The present paper summarizes the findings of several GOMRI-funded studies focusing on measuring and modeling small scale physical and chemical processes affecting the interactions of oil with the marine environment. The experimental research has covered: (i) physical breakup of oil slicks and its subsequent transport by currents and turbulence with a brief summary of research involving oil plumes (section 2), (ii) interfacial phenomena involving oil, dispersants, and bacteria and their effect on the transport and biodegradation of oil (section 3), (iii) effects of dispersants on the aerosolization of oil (Section 4). The modeling efforts include: (iv) stability and mass transport at oil-water interfaces (section 5), (v) applications of Large Eddy Simulations (LES) to model the dynamics of oil plumes (Section 6), and (vi) modeling of droplet size distributions (Section 7). An overview and implications of these findings are discussed in Section 8. Each section specifies the research groups and institutions involved and provides relevant references for published literature. These review does not cover the topic of subsurface plumes – they are discussed in a separate parallel paper.

As part of the DROPPS I–III Consortium, a series of experimental studies Johns Hopkins University (JHU) have focused on characterizing processes affecting the breakup of surface slicks by surface waves (Li, Miller et al. 2017, Afshar-Mohajer, Li et al. 2018) and subsurface plumes (Murphy, Xue et al. 2016, Xue and Katz 2019) into droplets, and the subsequent transport of the subsurface droplets by current and turbulence. The measurements have been performed in a 6×0.6×0.3 m transparent wave tank (Fig. 1), and the subsurface droplet statistics have been measured using digital holography at two magnifications (Fig. 2). The comprehensive database provides the spatio-temporal size distribution of subsurface droplets as a function of wave energy, dispersant concentration (e.g. Fig. 3), oil viscosity, depth, and time after wave breaking (e.g. Fig. 4). Furthermore, the time evolution of turbulence in the tank has also been measured using article image Velocimetry (PIV). The unique database has been used for since then for validating predictions of wave breaking and for modeling the of oil transport after spills, and to support parallel numerical simulations and characterization of chaotic phenomena in wave breaking (Wei, Li et al. 2018).

Fig. 1:

The JHU 6m wave tank used for measuring the subsurface and airborne droplet statistics.

Fig. 1:

The JHU 6m wave tank used for measuring the subsurface and airborne droplet statistics.

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Fig. 2:

Digital holography system for measuring the droplets in the wave tank.

Fig. 2:

Digital holography system for measuring the droplets in the wave tank.

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Fig. 3:

Effect of dispersant on the initial (2–10s) subsurface crude oil droplet size distributions generated by breaking waves. MA–magnification. (Li et al. (2017).

Fig. 3:

Effect of dispersant on the initial (2–10s) subsurface crude oil droplet size distributions generated by breaking waves. MA–magnification. (Li et al. (2017).

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Fig. 4:

Time evolution of crude oil droplet size distribution for the first minute after wave breaking (points) compared to predictions of a 1D advection-diffusion model (solid lines) (Li et al. 2017).

Fig. 4:

Time evolution of crude oil droplet size distribution for the first minute after wave breaking (points) compared to predictions of a 1D advection-diffusion model (solid lines) (Li et al. 2017).

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As Fig. 3 shows, the dispersant causes a dramatic changes to the droplet size distributions, reducing the concentration of mm size droplets, and increase the concentrations of micro-droplets. It should be noted that the minimum size indicated in Fig. 3 is caused by the resolution of the holography system. For Dispersant to Oil ratio (DOR) of 1:25, the water contains large cloud of submicron droplets. While micron scale droplets are generated by the so-called tip streaming (Gopalan and Katz 2010), the mechanism causing generation of subsurface nano-droplets has not been established, and remains an open question. Considering that the physical length scales are many orders of magnitude larger (Kolmogorov scales >100μm), it is unlikely that the nano-droplet are generated by hydrodynamic shear.

For crude oil without dispersant, the sample data in Fig. 4 shows how the size distribution evolves with time for the first one minute after wave breaking. As is evident (an expected), the measured rate of droplet disappearance from the water column increases with droplet size. Accompanying velocity measurements (using Particle image Velocimetry – PIV) have been used for determining the corresponding evolution of turbulence level, dissipation rate, (integral and Kolmogorov) scales, and diffusion rate. The solid lines in Fig. 4 are predictions of the time evolution of size distribution based on a solution to the one-dimensional unsteady advection (by buoyancy) – turbulent diffusion equation for the droplets in the tank. Fig. 4 shows that once fragmentation and coalescence are not significant, the droplet dynamics could be predicted and modeled effectively by combining effects of buoyant rise and turbulent dispersion. This model fails once dispersants are involved (Li, Miller et al. 2017) owing to subsequent fragmentation of the droplets, presumably by tip streaming (Gopalan and Katz 2010), requires more sophisticated modeling tools (see discussion in Section 7). Other notable relevant studies include prior measurements of droplet generated by breaking waves can be found in (Delvigne and Sweeney 1988, Li, Kepkay et al. 2007, Li, Lee et al. 2008, Li, Lee et al. 2009, Li, Lee et al. 2009, Li, Lee et al. 2010).

While subsurface plumes are not a primry topic in this paper (they are discussed in a separate paper), in terms of size distribution measurements and modeling, one should mention the semi-empirical models derived from laboratory measuremtns of subsurface blowouts at SINTEF (Brandvik, Johansen et al. (2013), Johansen, Brandvik et al. (2013), Johansen, Reed et al. (2015), and the measurements of depth effects on droplet size distributions (Brandvik, Storey et al. (2019), which has been performed using the SINTEF silhouette camera (Davies, Brandvik et al. (2017). The associated models are discussed in detail in Section 7. In terms of techniques, the work of Davies, Brandvik et al. (2017) at SINTEF introduce a new approach for automated classification of oil droplets based on quasi-silhouettes of the particles. A particle-specific transmittance is used to classify the particle type rather than its size and shape. The system can be used in mixtures of oil droplet, gas bubbles, oil coated bubbles and large flocs during an oil spill. Such characterization is important during a spill to accurately measure the droplet and gas bubble size distributions and to characterize oil marine snow formation as comparted to natural marine snow formation. Finally, the droplet size distributions in oil jets in cross flow have been measured in a towing tank at JHU (Murphy, Xue et al. 2016).

In terms of impact, during the DWH subsurface dispersant injection (SSDI) operations, responders have noted that the oil surfacing during SSDI does not seem to reach the surface, but stays just below the surface, unlike fresh oil. SSDI is known to create wider and thinner surface slicks Daae, Skancke et al. (2018). By modeling different half-lives for dispersant retention in the oil droplets, they show that the disassociation time (for the oil droplets and dispersant) modifies the surface oil signature. With increasing dispersant concentration and/or higher wind speeds, the oil droplets tended to stay submerged for a longer time. Hypothetically, this trend could mean reduced tarball formation, as there would not be continuous oil at the surface.

3.1 Small scale phenomena at the oil-water interface

This section summarizes research performed at Tulane University in a group led by V. John. The Deepwater Horizon incident has led to tremendous research efforts in oil spill remediation technologies (John, Arnosti et al. 2016), including application of dispersants. As shown in the previous section, by reducing the oil-water interfacial tension dispersants enhance the breakup the oil into small droplets that are colloidally stable, allowing it to disperse, and presumably consumed eventually by oil-degrading bacteria (Lessard and Demarco 2000, Venkataraman, Tang et al. 2013). Over 2.1 million gallons of dispersant were utilized during the Deepwater Horizon Oil Spill (Allan, Smith et al. 2012). These dispersants are commonly dissolved in organic solvents such as propylene glycol and other light petroleum distillates (Venkataraman, Tang et al. 2013) leading to concerns about the long-term effects of using large volumes of dispersants on the ecosystem. Thus, there is a need to optimize dispersant use, reduce solvent use and to target the dispersant to the oil-water interface (Hemmer, Barron et al. 2011, Athas, Jun et al. 2014, John, Arnosti et al. 2016, Owoseni, Nyankson et al. 2016). Following the Deepwater Horizon incident, there have been several studies aimed at understanding the details of phenomena occurring at the oil-water interface. This research has been aided by the development of high-resolution imaging and advanced chemical spectroscopy.

It is common to assume that dispersant stabilized droplets allow a higher rate of biodegradation due to the significant increase in interfacial area. But one must recognize that the interface is not the same; it is a new interface that is now decorated with surfactants. Surfactants are simply soaps and are inhibitory to bacteria through insertion of the hydrocarbon tails into bacterial membranes. Indeed, recent work indicates that bacterial attachment to an oil-water interface decorated by the synthetic surfactants in Corexit 9500, appears to be significantly inhibited. On the other hand, natural biosurfactants produced by oil degrading bacteria do not have such an inhibitory effect. Figure 5 shows droplets of oil formed through the reduction of interfacial tension by biosurfactants, allow prolific growth of biofilm that sequester oil biodegrading bacteria such as Alkanivorax borkumensis (Omarova, Swientoniewski et al. 2019). The role of biosurfactants as environmentally benign dispersant components and that of biofilm in stabilizing oil droplets is one of continued research interest.

Figure 5:

Cryogenic Scanning Electron Micrographs of oil droplets formed through the generation of biosurfactant and stabilized by biofilm. The inset shows a high resolution cryo SEM of biofilm sequestering oil-degrading bacteria (A. borkumensis).

Figure 5:

Cryogenic Scanning Electron Micrographs of oil droplets formed through the generation of biosurfactant and stabilized by biofilm. The inset shows a high resolution cryo SEM of biofilm sequestering oil-degrading bacteria (A. borkumensis).

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Research has also focused on the development of new materials as alternative dispersants. An environmentally benign approach to dispersion is the use of surface-active particles to stabilize oil-in-water emulsions through the formation of steric barriers to droplet coalescence (Binks and Lumsdon 2000, Owoseni, Nyankson et al. 2014). A recent work performed in Tulane has shown that emulsion stabilization and a reduction of interfacial tension can be implemented using natural, environmentally benign hollow tubular nanoparticles known as halloysites that release a cargo of surfactants encapsulated within the lumen of these nanotubes.(Nyankson, Ober et al. 2014, Owoseni, Nyankson et al. 2014, Nyankson, DeCuir et al. 2015, Nyankson, Olasehinde et al. 2015, Owoseni, Zhang et al. 2015, Owoseni, Nyankson et al. 2016, Owoseni, Zhang et al. 2018, Panchal, Swientoniewski et al. 2018). A disadvantage of this system is the almost instantaneous release of the surfactant from the halloysite nanotubes (HNT – Figure 6) and it is important to develop technologies to control the release. The Tulane concept is to stopper the HNTs to prevent such instantaneous release. When delivered to the oil-water interface, the halloysite adsorb to the oil-water interface and the stoppers gradually break up, releasing surfactant to lowers the interfacial tension. The effort involves design of an environmentally benign two-dimensional metal organic framework (MOF) for coating and stoppering HNTs that contain surfactant. This group also uses stoppers made of hydrocarbon waxes which gradually dissolve in contact with oil, thus releasing the surfactant. Fundamental work involves the understanding of particle stabilized interfaces especially in the presence of oil-degrading bacteria and the role of bacterial biofilm in conjunction with surfactants and particles in sustaining oil droplets in solution (Omarova, Swientoniewski et al. 2018).

Figure 6:

Stoppered Halloysite Nanotubes for controlled delivery of surfactants to the oil-water interface.

Figure 6:

Stoppered Halloysite Nanotubes for controlled delivery of surfactants to the oil-water interface.

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A key challenge to the effective use of existing liquid dispersants in the treatment of oil spills is that the dispersants suffer from spray drift and gets washed off by ocean currents, especially when applied onto heavy or weathered oils (Owoseni, Nyankson et al. 2014, Nedwed, Canevari et al. 2005). To overcome the current limitations of existing liquid dispersants, research efforts have advanced the design of alternative dispersant systems including gel-like dispersants (Owoseni, Nyankson et al. 2014, Pi, Mao et al. 2015, Nedwed, Canevari et al. 2005). Key beneficial characteristics of gel type dispersants over traditional liquid dispersants include: (i) close adherence to the spill oils without being washed off, (ii) buoyancy for extended periods allowing more contact with oil, (ii) high surfactant concentrations, and (d) possibility of the gels providing some degree of visible feedback to oil spill responders (Nedwed, Canevari et al. 2005). The research focuses on involves understanding the self-assembly of environmentally benign surfactants such as phosphatidyl choline (lecithin) and sorbitan monolaurate (Tween) and dioctyl sodium sulfosuccinate (DOSS) into a gel-like mesophase for potential application as a buoyant gel dispersant for oil spill remediation. The gel-like surfactant mesophase is buoyant on water and slowly breaks down releasing surfactant, and significantly improving the encounter rates with oil (Owoseni, Zhang et al. 2018). The microstructure of the gel-like surfactant mesophase is characterized using Small Angle Neutron Scattering (SANS),(Owoseni, Zhang et al. 2018), P Nuclear Magnetic Resonance (NMR), Spectroscopy (Liu, John et al. 2005) and Cryogenic Scanning Electron Microscopy (cryo-SEM) (Tan, Xu et al. 2008, Owoseni, Zhang et al. 2018). These gel-like surfactant mesophases constitute a new class of effective dispersants for the treatment of oil spills (Owoseni, Zhang et al. 2018).

3.2 Microscale interactions between microbes and oil droplets

The research in J. Sheng's group at Texas A&M Corpus Christie (TAMUCC) focuses on uncovering and understanding key micro-scale mechanisms and interactions involving microbes, particles and oil droplets with or without dispersant under both turbulent and laminar flows. Consequently, it elucidates physical and biological small-scale processes crucial to accurately determine the fate of released oil in the environment. To achieve these objectives, this group has developed several techniques and experimental platforms (see Figure 7) (Sheng, Malkiel et al. 2006, Molaei and Sheng 2014, Jalali, White et al. 2018, White, Jalali et al. 2019) enabling it to manipulate the physical, biological, and chemical environment around oil rising droplets, and have applied them to uncover oil-bacteria interactions under flow shear by several kernel microfluidic experiments. Studies of degradation pathways of 5–100 μm droplets have used microfluidic flows over a surface textured with microdroplets (Jalali, White et al. 2018). They have: (i) quantified the degradation rate of oil droplets through dissolution as well as biodegradation by bacteria isolates and natural microbial consortia, (ii) examined biofilm formation processes over microdroplets under mild shear, and (iii) investigated the effects of dispersant on these processes. For Degradation of medium (100–1000 μm) droplets an Ecology-on-a-chip (eChip) microcosm platform (White, Jalali et al. 2019, White, Jalali et al. 2019) has been used for: (i) observing bacterial behavior and microbial community responses around a rising micro-oil droplet at ecologically relevant time and length scales, (ii) quantifying hydrodynamic impacts by bacterial responses on droplet kinematics using high-speed μPIV and μPTV (micro particle tracking velocimetry), (c) investigating rheological mechanisms that would cause such microbial responses for dispersed droplets, and (iv) exploring effects of dispersant on aggregates formation.

Figure 7:

Dissolution and biodegradation of droplets by bacteria using microfluidics textured with array of micro oil droplets: (a–d) Sample of substrates textured by arrays of oil droplets with sizes of (a) 10, (b) 20, & (c) 50 μm, (d) Atomic Force Micrograph of a 50×50μm square sessile drop, (e & f) A microfluidic channel with the textured bottom surface (e) and a motorized Leica inverted microscope (f), (g) oil droplet volume reduction by dissolution and microbial degradation over time; (h) a 50 μm oil droplets with & without COREXIT 9500 after 96h growth experiments using Alcanivorax, Pseudomonas, Marinobacter. Pristine interfaces are observed in cases with dispersant, and (i) 10 and 20μm oil droplets after 96 h using Marinobacter show the size discrimination in bacteria attachment.

Figure 7:

Dissolution and biodegradation of droplets by bacteria using microfluidics textured with array of micro oil droplets: (a–d) Sample of substrates textured by arrays of oil droplets with sizes of (a) 10, (b) 20, & (c) 50 μm, (d) Atomic Force Micrograph of a 50×50μm square sessile drop, (e & f) A microfluidic channel with the textured bottom surface (e) and a motorized Leica inverted microscope (f), (g) oil droplet volume reduction by dissolution and microbial degradation over time; (h) a 50 μm oil droplets with & without COREXIT 9500 after 96h growth experiments using Alcanivorax, Pseudomonas, Marinobacter. Pristine interfaces are observed in cases with dispersant, and (i) 10 and 20μm oil droplets after 96 h using Marinobacter show the size discrimination in bacteria attachment.

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Degradation of 5–100 μm droplets: To assess potential degradation pathways by enhanced dissolution, microbial consumption of dissolved oily components, and direct consumption of undissolved oil, the TAMUCC group has developed a new micro-transfer molding (μTM) technique (Jalali, White et al. 2018) to pattern a substrate with an array of pico-liter crude oil sessile drops to emulate dense droplets cloud rising through the water column. Exploiting the principle of pinning contact line by nano particles, they have printed oil droplets with non-Laplacian shapes (Figure 7a–d), which allows them to print oil droplets with dispersant at various concentrations before it disperses.

Applying this technique in a microfluidic microcosm chamber (Figure 7e &f), they have assessed several degradation hypotheses. In abiotic conditions, an atomic force microscope (AFM) has been used to quantify the dissolution rate of a 50μm droplet (Louisiana sweet, Figure 7g). After 96h, ~20% of the oil dissolves in the aqueous solutions, agreeing with 1D diffusion relationship. Dispersant slightly reduce the dissolution, e.g. 18% with dispersant vs. 22% without. This discrepancy is still within the statistical uncertainty. Biotic experiments using motile Pseudomonas sp. and immotile Alcanivorax borkumensi show that Pseudomonas can only degrade soluble oil components, while Alcanivorax is capable of degrading insoluble oil (additional 25% were lost after dissolution). In addition, biotic experiments with 50μm droplets and Marinobacter hydrocarbonoclasticus, have investigated the effects of dispersant on biodegradation of insoluble oil component (Figure 7h). After 96 h, the interfaces of oil droplets with COREXIT 9500 (DOR 1:20) remain pristine suggesting no permanent biofilms formation, but the bacteria form sporadic small “rafts” over these small oil droplets. Biotic experiments to determine the effect of droplet size (Figure 7i) have shown an anecdotal threshold of five cell body length to allow cellular attachment, suggesting (a controversial but plausible implication) that the biodegradation of insoluble oil ceases when the droplet size decreasesbelow a critical threshold.

Degradation of medium (100–1000 μm) droplets: The ecology-on-a-chip microfluidics technology (Figure 8a &b (White, Jalali et al. 2019, White, Jalali et al. 2019)) developed at TAMUCC simulates the conditions around a medium oil droplet rising through ocean water. It enables detailed observations of microbe-oil interactions at relevant spatial scales of individual bacterium in a dense suspension and temporal scales ranging from milliseconds to weeks or months. This unique setup enabled observations on formation of polymeric microbial aggregates around rising oil droplets and measuring their hydrodynamic impacts.

Figure 8:

eChip experiments. (a–b) eChip microcosm platform. (c) crude oil drop in a flow containing Pseudomonas sp. (P62) at the indicated times after exposure to bacterial suspension, Δt, of (c1) 12 min, (c2) 12.5 min, (c3) 40 min, (c4) 4 h 50 min, (c5) 21 h 33 min, and (c6) 30 h 53 min. They show stages of EPS streamers around an oil drop as (c1) smooth drop, (c2) streamer initiation, (c3) bundling, (c4) proliferation and aggregation, (c5) dispersal, and (c6) reformation. Scale: 100 μm.

Figure 8:

eChip experiments. (a–b) eChip microcosm platform. (c) crude oil drop in a flow containing Pseudomonas sp. (P62) at the indicated times after exposure to bacterial suspension, Δt, of (c1) 12 min, (c2) 12.5 min, (c3) 40 min, (c4) 4 h 50 min, (c5) 21 h 33 min, and (c6) 30 h 53 min. They show stages of EPS streamers around an oil drop as (c1) smooth drop, (c2) streamer initiation, (c3) bundling, (c4) proliferation and aggregation, (c5) dispersal, and (c6) reformation. Scale: 100 μm.

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For a droplet passing through an in-situ cultured Pseudomonas suspension, they have discovered that within minutes bacteria attach onto the droplet and extrude polymeric streamers that rapidly bundle into an elongated aggregate. The long-term microcosm experiments have demonstrated that the formation of extracellular polymeric substance (EPS) streamers and later larger aggregates is a rapid (30 min) and robust process. These aggregations change their morphology in stages such as cell attachment (Figure 8c1), streamer initiation and bundling (Figure 8c2 & c3), proliferation/growth (Figure 8c4), dispersal (Figure 8c5) and reformation (Figure 8c6), resembling the life of a biofilm over a solid substrate. Contrary to a conventional view, the TAMUCC results show that bacteria are capable of forming EPS aggregates directly on a rising oil droplet and grow into a larger aggregate. To resolve the dilemma between the rapid rise velocity and its short exposure time in nearby microbes, they have measured the flow around a drop with streamers to assess the hydrodynamic impact of the latter. They show that even a few isolated streamers increase the drag on a drop is by at least 80%, causing the drop to slow down and subsequently lengthen its residence time, thereby facilitating interactions with the surrounding microbes. These phenomena are likely to affect the transport and biodegradation of oil in the water (Bagby, Reddy et al. 2017). For Pseudomonas, Marinobacter, and Alcarnivorax, the polymeric aggregates present significant differences in morphology, growth rates, and hydrodynamic impacts. To further explore the underlying principles, experiments involving suspensions containing purified EPS from bacterial cultures have shown that EPS aggregates only form in the presence of particles. Most importantly, the composition of EPS may strongly affect the aggregation rate and morphology of the aggregates.

3.3 Oil-Bacterial Interactions

This section summarizes the research performed by K. Stebe's group at the University of Pennsylvania (UPENN). The importance of bacteria in consuming oil spilled in the Gulf of Mexico has motivated a series of studies of bacteria interactions with alkane-aqueous interfaces. This research focuses primarily on the well-characterized species Pseudomonas at hexadecane-aqueous interfaces. The interactions of the bacteria with the interface depends strongly on the particular bacteria strain. The interfacial region is a highly asymmetric environment in terms of its chemistry and mechanics. Bacteria can interact physically with these boundaries, for example, hydrodynamically, by swimming adjacent to the interfaces, and directly by adhering to the fluid interface (Vaccari, Molaei et al. 2017). Upon adhering to the interface, the bacteria can remain motile or they can change into sessile states and restructure the interface. In one example, there are remarkable changes in the interface structure when oil is placed in contact with an aqueous suspension of Pseudomonas sp. ATCC 27259, strain P62. Over times of minutes to hours, the films change their dynamical and mechanical properties from fluid interface covered with motile bacteria, which interact with the colloidal tracers, to generate ballistic and then diffusive ensemble mean square displacements indicative of soft glassy rheology. This transition is attributed to structure formation at the interface of secreted polysaccharides and surfactants. Thereafter, the bacteria motility is suppressed, and the bacteria form a thin solid elastic film with elastic moduli and bending energies of soft solids. The biological role of this elastic film of bacteria at interfaces (FBI) remains to be established (Vaccari, Allan et al. 2015).

The bacterium Pseudomonas aeruginosa PAO1 interacts with the oil-water interface in a similar fashion to form an elastic film comprising PAO1 cells, excreted polysaccharides and proteins. This film formation persists even in PAO1 mutants lacking flagella, pili, or certain polysaccharides. Remarkably, PA14 do not restructure the interface under identical conditions, in spite of the fact that this strain is known to produce robust biofilms. Rather, the PA14 cells remain highly motile with no evidence of structure formation in the interface. Transcriptional profiling revealed highly induced genes include a carbohydrate metabolism enzyme, alkB2 that is upregulated in PA01, but not in PA14 cells. Notably, PAO1 mutants lacking the alkB2 gene remain motile like PA14, failing to restructure the interface. These findings suggest that the ability to form these elastics films could reply on the ability to metabolize the oil phase (Niepa, Vaccari et al. 2017). However, more recent research has revealed that by suspending PA01 in TRIS motility buffer, it too can remain motile for hours at interfaces without forming an elastic film, suggesting more nuanced dependence.

Bacteria also interact with micron scale objects to enhance the dispersion and transport of micron-scale objects via a cargo carrying mechanism (Vaccari, Molaei et al. 2018). This phenomenon has been investigated using the bacterium PA14, selected to study the bacteria as active colloids that do not form elastic films. The bacteria have been again observed near hexadecane-aqueous interfaces in the presence of colloidal tracers placed at the fluid interface. The colloidal tracers have revealed ballistic and diffusive motion expected of enhanced transport in the presence of the motile bacteria. However, inspection of individual trajectories of thousands of particles reveals unexpected prolonged, highly directed motion that are not consistent with a brief interaction that can attributed to hydrodynamics. These complex paths have been revealed to rely on direct adhesion of the colloids to the bacteria, which carries the colloids as cargo.

More recently, the motility of bacteria themselves has been studied in detail for PA01 in Tris buffer at aqueous-oil interfaces. These monotrichous bacteria swim as pushers (away from their flagellum) or pullers (toward their flagellum) depending on the sense of the flagellar rotation. They display a range of behaviors that depend on the orientation and adhesion state at the interface, including purely Brownian motion typical of inert colloids, swimming in circular trajectories influenced by the highly asymmetric hydrodynamic drag environment, to highly directed swimming of cells that are not adhered to the interface (Deng, Molaei et al. 2020).

3.4 Bio Degradation of Oil

This section summarizes several studies involving oil biodegradation performed at SINTEF.

Pristine vs oil exposed waters: In research performed at SINTEF, the “propane jumpstart” discussed by Valentine, Kessler et al. (2010) during the DWH oil spill has been investigated as to whether this is a natural phenomenon or related to previous exposure to oil development. Experimental work using seawater from the Trondheim fjord, which does not have oil development in the vicinity shows that propane oxidation is not faster than methane oxidation in a pristine environment (Brakstad, Almås et al. (2017).

Temperature Effect: Temperature effects on oil biodegradation need to be carefully considered. There is a difference between measuring metabolic processes of a sample of biodegraders from a specific area at two different temperatures vs. comparing the rates of naturally occurring (acclimatized) biodegraders from two different areas. Ribicic, Netzer et al. (2018) compare microbial communities in the Arctic and a temperate Norwegian fjord in low temperature conditions. In the Arctic samples, n-alkane biotransformation is faster in the Arctic than that in the temperate seawaters due to an initially higher abundance of genus Oleispira. Though biodegradation rates are similar, the lag time to initiate biodegradation is longer, possibly due to the lower temperatures seaware or the properties of the oil in low seawater temperatures.

Nutrient Amendment:Williams, Bacosa et al. (2017) show that an increase in nutrient amendment cause the resident microbial groups to be less inhibited (higher in population). This trend indicates that nutrient amendments may a response option that could increase the biodegradation of oil in the water column.

Potential formation of hypoxic or anoxic subsurface areas: During the DWH oil spill, there was concern related to the subsurface dissolved oxygen levels from the peeling layer, which contain dissolved gases, hydrocarbons, and small droplets (Joint Analysis Group 2012). The deep ocean is seasonally ventilated in limited areas in the world oceans. Hence, as oil biodegradation consumes dissolves oxygen, the oxygen anomaly would continue until the water is returned to the surface, which can take decades to centuries. Beegle-Krause, Brakstad et al. (in revision) and Beegle-Krause, Daae et al. (2016) show that in some oil development areas, where the deep ocean is remote from dissolved oxygen renewal, there can be a risk of hypoxic conditions developing in the plume peeling layer. Hence subsurface dispersant injection might lead to increased areas of hypoxic conditions. Brakstad et al. (submitted) have measured the consumption of dissolved oxygen by oil component, and their data could be used to improve risk estimates of development of hypoxic conditions. This consideration is a new tool in Spill Impact Mitigation Analysis (SIMA), which should be added the framework discussed in Robinson, Gardiner et al. (2017).

A series of experiments at JHU involving K. Koehler's, J. Katz's, and R. Sidhaye's groups have investigated the effect of dispersants on the aerosolization of oil slicks by breaking waves (Afshar-Mohajer, Li et al. 2018), and subsequently by bursting of bubbles at an oil-water interface (Sampath, Afshar-Mohajer et al. 2019). The ongoing research assesses the impact of the oil aerosols on human lung epithelial cells in a special exposure chamber developed for this purpose (Chandrala, Afshar-Mohajer et al. 2019).

The wave tank experiments (Afshar-Mohajer, Li et al. 2018) have been performed in the transparent tank depicted in Figure 1. The aerosol size distributions have been measured in sizes ranging from 10 nm to 20 μm. A scanning mobility particle sizer (SMPS) has been used for to 10 to 400 nm size range, and an aerodynamic particle sizer (APS) for the 0.5–20 μm range. Digital holography (Katz and Sheng 2010, Li, Miller et al. 2017) has been used in parallel for airborne droplets larger than 4 μm, showing an agreement with the APS data. In addition, the total particle-bound aromatic hydrocarbons (pPAH) has been measured using a photoelectric aerosol sensor (PAS), and a photoionization detector (PID) has been used for characterizing the total volatile organic compounds (TVOCs). The experiments involve plunging breaking waves entraining slicks of crude oil (Macondo oil), crude oil-dispersant mixtures and dispersant only. The results show that slicks of crude oil do not alter the concentration of nano-droplets. In contrast, the concentrations of nano-particles originating from oil-dispersant mixture are one to orders of magnitude higher than those of crude oil across the entire nano-scale range, reaching 100x for 20 nm particles. Conversely, the dispersant has little effect on the concentration of micro-aerosols. The average concentrations of pPAH vary, but remain similar (150–270 ng/m3). However, the VOC concentrations for crude oildispersant mixtures are 2–3 times lower than those of crude oil, in agreement with prior studies. As a general conclusion of this study, it appears that dispersant cause a drastic increase in the ultrafine particle concentrations, raise questions about effects of inhalation by cleanup workers and downstream communities. In contrast, VOC emissions decreases.

Figure 9:

Size distributions of airborne nano-particles during entrainment of oil slicks with the indicated compositions by breaking waves (Afshar-Mohajer, et al. 2018).

Figure 9:

Size distributions of airborne nano-particles during entrainment of oil slicks with the indicated compositions by breaking waves (Afshar-Mohajer, et al. 2018).

Close modal

In an attempt to identify and characterize the mechanisms affecting the generation of nano-droplets, subsequent studies have focused on generation of airbore droplets by bursting of rising bubbles at the liquid-gas interface (Sampath, Afshar-Mohajer et al. 2019). Bubbles with mode sizes of 86 μm (denoted as small), 178 μm (medium), and 595 μm (large) have been injected into a seawater column covered by slicks of crude oil, pure dispersant, and DOR 1:25 dispersant-oil mixture, and the above-mentioned sensors have been used for measuring the nano- and micro-aerosol size distributions a in clean and ambient air environments. In ambient air, the is an order of magnitude increase in the nano-particle concentration when the large bubbles burst on the slicks containing the DOR-1:25 oil or pure dispersant. Yet, tests performed at different ambient particle concentrations show that the elevated size distributions persistently maintain the same shape (but not the magnitude) as that of the ambient air suggesting that interactions of the airbone particles with the interface play a significant role (some are postulated in the paper). In contrast, smaller bubbles and tests not involving dispersants do not cause an increase in the nano-particle concentrations. The nano-droplets are also generated by large bubbles in particle-free air, but their concentrations are much lower. All plumes generate micron-sized aerosols, but trends vary. In conclusion, bubble bursting appears to be a primary contributor to generation of oil-nano-aerosols.

As for micro particles, In general, for the same contaminant, the micro-droplet concentration decreases with increasing oil slick thickness. Particularly striking is a reduction of two orders of magnitude in the micro-droplet concentration when medium and small bubbles burst on 500 μm crude oil slicks. Chemical analysis of air and particulates collected from filters sampling the particles confirm the presence of airborne oil above the slicks.

Another mechanism that causes generation of airborne droplets involves impact of raindrops on oil slicks (Murphy, Li et al. 2015). This study examines the associated generation of micro-aerosols for varying the oil layer thickness and dispersant concentration using high speed imaging and holography. Results show that the presence of oil slicks alters the initial spray, and subsequent the formation of a bubble, which breaks up to form a crown with ligaments. For oil layers thinner than 100 μm, the raindrop ruptures the oil layer immediately upon impact, ad appears similar to the seawater only case. Conversely, two distinct crowns form for 200 – 1100 μm oil slicks. The oil-comprised upper crown disintegrates rapidly, leaving a shorter lower crown containing water coated with oil, which rarely forms a bubble. Dispersants increase the height of the lower crown and restores the bubble canopy formation. Thicker oil layers produce a single crown composed only of oil, which also forms a canopy. Breakup of ligaments by capillary instability within the first millisecond produces a bimodal droplet size distribution with primary and secondary peaks at 50 and 225 μm, whose numbers increase with layer thickness and by addition of dispersants. The small droplets are ejected primarily at a shallow elevation angle during the initial splash, and the larger ones by breakup of ligaments. The study also classifies the splashing behavior based on the oil Froude and Reynolds numbers.

In addition to health concerns related to airborne particles, Almeda, Cosgrove et al. (2018) show that oil spills and chemical dispersants can alter the upper water column ecology in the area of a spill, leading to increases in harmful algal blooms (HABs) and their associated toxins. The naturally occurring grazers, such as tintinnids and oligrich ciliates are negatively affected by the oil spill, while the bloom-forming dinoflagellates increase. Hence, in addition to aerosolized dissolved oil components, changes to the HAB toxins could also potentially modify the Personal Protective Equipment (PPE) requirements for responders both in vessels and on the beach.

A series of studies have focused on modeling of oil-water interactions. This section describes the research performed by A. Prosperetti's group at the University of Houston (UH), which focuses on theoretical analysis of interfacial instabilities and dissolution of oil droplets.

5.1 Stability

It is often observed that small drops or bubbles detach from the interface separating two co-flowing immiscible fluids. The size of these drops or bubbles can be orders of magnitude smaller than the length scales of the parent fluid mass. The mechanism responsible for this phenomenon is not immediately clear as there is no obvious source of energy at these scales. In this study it is found that, underlying the phenomenon, is a new generic interfacial instability that occurs near zero-vorticity points (Tseng and Prosperetti 2015). It is shown that, in both two and three dimensions, a generic feature of such points is a local convergence or divergence of streamlines. In the former case, a small perturbation on the interface is forced to grow both by the kinematic compression due to the converging velocity field, and by the dynamic effects of the viscous shear stress exerted by the fluid into which it protrudes together with the surrounding pressure field. The force opposing the growth of the instability is surface tension the action of which, in the presence of surfactants, is strongly inhibited by the fact that the very flow which causes the instability to grow also accumulates the surfactants in its vicinity. A cartoonish explanation of the mechanism of the instability is provided in Figure 10. The results of the Navier-Stokes simulation of a two-dimensional drop rising in an immiscible liquid are shown in Figure 11 as successive snapshots of the interface. The green lines are iso-vorticity lines corresponding to zero vorticity and the red circle is the zero-vorticity point on the interface. The figure shows the “pulling” of the drop rim into an elongated structure that will eventually detach to form small drops.

Figure 10:

Qualitative explanation of the surface instability near a zero vorticity point O, where the streamlines can be convergent or divergent. In the former case, illustrated here, a local perturbation can grow due to kinematic compression and viscous stresses. If surfactants are present, they are carried by the flow toward the zero-vorticity point and weaken the action of surface tension.

Figure 10:

Qualitative explanation of the surface instability near a zero vorticity point O, where the streamlines can be convergent or divergent. In the former case, illustrated here, a local perturbation can grow due to kinematic compression and viscous stresses. If surfactants are present, they are carried by the flow toward the zero-vorticity point and weaken the action of surface tension.

Close modal
Figure 11:

Successive snapshots from the Navier-Stokes simulation of a two-dimensional drop rising in an immiscible liquid. The green lines are iso-vorticity lines corresponding to zero vorticity and the red circle is the zero-vorticity point on the interface. The rim of the drop is “pulled” into an elongated structure that eventually detaches to form small drops (Tseng and Prosperetti 2015)

Figure 11:

Successive snapshots from the Navier-Stokes simulation of a two-dimensional drop rising in an immiscible liquid. The green lines are iso-vorticity lines corresponding to zero vorticity and the red circle is the zero-vorticity point on the interface. The rim of the drop is “pulled” into an elongated structure that eventually detaches to form small drops (Tseng and Prosperetti 2015)

Close modal

5.2 Dissolution

In order to model the dissolution of an oil drop in water it is necessary to know the concentration of the solute at the drop surface. In the case of a single-component drop, this is just the solubility of the drop material. This simple prescription however fails in the case of a multi-component oil drop as the surface concentration of the solutes depends on the drop composition, which varies as the drop dissolves due to the different solubilities of the individual components. This issue had not been adequately addressed in the literature before our work. The UH team has carried out a first-principles demonstration of the necessary strategy based on the fundamental law of equality of each component's chemical potentials in the drop and in the external solvent (Chu and Prosperetti 2016). In pursing this route, one encounters the difficulty that the chemical potential is mostly unknown and can only be approximated by means of semi-empirical relations. For this purpose, the have used the UNIQUAC model, which is one of the most widespread methods to approximate chemical potentials in chemical physics. An example of the difference between the results and those obtained by of a standard models in the literature (Su and Needham 2013) is given in Figure 12.

Figure 12:

Normalized drop radius R(t)/R0 vs. time according to the present theory (solid line) and one of the standard ad hoc theories in the literature (Su and Needham 2013). The drop consists of ethyl acetate (a) and butyl acetate (b) with an initial mole ratio na(0)/nb(0)= 9 in water; the initial radius is 43 μm (Chu and Prosperetti, 2016).

Figure 12:

Normalized drop radius R(t)/R0 vs. time according to the present theory (solid line) and one of the standard ad hoc theories in the literature (Su and Needham 2013). The drop consists of ethyl acetate (a) and butyl acetate (b) with an initial mole ratio na(0)/nb(0)= 9 in water; the initial radius is 43 μm (Chu and Prosperetti, 2016).

Close modal

This section focuses on computational modeling of multiphase flows, using subsurface oil plumes and aerosol generated at the ocean surface as the target problem. The research has been performed by M. Chamecki's group at the University of California Los Angeles (UCLA), and by D. Yang's group at the University of Houston (UH). Understanding and accurately modeling the multiphase hydrocarbon plume dispersion in the oceanic environment is crucial for rapid response and remediation of the oil spill disaster after e.g. an oil well wellhead blowouts. The plume dispersion is a highly dynamic and turbulent process, with rich multiscale flow physics ranging from scale of individual oil droplets (a few micrometers) to scales of the entire plume (hundreds of meters). This range of physical scales is further increased when the plume reaches the upper ocean boundary layer, where its transport and fate are determined by both the fine-scale three-dimensional turbulence (tens of meters) and the quasi-two-dimensional submesoscale and mesoscale eddies (hundreds of meters to tens of kilometers). Near the wellhead, the small-scale physics associated with individual buoyant particles (e.g. oil droplets and gas bubbles) determines the particle size distribution, which strongly affects the plume dynamics through the complex variation of buoyancy force induced by buoyant particles (mostly the gas bubbles) and determines how the oil droplets get transported through the ocean column. Large-eddy simulation (LES) is the ideal approach to handle the numerical modeling of such complex multiscale turbulent phenomena.

Accurate large-eddy simulations of oil plumes require parameterization of the effect of many small-scale physical processes such as oil droplet and gas bubble formation, coalescence, and breakup, gas dissolution, biodegradation, hydrate formation, etc. During the GoMRI era, several LES studies of oil and gas plumes extending from near-source plume dynamics to large scale transport on the ocean surface have been performed. In these studies, LES has served as a computational platform to investigate the effect of particle-scale physics on the plume-scale dispersion phenomena. These studies also show a continuous improvement of the numerical models by incorporation of additional small-scale physical process, leading to increasingly more realistic simulations of plume dynamics and oil transport.

For example, (Chen, Yang et al. 2018) combine the LES model of nearfield plume (Yang, Chen et al. 2016) with the extended nonperiodic domain LES for scalar transport (ENDLESS) (Chen, Yang et al. 2016) and study the effect of dispersant application on the transport of surface oil plumes by three-dimensional Langmuir turbulence and synthetic two-dimensional submesoscale eddies. They have found that the significant reduction of oil droplet size caused by the dispersant can enhance the vertical mixing of the oil in the ocean mixed layer, resulting in a shift of the mean plume transport direction. This phenomenon is caused by the ocean Ekman spiral, the continuous change of mean horizontal flow direction and speed with depth due to the effect of Earth's rotation (Figure 13).

Figure 13.

Time evolution of vertically integrated oil concentration that is located initially inside the rectangular patch marked by the dashed lines (a)–(f). The top row (a)–(c) is for the case without dispersant application, and the bottom row (d)–(f) is with dispersant application. The three columns show results 6, 12, and 18 h after application of surface dispersant. The black dot shows the center of mass of the oil patch (Chen et al. 2018).

Figure 13.

Time evolution of vertically integrated oil concentration that is located initially inside the rectangular patch marked by the dashed lines (a)–(f). The top row (a)–(c) is for the case without dispersant application, and the bottom row (d)–(f) is with dispersant application. The three columns show results 6, 12, and 18 h after application of surface dispersant. The black dot shows the center of mass of the oil patch (Chen et al. 2018).

Close modal

Note that in (Chen, Yang et al. 2018) the physical processes involved in the interaction between the dispersant and the oil droplets (mixing between the two phases, reduction of surface tension on the oil, slick and droplet breakup, etc.) are not explicitly represented, and the reduction of oil droplet size is prescribed for the sake of model simplification. Recently, (Aiyer, Yang et al. 2019) have implemented a population balance model in the Eulerian-Eulerian LES modeling framework (Chamecki, Chor et al. 2019) to simulate the effect of turbulent eddies on oil droplet breakup (Figure 14), and have obtained droplet size distributions in agreement with the experimental data of (Murphy, Xue et al. 2016). This LES with a population balance model makes it possible to directly incorporate the effects of dispersant by prescribing a reduction in surface tension. In such a model, LES would be used to simulate the transport and mixing of oil and dispersant droplets and account for the effect of dispersant-induced surface tension reduction on the breakup process of oil droplets. In addition, recently Peng et al. (in preparation) have developed a new modeling strategy within the Eulerian-Eulerian LES framework to efficiently simulate the effect of gas bubble dissolution on the dynamics and material transport of multiphase hydrocarbon plume originated from deep-water wellhead blowout. For deep-water plumes, LES modeling shows considerable reduction of plume buoyancy as a result of the continuous gas dissolution from the bubbles to the surrounding ocean. Gas dissolution strongly impacts the intensity of the upward plume motion as well as the peeling/intrusion processes that cause small oil droplets to escape from the rising plume and become trapped in deep ocean intrusion layers. The capability of properly accounting for the gas dissolution effect is crucial for accurately predicting the plume characteristics and material transport rate, and the new LES model by provides an accurate and cost-efficient option to achieve such a goal.

Figure 14:

Contour plots of instantaneous droplet concentration fields at the midplane of a jet in a cross flow. The concentration is plotted in logarithmic scale for droplet sizes of: (a) e 1000 μm, (b) 432 μm, (c) 107 μm and (d) 20 μm (Aiyer et al. (2019).

Figure 14:

Contour plots of instantaneous droplet concentration fields at the midplane of a jet in a cross flow. The concentration is plotted in logarithmic scale for droplet sizes of: (a) e 1000 μm, (b) 432 μm, (c) 107 μm and (d) 20 μm (Aiyer et al. (2019).

Close modal

At the ocean surface, various physical processes, such as wave breaking and bubble bursting, cause the aerosolization of crude oil droplets. The application of dispersant as well as the variation of the environmental conditions, e.g. wind speed, sea state, wave breaking intensity, etc., can result in the release of oil droplet aerosol of a wide range of sizes into the marine atmospheric boundary layer (MABL), which can impose a health threat to coastal communities. (Li, Zhao et al. 2019) apply a wave-effect-resolving LES technique to study the transport of oil droplet aerosols by wind over progressive water waves in the MABL. Their LES results show considerable enhancement of oil droplet suspension and vertical/lateral spreading by wind with strong wave-induced disturbance when ocean swell waves are present. The spatial distribution of the aerosol concentration also exhibits considerable streamwise variations that correlates with the phase of the long progressive waves (Figure 15). Considering that swell waves appear quite frequently in coastal region after a storm has occurred remotely, and their propagation direction turns towards the shoreline during the shoaling process, their interactions with the onshore wind may enhance the oil droplet aerosol transport towards coastal urban regions.

Figure 15:

Dispersion of oil droplet aerosol in marine atmospheric boundary layer. Top: an illustration of three-dimensional instantaneous flow and aerosol fields for wind over swell waves. Bottom: center-of-mass height of oily aerosols as a function of the streamwise location. Solid lines - swell wave cases; dashed lines – flat surface benchmark cases. Li et al. (2019).

Figure 15:

Dispersion of oil droplet aerosol in marine atmospheric boundary layer. Top: an illustration of three-dimensional instantaneous flow and aerosol fields for wind over swell waves. Bottom: center-of-mass height of oily aerosols as a function of the streamwise location. Solid lines - swell wave cases; dashed lines – flat surface benchmark cases. Li et al. (2019).

Close modal

Despite the need for additional parameterizations of small-scale physical processes in LES, the recent advances during the GoMRI era have provided a leap in the ability of LES to represent the dynamics of oil and gas plumes in the ocean. There is no doubt that the future LES model development will benefit from the much-improved understanding on the small-scale physics related to oceanic oil spill science from the GoMRI-funded research. In return, the improved LES model can also serve better as a computational platform for the application and assessment of new parameterizations of small-scale physical processes for more realistic modeling of offshore oil spills.

This section describes the effort of M. Boufadel's group at New Jersey Institute of Technology (NJIT) to develop an applied model for; predict the spatio-temporal evolution of droplets. In his seminal work, (Hinze 1955) finds that the critical Weber number for the breakup of droplets is around 1.1, hence the largest diameter that can survive in turbulence (approximated by the d95) can be estimated at a critical Weber number, Wec = ρcu′2d95/σ = 1.17. The velocity is estimated by assuming isotropic turbulence at the scale of the droplet, u′2 ∼ 2.0ε2/3d2/3. To account for viscous resistance to breakup, (Hinze 1955) also introduces a viscosity group, Vi,G = μd / (ρdσ.d)0.5. Using dimensional analysis, he argues that a general Weber number, Weg, sould be related to Wec and Vi,G through Weg = Wec (1+ϕVi,G), where ϕ → 0 as Vi,G → 0. These concepts have been expanded on in the 80s (Calabrese, Chang et al. 1986, Calabrese, Chang et al. 1986, Wang and Calabrese 1986) for dealing with high viscosity oils. They show that the contribution of the interfacial tension affects the droplet size distributions even for oil viscosity of 10,000 cp.

Johansen et al. (Johansen, Brandvik et al. 2013) extended these concepts to oil droplets in jets. They have developed a semi-empirical model based corrected Weber number We,v = We [1+BVi (d50 / D)1/3]−1, where We=ρU2D/σ, Vi = We / Re = μdU/σ, Re = ρUD / μ, B is a constant ρ is density, μ is viscosity and U an D are the jet speed and diameter. Their model is
formula

In the presence of gas, U is replaced by an “effective” velocity computed by assuming constriction due to gas. By fitting to droplet data in vertical jets from millimeter size orifices without and with dispersant (Brandvik, Johansen et al. 2013), they find that A=15 and B=0.8, which has been replaced by A=24 and B=0.08 in subsequent studies (Brandvik, Johansen et al. 2014). (Johansen, Brandvik et al. 2013) assume that the full DSD can be predicted by assuming a lognormal distribution for cases without dispersant and by Rossin-Romler (or Weibul) distribution for cases with dispersant. When dispersants are used, resulting in a very large Vi, the bracket term becomes large. In these situations, neglecting the viscosity of the oil would underestimate d50.

Li et al. (Li, Spaulding et al. 2017) argue that Eqn. 1 could overestimate the size of droplets from large orifices as the droplets could become larger than the maximum stable droplet, dmax, based on the Rayleigh stability criterion (Grace, Wairegi et al. 1978),
formula
They proposed the following correlation:
formula
Where do is the minimum of the orifice diameter and dmax, Oh = μ(ρdσd0)1/2, We,c = ρcU2D/σ, and ρc is the water density. For situations with density close to that of water and orifices smaller than dmax, the two equations give essentially the same results. Li et al. (Li, Spaulding et al. 2017) also assume the a lognormal size distribution, and requires that the oil properties used in Eqn. 3 are those of dead oil, i.e. there does not seem to be a means to account for the density of live oil at different depths. Both semi-empirical correlation models are expedient for obtaining the d50. For cases where d50 is close or larger than dmax, hence the size distribution would have to be truncated, hence (Dissanayake, Crowley et al. 2018) use a distribution truncated at dmax.

The correlations of (Johansen, Brandvik et al. 2013) and (Li, Spaulding et al. 2017) allows one to account for dispersant through the reduction of the interfacial tension. However, numerous experiments have shown that the application of dispersants results in formation of micron-size droplets, and even a bimodal distribution with the large mode at values ranging between 10 to 30 μm, e.g. (Li, Lee et al. 2008, Li, Lee et al. 2008) for waves, and (Murphy, Xue et al. 2016, Zhao, Gao et al. 2017) for oil jets released from millimeter size orifices. The micron-size droplets result from a phenomenon known as tip-streaming (Gopalan and Katz 2010, Tseng and Prosperetti 2015, Li, Miller et al. 2017) which is mentioned earlier, whereby the dispersant accumulates at certain regions on the droplets and resulting in generation of long threads from regions of nearly zero interfacial tension (De Bruijn 1993). Consequently, in turbulent flow multiple threads form within milliseconds, resulting in the formation of a droplet cloud.

The VDROPJ model introduced by NJIT is a population model for predicting the oil droplet size distribution from multiphase jets (Zhao, Boufadel et al. 2014). It combines the droplet population model VDROP (Zhao, Torlapati et al. 2014) that applies to a control volume with correlations for jet/plume properties in the near field. VDROP accounts for simultaneous breakup and formation of oil droplets following approaches developed for droplet population in reactor modeling. Breakage is assumed due to the dynamic pressure resulting from isotropic turbulence and is accounted for using a breakage frequency, g(di), given by , where Sed is the cross section area between a droplet of size di and an eddy of size de; and ue and ud are the speeds of an eddy and a droplet, respectively. BE is the breakage efficiency, given by BE(di,de,t) = exp[−[(Ec + Ev) / e] / c1], where Ec and Ev are the resistance energies to breakup due to surface tension and droplet viscosity, respectively; e is the energy of an eddy, and “c1” is an empirical constant of order 1.0. The term Kb is an empirical constant discussed below.

The coalescence of droplets is captured using a coalescence frequency, h(di,dj) (Narsimhan, Gupta et al. 1979, Tsouris and Tavlarides 1994, Luo and Svendsen 1996), where di and dj are arbitrary diameters. The breakup of droplets and their coalescence is assumed driven by the energy dissipation rate for isotropic turbulence and differential settling of droplets. Similar to previous models, VDROP assumes that only eddies of comparable and smaller size than the droplet break it, while eddies larger would advect it (Prince and Blanch 1990, Tsouris and Tavlarides 1994). This is obviously a mathematical convenience as eddies a few folds larger than the droplets are still expected to break it. While the vast majority of population models assume that the resistance to breakup is caused by interfacial tension, VDROP includes viscous resistance formulation based on the works of (Calabrese, Chang et al. 1986, Baldyga and Podgorska 1998). Baldygha and Podgorska (1998) account for the droplet viscosity through an elongation time pre-breakup, assuming that break up occurs when their length is equal to double their initial diameter. Their model also considers that only eddies of equal size to the droplet would break it, but they integrate the dynamic pressure resulting from the whole multifractal spectrum (Anselmet, Gagne et al. 1984, Meneveau and Sreenivasan 1987).

The VDROPJ model relies on moving a control volume downstream along the jet centerline while allowing for changes in the jet hydrodynamics to impact the size distribution. The correlations are obtained based on the hydrodynamics of miscible jets, and include the change along the centerline of the jet of the jet velocity (Figure 16), flow rate, and energy dissipation rate. The oil mass is then moved downstream by advection at the centerline velocity, where the VDROP is solved using the upstream size distribution and the local conditions.

Figure 16:

A conceptual framework for the model VDROPJ. At each point, Pi (i=0, 1, 2, ..) the volume V, the concentration of droplets n, fluid velocity, u, holdup (volume of oil divided by total volume of fluids), ϕ, and energy dissipation rate, ε, are obtained based on correlations for miscible fluids, and used to predict the DSD for the next point.

Figure 16:

A conceptual framework for the model VDROPJ. At each point, Pi (i=0, 1, 2, ..) the volume V, the concentration of droplets n, fluid velocity, u, holdup (volume of oil divided by total volume of fluids), ϕ, and energy dissipation rate, ε, are obtained based on correlations for miscible fluids, and used to predict the DSD for the next point.

Close modal

The standard approach for capturing the effect of dispersant has been to reduce the oil water interfacial tension. However, Zhao et al. (Zhao, Gao et al. 2017) show that this approach cannot reproducing the bimodal distribution 1000 diameters from the orifice. They conclude that tip-streaming (Gopalan and Katz 2010) occur must be accounted for. Hence, they introduce modules within VDROPJ to simulate tip streaming. The release of oil mass from droplets of diameter di, is simulated as a function of release mass flow rate of oil, Jo, from the droplet, i.e. dMd,i / dt = −ktipJ0, where ktip is a mass transfer coefficient dependent on the hydrodynamics around the droplet and the interfacial tension in the jet. Although small droplets occupy only a small percentage of the total volume, they might be of major environmental interest because they tend to get entrained in the intrusion layer (Wang and Adams 2016, Gros, Socolofsky et al. 2017), whereas larger droplets (e.g., >500 μm) would rise directly to the surface (Zhao, Boufadel* et al. 2015).

It is very difficult to summarize the insight derived from the small scale studies described in this paper owing to their breadth both in terms of new understanding of the complex processes involved, and new sophisticated tools for modeling and measuring them. Because of the paper size, we have not accounted for several other complex effects, such as formation of oil-particle aggregates (Zhao, Boufadel et al. 2017). However, the summary above indicates that we have much better understanding how dispersants affect the breakup of oil slicks and jets, the resulting droplet size distributions, and the models that could be used for predicting them. We also have also shown that dispersants affect the aerosolization of oil, most notably the generation of airbone nano-droplets, raising health questions which have not been addressed adequately yet. Furthermore, although there are still many open questions, we also have a much clearer idea on how oil interacts with bacteria, and the resulting impact of these interactions on the droplet dynamics (i.e. altered drag by ESP), and biodegradation of oil.

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