Crude oil jets in crossflow: Effects of dispersant concentration on plume behavior

Murphy, David; Xue, Xinzhi; Sampath, Kaushik; Katz, Joseph

doi:10.1002/2015jc011574

Cited by 34 publications

(44 citation statements)

References 70 publications

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“…For example, samples of water collected after the DOR 1:25 oil experiments and examined under a microscope clearly show numerous ∼1 µm and smaller droplets that has not been quantified. In fact, a cloud of such droplets has remained suspended in the water indefinitely, consistent with phenomena observed in submerged plumes (Murphy et al, ). In agreement, Li et al () show a bimodel distribution for oil‐dispersant cases, with a sharp increase in the number of 1 µm droplets.…”

Section: Discussionsupporting

confidence: 83%

Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

et al. 2017

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This laboratory experimental study investigates the temporal evolution of the size distribution of subsurface oil droplets generated as breaking waves entrain oil slicks. The measurements are performed for varying wave energy, as well as large variations in oil viscosity and oil‐water interfacial tension, the latter achieved by premixing the oil with dispersant. In situ measurements using digital inline holography at two magnifications are applied for measuring the droplet sizes and Particle Image Velocimetry (PIV) for determining the temporal evolution of turbulence after wave breaking. All early (2–10 s) size distributions have two distinct size ranges with different slopes. For low dispersant to oil ratios (DOR), the transition between them could be predicted based on a turbulent Weber (We) number in the 2–4 range, suggesting that turbulence plays an important role. For smaller droplets, all the number size distributions have power of about −2.1, and for larger droplets, the power decreases well below −3. The measured steepening of the size distribution over time is predicted by a simple model involving buoyant rise and turbulence dispersion. Conversely, for DOR 1:100 and 1:25 oils, the diameter of slope transition decreases from ∼1 mm to 46 and 14 µm, respectively, much faster than the We‐based prediction, and the size distribution steepens with increasing DOR. Furthermore, the concentration of micron‐sized droplets of DOR 1:25 oil increases for the first 10 min after entrainment. These phenomena are presumably caused by the observed formation and breakup oil microthreads associated with tip streaming.

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Section: Discussionsupporting

confidence: 83%

Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

et al. 2017

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“…When dispersant is applied, tip-streaming occurs, whereby oil sloughs off the oil droplets in strings or "streams" that are microns in thickness, and eventually break into micron-sized droplets (Gopalan and Katz [128]). This results in a bimodal oil DSD, as noted in the experiments of Murphy et al [129] and Zhao et al [126,127]; the latter amended the VDROPJ model with a module to capture this mechanism.…”

Section: Droplet Sizementioning

confidence: 90%

Progress in Operational Modeling in Support of Oil Spill Response

Barker

Kourafalou

Beegle-Krause

et al. 2020

JMSE

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Following the 2010 Deepwater Horizon accident of a massive blow-out in the Gulf of Mexico, scientists from government, industry, and academia collaborated to advance oil spill modeling and share best practices in model algorithms, parameterizations, and application protocols. This synergy was greatly enhanced by research funded under the Gulf of Mexico Research Initiative (GoMRI), a 10-year enterprise that allowed unprecedented collection of observations and data products, novel experiments, and international collaborations that focused on the Gulf of Mexico, but resulted in the generation of scientific findings and tools of broader value. Operational oil spill modeling greatly benefited from research during the GoMRI decade. This paper provides a comprehensive synthesis of the related scientific advances, remaining challenges, and future outlook. Two main modeling components are discussed: Ocean circulation and oil spill models, to provide details on all attributes that contribute to the success and limitations of the integrated oil spill forecasts. These forecasts are discussed in tandem with uncertainty factors and methods to mitigate them. The paper focuses on operational aspects of oil spill modeling and forecasting, including examples of international operational center practices, observational needs, communication protocols, and promising new methodologies.

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“…As a final laboratory-scale validation case, we consider data for an oily jet reported in Murphy et al [66]. In their experiments, the release orifice was cosine-shaped, leading to a contraction of the flow shortly after emission from the orifice.…”

Section: Multiphase Plume Validation: Laboratory Experimentsmentioning

confidence: 99%

“…This assumption is valid as long as the jet-to-plume transition length scale l M of the dispersed phase is small. In the oily jet experiments of Murphy et al [66], l M given by the oil momentum and buoyancy flux is not negligible, and we modified the BPM to include the momentum of the discharged oil. This was not required in the Zhang and Zhu [65] experiments because the momentum in their bubbly jets resulted from the water in the release, which is always included in the TAMOC plume models.…”

Section: Multiphase Plume Validation: Laboratory Experimentsmentioning

confidence: 99%

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

2018

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We present the development and validation of a numerical modeling suite for bubble and droplet dynamics of multiphase plumes in the environment. This modeling suite includes real-fluid equations of state, Lagrangian particle tracking, and two different integral plume models: an Eulerian model for a double-plume integral model in quiescent stratification and a Lagrangian integral model for multiphase plumes in stratified crossflows. Here, we report a particle tracking algorithm for dispersed-phase particles within the Lagrangian integral plume model and a comprehensive validation of the Lagrangian plume model for single-and multiphase buoyant jets. The model utilizes literature values for all entrainment and spreading coefficients and has one remaining calibration parameter j, which reduces the buoyant force of dispersed phase particles as they approach the edge of a Lagrangian plume element, eventually separating from the plume as it bends over in a crossflow. We report the calibrated form j ¼ ½ðb À rÞ=b 4 , where b is the plume halfwidth, and r is the distance of a particle from the plume centerline. We apply the validated modeling suite to simulate two test cases of a subsea oil well blowout in a stratificationdominated crossflow. These tests confirm that errors from overlapping plume elements in the Lagrangian integral model during intrusion formation for a weak crossflow are negligible for predicting intrusion depth and the fate of oil droplets in the plume. The Lagrangian integral model has the added advantages of being able to account for entrainment from an arbitrary crossflow, predict the intrusion of small gas bubbles and oil droplets when appropriate, and track the pathways of individual bubbles and droplets after they separate from the main plume or intrusion layer.

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Crude oil jets in crossflow: Effects of dispersant concentration on plume behavior

Cited by 34 publications

References 70 publications

Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

Progress in Operational Modeling in Support of Oil Spill Response

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

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