Calculation of oil droplet size distribution in ocean oil spills: A review

Nissanka, Indrajith D.; Yapa, Poojitha D.

doi:10.1016/j.marpolbul.2018.07.048

Cited by 45 publications

(28 citation statements)

References 72 publications

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“…2018). A review of recent developments in oil spill modelling can be found in Nissanka & Yapa (2018). In combustion of fuel sprays the droplet size distribution plays an important role, determining atomization quality and spray combustion performance (Bossard & Peck 1996).…”

Section: Introductionmentioning

confidence: 99%

“…The size of oil droplets affects their rise velocity and can influence the entire plume's transport characteristics in the ocean (Chen et al 2018). A review of recent developments in oil spill modelling can be found in Nissanka & Yapa (2018). In combustion of fuel sprays the droplet size distribution plays an important role, determining atomization quality and spray combustion performance (Bossard & Peck 1996).…”

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confidence: 99%

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A population balance model for large eddy simulation of polydisperse droplet evolution

et al. 2019

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In the context of many applications of turbulent multi-phase flows, knowledge of the dispersed phase size distribution and its evolution is critical to predicting important macroscopic features. We develop a large eddy simulation (LES) model that can predict the turbulent transport and evolution of size distributions, for a specific subset of applications in which the dispersed phase can be assumed to consist of spherical droplets, and occurring at low volume fraction. We use a population dynamics model for polydisperse droplet distributions specifically adapted to a LES framework including a model for droplet breakup due to turbulence, neglecting coalescence consistent with the assumed small dispersed phase volume fractions. We model the number density fields using an Eulerian approach for each bin of the discretized droplet size distribution. Following earlier methods used in the Reynolds averaged Navier-Stokes framework, the droplet breakup due to turbulent fluctuations is modelled by treating droplet-eddy collisions as in kinetic theory of gases. Existing models assume the scale of droplet-eddy collision to be in the inertial range of turbulence. In order to also model smaller droplets comparable to or smaller than the Kolmogorov scale we extend the breakup kernels using a structure function model that smoothly transitions from the inertial to the viscous range. The model includes a dimensionless coefficient that is fitted by comparing predictions in a one-dimensional version of the model with a laboratory experiment of oil droplet breakup below breaking waves. After initial comparisons of the one-dimensional model to measurements of oil droplets in an axisymmetric jet, it is then applied in a threedimensional LES of a jet in crossflow with large oil droplets of a single size being released at the source of the jet. We model the concentration fields using N d = 15 bins of discrete droplet sizes and solve scalar transport equations for each bin. The resulting droplet size distributions are compared with published experimental data, and good agreement for the relative size distribution is obtained. The LES results also enable us to quantify size distribution variability. We find that the probability distribution functions of key quantities such as the total surface area and the Sauter mean diameter of oil droplets are highly variable, some displaying strong non-Gaussian intermittent behavior.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A population balance model for large eddy simulation of polydisperse droplet evolution

et al. 2019

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“…Both models need to be calibrated with field data or experimental data [60]. For a more detailed discussion on these models, the reader is referred to [21].…”

Section: Sizes Of Oil Droplet In the Intrusion Layermentioning

confidence: 99%

Formation, Detection, and Modeling of Submerged Oil: A Review

Beegle-Krause

Englehardt

2020

JMSE

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Submerged oil, oil in the water column (neither at the surface nor on the bottom), was found in the form of oil droplet layers in the mid depths between 900–1300 m in the Gulf of Mexico during and following the Deepwater Horizon oil spill. The subsurface peeling layers of submerged oil droplets were released from the well blowout plume and moved along constant density layers (also known as isopycnals) in the ocean. The submerged oil layers were a challenge to locate during the oil spill response. To better understand and find submerged oil layers, we review the mechanisms of submerged oil formation, along with detection methods and modeling techniques. The principle formation mechanisms under stratified and cross-current conditions and the concepts for determining the depths of the submerged oil layers are reviewed. Real-time in situ detection methods and various sensors were used to reveal submerged oil characteristics, e.g., colored dissolved organic matter and dissolved oxygen levels. Models are used to locate and to predict the trajectories and concentrations of submerged oil. These include deterministic models based on hydrodynamical theory, and probabilistic models exploiting statistical theory. The theoretical foundations, model inputs and the applicability of these models during the Deepwater Horizon oil spill are reviewed, including the pros and cons of these two types of models. Deterministic models provide a comprehensive prediction on the concentrations of the submerged oil and may be calibrated using the field data. Probabilistic models utilize the field observations but only provide the relative concentrations of the submerged oil and potential future locations. We find that the combination of a probabilistic integration of real-time detection with trajectory model output appears to be a promising approach to support emergency response efforts in locating and tracking submerged oil in the field.

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“…Despite the widely researched spilled oil including in situ, ship-borne, airplane and space-borne techniques [ 20 , 21 ], the fates of dispersed oil in seawater are much less known and are not monitored on a regular basis. Recently dispersed oil gained attention in marine research; however, most studies focus on its biological and ecological impact [ 22 , 23 ], chemical and microbiological consequences [ 24 , 25 , 26 ] or on oil spill modeling [ 27 , 28 , 29 ]. Few studies focus on optical properties of dispersed oil demonstrating the significance and potential utility of optical sensing of oil droplets using both active and passive remote techniques [ 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Dispersed Oil Pollution in the Ocean—The Role of Chlorophyll Concentration

Haule

Freda

2021

Sensors

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In the contrary to surface oil slicks, dispersed oil pollution is not yet detected or monitored on regular basis. The possible range of changes of the local optical properties of seawater caused by the occurrence of dispersed oil, as well as the dependencies of changes on various physical and environmental factors, can be estimated using simulation techniques. Two models were combined to examine the influence of oceanic water type on the visibility of dispersed oil: the Monte Carlo radiative transfer model and the Lorenz–Mie model for spherical oil droplets suspended in seawater. Remote sensing reflectance, Rrs, was compared for natural ocean water models representing oligotrophic, mesotrophic and eutrophic environments (characterized by chlorophyll-a concentrations of 0.1, 1 and 10 mg/m3, respectively) and polluted by three different kinds of oils: biodiesel, lubricant oil and crude oil. We found out that dispersed oil usually increases Rrs values for all types of seawater, with the highest effect for the oligotrophic ocean. In the clearest studied waters, the absolute values of Rrs increased 2–6 times after simulated dispersed oil pollution, while Rrs band ratios routinely applied in bio-optical models decreased up to 80%. The color index, CI, was nearly double reduced by dispersed biodiesel BD and lubricant oil CL, but more than doubled by crude oil FL.

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Calculation of oil droplet size distribution in ocean oil spills: A review

Cited by 45 publications

References 72 publications

A population balance model for large eddy simulation of polydisperse droplet evolution

A population balance model for large eddy simulation of polydisperse droplet evolution

Formation, Detection, and Modeling of Submerged Oil: A Review

Remote Sensing of Dispersed Oil Pollution in the Ocean—The Role of Chlorophyll Concentration

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