Extended oil spill spreading with Langmuir circulation

Simecek-Beatty, Debra; Lehr, William

doi:10.1016/j.marpolbul.2017.06.047

Cited by 37 publications

(14 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spreading refers to the creation of a thin film, expanding over the sea surface, as soon as oil is being released [16,17]. Spreading algorithms in oil spill models provide an estimate of the spill thickness or surface area, used for modeling of many transport and fate processes such as evaporation, dispersion, and emulsification.…”

Section: Spreadingmentioning

confidence: 99%

“…A rigorous solution to the problem requires sea state and oil data that might not be available in the initial stage of an operational spill response. Simecek-Beatty and Lehr [16] used Langmuir circulation models to approximate the merging of oil streaks and modify existing oil spreading parametrizations by estimating a spreading surface area correction due to Langmuir effects. Their model has been validated with measurements from the 1990s North Sea field experiments, and as it requires limited data, it could be successfully incorporated into operational response oil spill models.…”

Section: Spreadingmentioning

confidence: 99%

See 1 more Smart Citation

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

156

View full text Add to dashboard Cite

Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil. This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions. Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion. The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing. Moreover, timely response to oil spills is lacking in the new generation of oil spill models. Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs.

show abstract

Section: Spreadingmentioning

confidence: 99%

Section: Spreadingmentioning

confidence: 99%

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

156

View full text Add to dashboard Cite

show abstract

“…They also estimated θ demonstrated that their approach significantly improves the prediction of upper ocean currents and temperatures. Simecek‐Beatty and Lehr () evaluated the impact of Langmuir circulations on the surface spreading of oil and devised a formula to account for the reduction of oil slick areas dependent on the Langmuir cells.…”

Section: Eddy Diffusivitymentioning

confidence: 99%

Transport of Oil Droplets in the Upper Ocean: Impact of the Eddy Diffusivity

et al. 2020

View full text Add to dashboard Cite

The transport of oil droplets following a surface oil spill was investigated using a uniform vertical eddy diffusivity model and the K‐profile parameterization model, which assumes a maximum K value at 1/3 depth of the mixed layer. The initial droplet size distribution was obtained based on the Delvigne and Sweeney (1988, https://doi.org/10.1007/s13131‐013‐0364‐7) model. Using a uniform eddy diffusivity Kave, an exact analytical solution was used to produce the transient and steady state profile of the concentration of droplets of all sizes. It was found that the concentration at the surface is proportional to the droplet rise velocity and inversely proportional to Kave. Thus, small droplets (smaller than 100 μm) do not persist at the water surface. It was found that K‐profile parameterization produces smaller concentrations at the water surface than the uniform K model. The impact of waves was introduced into the K‐profile parameterization model through a roughness height, zo, that is comparable to the wave height. The investigation herein reveals that the Delvigne and Sweeney approach, commonly used in oil spill modeling, is not sufficient to predict the oil droplet size distribution, and that one needs to use a vertical eddy diffusivity to accurately predict the transport in the following hours and days. A new dimensionless formulation was provided to generalize the results, and showed that transport depends on three major parameters, the water friction speed, the mixed layer depth, and the droplet diameter.

show abstract

“…(Elliott, 1986) suggests that vertical shear and differences in forcing between the surface and submerged oil leads to a protrusion of downwind extent. (Simecek-Beatty & Lehr, 2017) suggest that Langmuir circulation plays a major role for creating downwind streaks of oil spills. Most likely, a combination of various mechanisms is at play.…”

Section: Introductionmentioning

confidence: 99%

Integrated Analysis of Multisensor Datasets and Oil Drift Simulations—A Free‐Floating Oil Experiment in the Open Ocean

et al. 2021

View full text Add to dashboard Cite

Recent effort has gone into investigating the oil slick characterization capabilities of emerging radar sensors and drift simulations. Oil slick characterization involves problems such as separating mineral oil spills from look-alikes, for example, natural low-radar backscatter phenomena, extraction of physical oil properties such as the dielectric constant of the medium, estimating relative slick thickness, the oil volumetric fraction of an oil/water emulsion, and, finally, predicting slick drift and evolution. This work describes the experimental setup and results from a thoroughly planned and successfully conducted oil spill field campaign that took place in the North Sea in June 2019 as a part of the Norwegian Clean Seas Association for Operating Companies (NOFOs) annual oil-on-water exercise.

show abstract

Extended oil spill spreading with Langmuir circulation

Cited by 37 publications

References 33 publications

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Transport of Oil Droplets in the Upper Ocean: Impact of the Eddy Diffusivity

Integrated Analysis of Multisensor Datasets and Oil Drift Simulations—A Free‐Floating Oil Experiment in the Open Ocean

Contact Info

Product

Resources

About