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Item type:Publication, Access status: without files , The choice of droplet size probability distribution function for oil spill modeling is not trivialThe droplet size distribution (DSD) formed by gas-saturated oil jets is one of the most important characteristics of the flow to understand and model the fate of uncontrolled deep-sea oil spills. The shape of the DSD, generally modeled as a theoretical lognormal, Rosin-Rammler or non-fundamental distribution function, defines the size and the mass volume range of the droplets. Yet, the fundamental DSD shape has received much less attention than the volume median size (d50) and range of the DSD during ten years of research following the Deepwater Horizon (DWH) blowout. To better understand the importance of the distribution function of the droplet size we compare the oil rising time, surface oil mass, and sedimented and beached masses for different DSDs derived from the DWH literature in idealized and applied conditions, while keeping d50 constant. We highlight substantial differences, showing that the probability distribution function of the DSD for far-field modeling is, regardless of the d50, consequential for oil spill response.Publicationtype: Journal Article69 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Behavior of Rising Droplets and Bubbles: Impact on the Physics of Deep-Sea Blowouts and Oil Fate(Springer, 2020); ; ; ; Publicationtype: Book Part77 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Jet Formation at the Spill Site and Resulting Droplet Size Distributions(Springer, 2020); ; ; ; The size distribution of oil droplets and gas bubbles forming at the exit geometry of a deep-sea blowout is one of the key parameters to understand its propagation and fate in the ocean, whether with regard to rising time to the surface, drift by ocean currents, dissolution or biodegradation. While a large 8 mm droplet might rise to the sea surface within minutes or hours, microdroplets <100 µm may take weeks or months to surface, if at all. On the other hand, a microdroplet or bubble dissolutes faster due to its larger surface to volume ratio and is also more available for biodegrading bacteria. To be able to properly model these effects, it is necessary to understand the drop formation processes near the discharge point and to predict the evolving droplet size distribution (DSD) for the specific conditions. In this chapter, the general breakup mechanisms and flow regimes of an oil-in-water jet are discussed in Sect. 4.1. Section 4.2 focuses on the different approaches to determine the DSD in the laboratory and field settings and critically reviews the existing datasets. State-of-the-art models for the prediction of the DSD of a subsea oil discharge are presented alongside a new approach based on the turbulent kinetic energy (TKE) in Sect. 4.3, while Sect. 4.4 takes a closer look at the specific effects of the deep sea on the DSD. Based on this, Sect. 4.5 discusses the advantages and limitations of subsea dispersant injection. Section 4.6 provides a summary of the chapter and gives an outlook to unresolved questions.Publicationtype: Book Part122 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Experimental investigation, scale-up and modeling of droplet size distributions in turbulent multiphase jetsTo accurately model the subsea distribution of accidentally released crude oil as a result of deep-sea oil well blowouts, the initial droplet size distribution is one of the most important input parameters. To date, only a very limited number of datasets contain results from large-scale experiments. Hence, a vast extrapolation to the field scale is often required, causing uncertainty when applying model correlations for the prediction of the ensuing droplet sizes, which have been tuned by means of lab-scale experimental results, to the field scale. In this paper, we present two-phase oil-in-water jet experiments conducted in two differently sized facilities with optical access. Lab-scale experiments with nozzle pipe diameters of 1-7.5 mm are compared to pilot-plant-scale experiments with nozzle pipe diameters of 32-74 mm. For the 32 mm nozzle pipe, additional cases with built-ins upstream of the nozzle exit are run for comparison, in order to simulate the influence of irregular blowout geometries. Endoscopic imaging is employed for the investigation of the droplet sizes. For the validation of the expected velocities and levels of the turbulent kinetic energy dissipation rate, particle image velocimetry is used. The resulting droplet size distributions are approximately log-normal. A model correlation for the prediction of droplet sizes based on existing literature correlations using the energy dissipation rate is deduced. Scale-up from lab to large scale is carried out on the basis of this correlation. The large-scale experimental results deviate significantly from the model correlation tuned by means of the small-scale results, which is referable to an upper limit of droplet stability. Considering the respective equation for this limit, in conjunction with the presented model correlation, provides a reasonable prediction of expectable droplet sizes for any scale of experiments.Publicationtype: Journal Article92 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Far-Field Modeling of a Deep-Sea Blowout: Sensitivity Studies of Initial Conditions, Biodegradation, Sedimentation, and Subsurface Dispersant Injection on Surface Slicks and Oil Plume Concentrations(Springer, 2020); ; ; ; Modeling of large-scale oil transport and fate resulting from deep-sea oil spills is highly complex due to a number of bio-chemo-geophysical interactions, which are often empirically based. Predicting mass-conserved total petroleum hydrocarbon concentrations is thus still a challenge for most oil spill models. In addition, dynamic quantification and visualization of spilled oil concentrations are necessary both for first response and basin-wide impact studies. This chapter presents a new implementation of the Connectivity Modeling System (CMS) oil application that tracks individual multi-fraction oil droplets and estimates oil concentrations and oil mass in a 3D space grid. We used the Deepwater Horizon (DWH) blowout as a case study and performed a sensitivity analysis of several modeling key factors, such as biodegradation, sedimentation, and alternative initial conditions, including droplet size distribution (DSD) corresponding to an untreated and treated live oil from subsurface dispersant injection (SSDI) predicted experimentally under high pressure and by the VDROP-J jet-droplet formation model. This quantitative analysis enabled the reconstruction of a time evolving three-dimensional (3D) oil plume in the ocean interior, the rising and spreading of oil on the ocean surface, and the effect of SSDI in shifting the oil to deeper waters while conserving the mass balance. Our modeling framework and analyses are thus important technical advances for understanding and mitigating deep-sea blowouts.Publicationtype: Book Part64 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Resolving the dilemma of dispersant use for deep oil spill response(IOP Publishing, 2019-09-06); ; ; Publicationtype: Journal ArticleScopus© Citations 11 223 331 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The Pressure is On: Understanding Deep Oil Spills in the Ultra-Deep EnvironmentPublicationtype: Conference Paper78