Dr.-Ing. Marko Hoffmann
Eissendorfer Str. 38, Building O, Room 1.014
Tel.: +49 40 42878-3152
E-Mail: Marko Hoffmann.
- Construction and Apparatus Engineering
- Fundamentals of Process Engineering and Material Engineering
- Fundamentals of Technical Drawing
|Title: Experimental Investigation, Scale-Up and Modeling of Droplet Size Distributions in Turbulent Multiphase Jets. <em>Deep Oil Spills</em>|
|Written by: Pesch, S.; Knopf, R.; Radmehr, A.; Paris, C.; Zachary, A.; Hoffmann, M.; Schlüter, S.|
|in: <em>Multiphase Science and Technology</em>. (2020).|
|Volume: <strong>32</strong>. Number: (2),|
|on pages: 113-136|
Abstract: To satisfy the demand for the primary energy carrier oil, the exploitation of remote deposits, e.g. in the deep sea, is of increasing importance as readily accessible oil reservoirs are more and more depleted. The oil production from subsea reservoirs is challenging and associated with elevated risk, which became clear when the Deepwater Horizon drilling rig ignited and sank in the Gulf of Mexico in 2010 causing one of the largest oil spills in human history with 800,000 m³ of oil spilled into the ocean at a depth of 1,522 m. A quick and scientifically sound response in case of such a submarine oil blowout and the development of mitigation strategies for future oil spills are important to avert major damage to people and the environment. The success of these measures hinges on the accuracy of the used models that have been developed in order to simulate the fate of accidentally released crude oil and natural gas masses. One of the most crucial input parameters for these models is the ensuing droplet size distribution that depends on the specific deep-sea conditions (high pressure, low temperature) and the multiphase character of the highly turbulent oil/gas/water jet that exits the wellhead into the ocean. At Hamburg University of Technology, experimental facilities in laboratory and pilot-plant scale have been developed that enable the investigation of blowouts for the development of appropriate predictive correlations. Particle image velocimetry and endoscopic droplet size measurement technologies are used. Experimental data are shown in conjunction with modeling results.