喷嘴
外推法
机械
湍流
海底
粒子图像测速
喷射(流体)
消散
湍流动能
比例(比率)
比例模型
材料科学
物理
地质学
热力学
数学
岩土工程
航空航天工程
工程类
统计
量子力学
作者
Simeon Pesch,Rebecca Knopf,Anahita Radmehr,Claire B. Paris,Zachary M. Aman,Marko Hoffmann,Michael Schlüter
出处
期刊:Multiphase Science and Technology
[Begell House Inc.]
日期:2020-01-01
卷期号:32 (2): 113-136
被引量:7
标识
DOI:10.1615/multscientechn.2020031347
摘要
To 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI