Fault characterization using volumetric curvature from continuous phase spectrum

Author(s):  
Hussain Syed Kazmi ◽  
Aftab Alam ◽  
Sohaib Ahmad ◽  
Arsalan Shafiq
1992 ◽  
Vol 57 (7) ◽  
pp. 1419-1423
Author(s):  
Jindřich Weiss

New data on critical holdups of dispersed phase were measured at which the phase inversion took place. The systems studied differed in the ratio of phase viscosities and interfacial tension. A weak dependence was found of critical holdups on the impeller revolutions and on the material contactor; on the contrary, a considerable effect of viscosity was found out as far as the viscosity of continuous phase exceeded that of dispersed phase.


Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 162 ◽  
Author(s):  
Thorben Helmers ◽  
Philip Kemper ◽  
Jorg Thöming ◽  
Ulrich Mießner

Microscopic multiphase flows have gained broad interest due to their capability to transfer processes into new operational windows and achieving significant process intensification. However, the hydrodynamic behavior of Taylor droplets is not yet entirely understood. In this work, we introduce a model to determine the excess velocity of Taylor droplets in square microchannels. This velocity difference between the droplet and the total superficial velocity of the flow has a direct influence on the droplet residence time and is linked to the pressure drop. Since the droplet does not occupy the entire channel cross-section, it enables the continuous phase to bypass the droplet through the corners. A consideration of the continuity equation generally relates the excess velocity to the mean flow velocity. We base the quantification of the bypass flow on a correlation for the droplet cap deformation from its static shape. The cap deformation reveals the forces of the flowing liquids exerted onto the interface and allows estimating the local driving pressure gradient for the bypass flow. The characterizing parameters are identified as the bypass length, the wall film thickness, the viscosity ratio between both phases and the C a number. The proposed model is adapted with a stochastic, metaheuristic optimization approach based on genetic algorithms. In addition, our model was successfully verified with high-speed camera measurements and published empirical data.


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