Comparative Simulations of Taylor Flow with Surfactants Based on Sharp- and Diffuse-Interface Methods

2017 ◽  
pp. 639-661 ◽  
Author(s):  
Sebastian Aland ◽  
Andreas Hahn ◽  
Christian Kahle ◽  
Robert Nürnberg
Keyword(s):  
Diffuse Interface ◽  
Taylor Flow

Quantitative comparison of Taylor flow simulations based on sharp-interface and diffuse-interface models

2013 ◽  
Vol 73 (4) ◽  
pp. 344-361 ◽  
Author(s):  
S. Aland ◽  
S. Boden ◽  
A. Hahn ◽  
F. Klingbeil ◽  
M. Weismann ◽  
...  
Keyword(s):  
Quantitative Comparison ◽  
Sharp Interface ◽  
Diffuse Interface ◽  
Flow Simulations ◽  
Taylor Flow ◽  
Interface Models

scholarly journals Contact-line behavior in boiling on a heterogeneous surface: Physical insights from diffuse-interface modeling

2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Biao Shen ◽  
Jiewei Liu ◽  
Gustav Amberg ◽  
Minh Do-Quang ◽  
Junichiro Shiomi ◽  
...  
Keyword(s):  
Contact Line ◽  
Heterogeneous Surface ◽  
Diffuse Interface ◽  
Interface Modeling

scholarly journals Reconstruction of the 3D pressure field and energy dissipation of a Taylor droplet from a $$\upmu$$PIV measurement

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Ulrich Mießner ◽  
Thorben Helmers ◽  
Ralph Lindken ◽  
Jerry Westerweel
Keyword(s):  
Energy Dissipation ◽  
Pressure Gradient ◽  
Pressure Field ◽  
Estimation Method ◽  
Dissipated Energy ◽  
Spatial Integration ◽  
Mean Flow ◽  
Bypass Flow ◽  
Taylor Flow ◽  
Piv Measurement

Abstract In this study, we reconstruct the 3D pressure field and derive the 3D contributions of the energy dissipation from a 3D3C velocity field measurement of Taylor droplets moving in a horizontal microchannel ($$\rm Ca_c=0.0050$$ Ca c = 0.0050 , $$\rm Re_c=0.0519$$ Re c = 0.0519 , $$\rm Bo=0.0043$$ Bo = 0.0043 , $$\lambda =\tfrac{\eta _{d}}{\eta _{c}}=2.625$$ λ = η d η c = 2.625 ). We divide the pressure field in a wall-proximate part and a core-flow to describe the phenomenology. At the wall, the pressure decreases expectedly in downstream direction. In contrast, we find a reversed pressure gradient in the core of the flow that drives the bypass flow of continuous phase through the corners (gutters) and causes the Taylor droplet’s relative velocity between the faster droplet flow and the slower mean flow. Based on the pressure field, we quantify the driving pressure gradient of the bypass flow and verify a simple estimation method: the geometry of the gutter entrances delivers a Laplace pressure difference. As a direct measure for the viscous dissipation, we calculate the 3D distribution of work done on the flow elements, that is necessary to maintain the stationarity of the Taylor flow. The spatial integration of this distribution provides the overall dissipated energy and allows to identify and quantify different contributions from the individual fluid phases, from the wall-proximate layer and from the flow redirection due to presence of the droplet interface. For the first time, we provide deep insight into the 3D pressure field and the distribution of the energy dissipation in the Taylor flow based on experimentally acquired 3D3C velocity data. We provide the 3D pressure field of and the 3D distribution of work as supplementary material to enable a benchmark for CFD and numerical simulations. Graphical abstract

scholarly journals Learning patient‐specific parameters for a diffuse interface glioblastoma model from neuroimaging data

2020 ◽  
Vol 43 (15) ◽  
pp. 8945-8979
Author(s):  
A. Agosti ◽  
P. Ciarletta ◽  
H. Garcke ◽  
M. Hinze
Keyword(s):  
Diffuse Interface ◽  
Patient Specific ◽  
Neuroimaging Data
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