Article (Scientific journals)
A Model to Predict Liquid Bridge Formation Between Wet Particles Based on Direct Numerical Simulations
Wu, Mingqiu; Khinast, Johannes; Radl, Stefan
2016In AIChE Journal, 62 (6), p. 1877-1987
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Keywords :
granular flows; liquid bridge; volume of fluid method
Abstract :
[en] We study dynamic liquid bridge formation, which is relevant for wet granular flows involving highly viscous liquids and short collisions. Specifically, the drainage process of liquid adhering to two identical, non-porous wet particles with different initial film heights is simulated using Direct Numerical Simulations (DNS). We extract the position of the interface, and define the liquid bridge and its volume by detecting a characteristic neck position. This allows us building a dynamic model for predicting bridge volume, and the liquid remaining on the particle surface. Our model is based on two dimensionless mobility parameters, as well as a dimensionless time scale to describe the filling process. In the present work model parameters were calibrated with DNS data. We find that the proposed model structure is sufficient to collapse all our simulation data, indicating that our model is general enough to describe liquid bridge formation between equally sized particles
Disciplines :
Chemical engineering
Computer science
Mechanical engineering
Author, co-author :
Wu, Mingqiu ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Khinast, Johannes;  Graz University of Technology > Institute of Process and Particle Engineering
Radl, Stefan;  Technische Universität Graz - TUG > Institute of Process and Particle Engineering
External co-authors :
yes
Language :
English
Title :
A Model to Predict Liquid Bridge Formation Between Wet Particles Based on Direct Numerical Simulations
Publication date :
16 February 2016
Journal title :
AIChE Journal
ISSN :
1547-5905
Publisher :
John Wiley & Sons, Inc, Hoboken, United States - New Jersey
Volume :
62
Issue :
6
Pages :
1877-1987
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
Funders :
FWF - Austrian Science Fund [AT]
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