Rheology of a Dilute Suspension of Aggregates in Shear-Thinning Fluids.
fractal aggregates
intrinsic viscosity
numerical simulations
rheology
shear-thinning fluids
Journal
Micromachines
ISSN: 2072-666X
Titre abrégé: Micromachines (Basel)
Pays: Switzerland
ID NLM: 101640903
Informations de publication
Date de publication:
22 Apr 2020
22 Apr 2020
Historique:
received:
30
03
2020
revised:
14
04
2020
accepted:
21
04
2020
entrez:
26
4
2020
pubmed:
26
4
2020
medline:
26
4
2020
Statut:
epublish
Résumé
The prediction of the viscosity of suspensions is of fundamental importance in several fields. Most of the available studies have been focused on particles with simple shapes, for example, spheres or spheroids. In this work, we study the viscosity of a dilute suspension of fractal-shape aggregates suspended in a shear-thinning fluid by direct numerical simulations. The suspending fluid is modeled by the power-law constitutive equation. For each morphology, a map of particle angular velocities is obtained by solving the governing equations for several particle orientations. The map is used to integrate the kinematic equation for the orientation vectors and reconstruct the aggregate orientational dynamics. The intrinsic viscosity is computed by a homogenization procedure along the particle orbits. In agreement with previous results on Newtonian suspensions, the intrinsic viscosity, averaged over different initial orientations and aggregate morphologies characterized by the same fractal parameters, decreases by increasing the fractal dimension, that is, from rod-like to spherical-like aggregates. Shear-thinning further reduces the intrinsic viscosity showing a linear dependence with the flow index in the investigated range. The intrinsic viscosity can be properly scaled with respect to the number of primary particles and the flow index to obtain a single curve as a function of the fractal dimension.
Identifiants
pubmed: 32331480
pii: mi11040443
doi: 10.3390/mi11040443
pmc: PMC7231323
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : H2020 European Research Council
ID : 760907
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