Lateral migration of viscoelastic capsules in tube flow
Bibliographic record
Abstract
In this article, the lateral migration process of a viscoelastic capsule of spherical original shape in a tube flow is simulated. The capsule membrane follows the Skalak constitutive law for elasticity, and the membrane viscosity is modeled using the recently developed finite difference scheme. The methodology is validated carefully by simulating the tank-treading motion of an elastic capsule in shear flow. The lateral migration of viscoelastic capsules is then investigated in detail with various combinations of viscosity ratio, membrane shear viscosity, and capillary number. In general, the migration process starts with an initial transient phase, where the capsule deformation and migration velocity suddenly increase from zero to a maximum value. Following that, the deformation and migration velocity gradually reduce as the capsule moves toward the tube centerline. The capsule also performs continuous rotation during the migration, and the rotation gradually slows down with the capsule migration. The interior-exterior fluid viscosity contrast and the membrane viscosity have similar effects in reducing the capsule deformation and inclination angle to the flow direction; however, a strong membrane viscosity may introduce significant oscillations in the capsule deformation, inclination, and migration velocity. Due to the reduced capsule deformation, the migration velocity and capsule rotation become slower for capsules with higher viscosity contrast and/or membrane viscosity. Moreover, the influence of membrane viscosity on the migration dynamics intensifies at higher capillary number. Finally, empirical correlations are proposed for the migration velocity and rotation period, and the proposed relations match fairly well the simulation results, which cover wide ranges of system parameters. The discussions and analysis could be valuable for better understanding the complicated flow–capsule interaction and capsule dynamics in the migration process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".