Settling dynamics of circular particles in vibrating tanks filled with a yield-stress liquid
Bibliographic record
Abstract
The effect of sinusoidal vibration is numerically investigated on the settling dynamics of a heavy particle of circular shape immersed in a viscoplastic fluid obeying the regularized Bingham–Papanastasiou (BP) model. Having modeled the solid particle as a highly viscous Newtonian droplet, we have relied on the finite element method for solving the equations of motion for the particle and the surrounding fluid. Our numerical results could closely recover the theoretical critical Bingham number of 0.0658 for circular particles settling in Bingham materials. Using a very small threshold velocity to decide whether a particle is stuck or unstuck, it is shown that stuck particles can be excited to fall in the BP liquid provided that, for any given frequency, the amplitude is larger than a minimum value. An increase in the size of the particle or its density is predicted to reduce the threshold amplitude. In general, vibration is found to have an accelerating effect on particle settling in the BP liquid although the effect is non-monotonic. The accelerative effect of vibration is attributed to the enlarged size of the yielded zone, while the non-monotonic behavior is attributed to the effect of the sidewalls. At high density ratios, a phase lag is predicted to arise between the particle and the vessel. A comparison between the obtained numerical results with published experimental data for spherical particles suggests that, in settling flows, circular particles can be used as a good paradigm for spherical particles.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".