Particle settling in a shear-thinning, viscoelastic fluid in the presence of wall effects
Bibliographic record
Abstract
Abstract The settling of particles in fluids is a widespread phenomenon and commonly involves accounting for the effects of walls. Particle settling and wall effects are well understood for Newtonian fluids but the consequences of non-Newtonian fluid properties on particle settling are less well known. Here, we present the results from a set of experiments quantifying wall effects on particle settling within quiescent shear-thinning and viscoelastic (non-Newtonian) fluids for sphere-to-tube diameter ratios $$\lambda \le 0.3$$ λ ≤ 0.3 . We find that wall effects on particle settling are reduced in non-Newtonian fluids and settling velocities are poorly predicted by conventional wall-corrected Stokes’ equations. We show that deviations in settling velocity are due to both the shear-thinning and viscoelastic properties of the fluid. Supported by our experimental dataset, we are able to show that calculating the shear-rate based on the particle diameter length-scale corresponds to an apparent viscosity that appropriately accounts for shear-thinning effects. A further correction factor for viscoelastic behaviour based on $$\lambda$$ λ and the Weissenberg number, Wi, is applied, and shows good agreement with all experimentally measured velocities. Together, we provide a quantitative method to accurately predict the terminal settling velocity of particles in shear-thinning, viscoelastic fluids up to sphere-to-tube diameter ratios of 0.3.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".