Dispersion of a passive tracer in the pressure-driven flow of a non-colloidal suspension
Bibliographic record
Abstract
This paper numerically quantifies the dispersion of a solute, and in particular, the Taylor dispersion, in the pressure-driven flow of a non-colloidal suspension at moderately high volume fractions (0.2 to 0.5) through conduits of different cross-sectional shapes. An obvious intuition is that the Taylor dispersivity should increase owing to a decrease in the molecular diffusivity of the solute in the presence of particles impermeable to the solute; however, this is true only at low volume fractions. At higher volume fractions, three other physical effects become important, all of which lead to a reduction in Taylor dispersivity relative to a Newtonian fluid. The first is the blunting of the velocity profile resulting from particle migration into the low shear-stress regions, an effect that has been alluded to in the past by Roht et al. [J. Contam. Hydrol., 2013, 14, 10] and is important only at low Péclet numbers (Pe). At higher Pe, the two stronger effects are shear-induced solute self-diffusion, which arises due to shear-induced particle-particle interactions, and secondary convection, which is observed in non-axisymmetric cross-sections as a result of the second normal stress differences exhibited by concentrated suspensions. For a given volume fraction and cross-sectional geometry, a regime map, developed using a scaling analysis, delineates five regimes of dispersion involving one or a combination of the mass transfer mechanisms mentioned above. Our analysis also suggests that the cross-sectional shape can be exploited to enhance or suppress solute dispersion by modifying the secondary current strength and profile.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".