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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".