The role of a porous wall on the solute dispersion in a concentric annulus
Bibliographic record
Abstract
We studied the role of a porous wall on the dispersion of a solute in an annular space in the presence of a pressure-driven flow. The continuity of concentration and mass flux at the annulus–porous medium interface is used to handle the interaction between the two media. The Reynolds decomposition technique and the cross-sectional averaging method are used to derive a reduced-order advective–dispersive transport model with the associated equivalent diffusion and advection terms. The resultant dispersion and advection coefficients for an annulus with a porous wall are fully characterized as a function of the inner solid core radius of the annulus. The findings reveal that dispersion is retarded in the presence of the inner core in an annulus for both porous and non-porous outer walls. The results also indicate that the transition to a fully advective regime occurs at higher Peclet numbers for an annulus with a porous outer wall. The results suggest that dispersion and advection in an annulus can be controlled by proper selection of the inner core diameter. We also identified the inner core size where the solute dispersion in an annulus with a porous wall is minimum compared to a non-porous boundary. The developed model and insights find applications in many engineering processes where a fluid containing a solute in an annulus interacts with a surrounding porous medium.
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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 | 0.001 |
| 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 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".