Secondary convection due to second normal stress differences: A new mechanism for the mass transport of solutes in pressure-driven flows of concentrated, non-colloidal suspensions
Bibliographic record
Abstract
A mechanism for enhancement in the mass transfer rates of solutes in flowing, concentrated suspensions that is often cited in the literature is the self-diffusion of particles arising from shear-induced interparticle interactions. Recently, it was demonstrated by Zrehen and Ramachandran (Phys. Rev. Lett., 2013, 110, 018306) that the pressure-driven flow of suspensions through non-axisymmetric geometries is not unidirectional; the main flow is accompanied by secondary currents within the cross-section of the conduit, driven by second normal stress differences. This secondary convection represents a new and heretofore unexplored, advective mechanism for the mass transfer of solutes normal to the primary streamlines in flowing suspensions, and is investigated in this paper via simulations. For small particle sizes, the enhancement of solute diffusivity by shear-induced self-diffusion is weak. However, the magnitude of the secondary currents is unaffected by particle size. Thus, for suspensions with particles much smaller than the conduit size, secondary convection, and not shear-induced self-diffusion, can be the dominant mechanism for shear-induced enhancement of mass transfer. In the limit where shear-induced self-diffusion is the dominant diffusive mode of mass transfer, secondary convection can, for some geometries and conditions, double the augmentation of mass transfer over that expected from self-diffusion alone. The relevance of this new mechanism of mass transfer is in the improved modeling of the transport of solutes for which the dominant mass transfer resistance is in the suspension phase. This mechanism also suggests the possibility of exploiting conduit geometry to improve the mass transfer rate of solutes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".