Brownian rod-like particles suspension in non-homogeneous system: flow-microstructure coupling
Bibliographic record
Abstract
This study investigates the microstructure and rheological response of Brownian rod suspensions considering both spatial and orientational fluctuations in simple shear, Poiseuille and Couette flows. Focusing on rod-fluid interactions and concentration gradients, we account here for the effects of the rods concentration-orientation coupling. Numerical simulations, based on a kinetic macro-model previously derived [1], are used to analyze these results. In simple shear flow, the presence of the rods do not impact the flow, and translational diffusion does not modify the rheological properties of the suspensions. In Poiseuille flow, the rods cause a flattening of the velocity profile and the two-way coupling enhances the cross-streamlines migration toward the walls. The two-way coupling between the flow field and the rods control their orientation, migration behavior, and rheological properties. In Couette flow, rod-fluid coupling results in outward flow near the fixed cylinder. The translational diffusion plays a crucial role as higher translational Peclet numbers lead to pronounced migration of rods towards the channel walls and increased alignment in the flow direction. This coupling effect also affects the velocity profile in Couette flow. Our findings provide valuable insights into the complex behavior of suspended Brownian rods in different flow conditions.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".