Near-wall Estimates of the Concentration and Orientation Distribution of Semi-dilute Rigid Fiber Suspensions in Poisieulle Flow
Bibliographic record
Abstract
A model is presented to describe the orientation and concentration state of semi-dilute, rigid fiber suspensions in a rectangular channel flow. A probability distribution function is used to describe the local orientation and concentration state of the suspension and evolves according to a Fokker-Plank type equation. Long range hydrodynamic fiber-fiber interactions are modeled using the approach outlined by Folgar and Tucker (J. Reinforced Plast. Comp. 3 98–119 1984). Near the channel walls, we apply the no-flux boundary conditions proposed by Schiek and Shaqfeh (J. Fluid Mech. 296, 271–324, 1995). Geometric constraints are used to couple the fibers’ rotary motion with its translational motion. This eliminates physically unrealistic orientation states in the near-wall region. A two-way coupling between the fiber orientation state and the momentum equations of the suspending fluid is considered. Experiments are performed to validate the numerical model by visualizing the motion of tracer fibers in an index-of-refraction matched suspension. The orientation distribution function is determined experimentally as a function of channel height. The results indicate that at distances less than one half fiber length from the channel walls, the model accurately predicts the available fiber orientation states and the distribution of fibers amongst these states. The model further predicts a sharp concentration gradient in this region.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".