Rheology of Semi-Flexible Fiber Suspensions
Bibliographic record
Abstract
The rheological behavior of model suspensions consisting of a Newtonian silicone oil and fibers of different flexibilities have been investigated in steady and transient shear flows. Various fiber suspensions have been prepared to investigate the effect of flexibility parameters (stiffness, aspect ratio) as well as the role of interactions in the semi‐concentrated and concentrated regimes. The experimental results show that the viscous and elastic properties of the fiber suspensions are strongly influenced by fiber flexibility. The suspensions' viscosity and first normal stress differences increase as fibers become more flexible, The suspensions also exhibit enhanced shear thining and the shear rate for the onset of shear thinning decreases as flexibility increases. In addition, fiber flexibility influences considerably the characteristic relaxation time, especially at low shear rate. In stress growth experiments the suspensions show stress overshoots, and the magnitude and width of the overshoots get larger as fiber flexibility increases. Under reversal flow, a delayed overshoot is observed for the various suspensions and the deformation at which the reverse overshoot occurs decreases with increasing fiber flexibility. This has been attributed to less orientation of the fibers in the flow direction during forward flow as flexibility increases. Finally the effect of fiber flexibilty on the rheological properties is more pronounced in the concentrated regime, and this is attributed to enhanced fiber‐fiber interactions at high filler content and flexibility.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".