Modeling of fiber–polymer coupling for suspensions of mono‐modal fibers
Bibliographic record
Abstract
Abstract The rheology of suspensions of fibers in polymer solutions is strongly dependent on fiber–fiber and fiber–polymer interactions. To model these interactions and their dependence on the flow and suspension properties, the steady shear viscosity of glass fibers in a polyethylene oxide polymer solution are measured for different fiber volume fractions and aspect‐ratios. The measurements are conducted for well characterized fiber samples that have a uniform and well defined aspect‐ratio and for moderate volume fractions. The results of the experimental study are used to correlate the polymer–fiber coupling factor and the fiber–fiber interaction coefficient using a mathematical model based on a modified FENE‐P (finitely extensible nonlinear elastic) constitutive equation. It was found that both parameters are strongly dependent on the characteristics of the suspension, but also depend on the flow shear rate that determines the degree of fiber orientation. In general, fiber–fiber and polymer–fiber interactions increase with both the aspect‐ratio and the volume fraction and are more important when the fibers are not fully oriented. POLYM. COMPOS., 27:82–91, 2006. © 2005 Society of Plastics Engineers
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".