Entry Flow of Polyethylene Melts in Tapered Dies
Bibliographic record
Abstract
Abstract The excess pressure losses due to end effects (mainly entrance) in the capillary flow of several types of polyethylenes were studied both experimentally and numerically under slip and no-slip conditions. These losses were first measured as a function of the contraction angle ranging from 15° to 90°. It was found that the excess pressure loss attains a local minimum at a contraction angle of about 30° for all types of polyethylenes examined. This was found to be independent of the apparent shear rate. This minimum becomes more dominant under slip conditions that were imposed by adding a significant amount of fluoropolymer into the polymer. Numerical simulations using a multimode K-BKZ viscoelastic model have shown that the entrance pressure drops can be predicted fairly well for all cases either under slip or no-slip boundary conditions. The clear experimental minimum at about 30° can only slightly be seen in numerical simulations, and at this point its origin is unknown. Further simulations with a viscous (Cross) model have shown that they severely under-predict the entrance pressure by an order of magnitude for the more elastic melts. Thus, the viscoelastic spectrum together with the extensional viscosity play a significant role in predicting the pressure drop in contraction flows, as no viscous model could. The larger the average relaxation time and the extensional viscosity are, the higher the differences in the predictions between the K-KBZ and Cross models are.
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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".