Bibliographic record
Abstract
When molten plastic is extruded from a die, it can collect on the open die face. Called die drool, this phenomenon costs plastics manufacturers by requiring shutdown for die cleaning. This has been attributed to cohesive failure within the fluid at an internal surface, where the fluid slips on itself; the corresponding isothermal analysis led to an analytical solution for the drool rate (Schmalzer and Giacomin, J. Polym. Eng. 2013, 33, 1). In this paper, we account for the frictional heating at the cohesive slip interface, which we call slip heating. We focus on slit flow, which is used in film casting, sheet extrusion, curtain coating, and in many other chemical engineering unit operations. In slit flow, the magnitude of the heat flux from the slipping interface is the product of the shear stress and the slip speed. We present the solution for the temperature rise in pressure‐driven slit flow subject to constant heat generation at the cohesive slip interface. We solve the energy equation in Cartesian coordinates for the temperature rise, for both the transient and steady temperature profiles, in both the drool layer and the bulk polymer. We then evaluate the effect of this temperature rise on the rate of die drool. For this simplest relevant non‐isothermal problem, we neglect viscous dissipation and convective heat transfer in the melt and we model viscosity as an Arrhenius function of temperature. We conclude with three worked examples showing the relevance of slip heating in determining die drool flow rates. We find that slip heating diminishes die drool. We arrive at two sufficient dimensionless conditions for the accurate use of our results: Br ≪1 or Gi ≪1.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".