Calendering of thermoplastics: models and computations
Bibliographic record
Abstract
Abstract John Vlachopoulos (JV) started his polymer processing career with the process of calendering. In two landmark papers with Kiparissides, C. and Vlachopoulos, J. (1976). Finite element analysis of calendering. Polym. Eng. Sci. 16: 712–719; Kiparissides, C. and Vlachopoulos, J. (1978). A study of viscous dissipation in the calendering of power-law fluids. Polym. Eng. Sci. 18: 210–214 he introduced the Finite Element Method (FEM) to solve the governing equations of mass, momentum, and energy based on the Lubrication Approximation Theory (LAT). This early work was followed by the introduction of wall slip (with Vlachopoulos, J. and Hrymak, A.N. (1980). Calendering poly(vinyl chloride): theory and experiments. Polym. Eng. Sci. 20: 725–731). The first 2-D simulations for calendering PVC were carried out with Mitsoulis, E., Vlachopoulos, J., and Mirza, F.A. (1985). Calendering analysis without the lubrication approximation. Polym. Eng. Sci. 25: 6–18. In the intervening 35 years, other works have emerged, however our understanding has not been drastically improved since JV’s early works. Results have also been obtained for pseudoplastic and viscoplastic fluids using the general Herschel-Bulkley constitutive model. The emphasis was on finding possible differences with LAT regarding the attachment and detachment points of the calendered sheet (hence the domain length), and the extent and shape of yielded/unyielded regions. The results showed that while the former is well predicted by LAT, the latter is grossly overpredicted. More results have been obtained for 3-D simulations, showing intricate patterns in the melt bank. Also, the transient problem has been solved using the ALE-FEM formulation for moving free-boundary problems. The results are compared with the previous simulations for the steady-state and show a good agreement. The transient simulations capture the movement of the upstream and downstream free surfaces, and also provide the attachment and detachment points, which are unknown a priori. Finding these still remains the prevailing challenge in the modeling of the calendering process.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".