Modeling of polymer melting, drop deformation, and breakup under shear flow
Bibliographic record
Abstract
Abstract Polyethylene (PE) or polycarbonate (PC) drop deformation and the breakup mechanism in a PE melt under shear flow were investigated using numerical simulations. The volume of fluid (VOF) method in FIDAP was used to track the dynamic interface. Two models were built for the investigation of a PE/PE system and a PE/PC system. Experimental data of polymer properties, such as specific heat capacity, viscosity, and heat conductivity, were incorporated in the simulations. For the PE/PE system, a temperature‐dependent viscosity model was used for the matrix PE and the dispersed PE. For the PE/PC system, generalized viscosity models were used for PE and PC with time‐dependent moving boundaries. An erosion mechanism similar to that observed in previous experiments was found for deformation and breakup of both PE and PC in the PE melt under simple shear flow. Local flow information, such as temperature, shear rate, viscosity, and shear stress, was obtained from the simulation results. The shear stress at the interface was much higher than the shear stress either in the dispersed phase or in the matrix phase, which could explain the erosion breakup mechanism. Polym. Eng. Sci. 44:1258–1266, 2004. © 2004 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".