Modeling of pneumatic melt drawing of polypropylene super-thin fibers in the Laval nozzle
Bibliographic record
Abstract
Abstract Melt spinning of the fibers by supersonic air jet in the Laval nozzle is a novel, efficient and energy saving method of formation of super-thin fibers. In the process, polymer melt is extruded from a row of orifices and fast drawn by the pneumatic forces. In the modelling, air velocity, temperature and pressure distributions are computed from the k-! aerodynamic model. Computations of the polymer air-drawing dynamics are based on the mathematical model of melt spinning in a single-, thin-filament approximation and Phan-Thien/Tanner non-linear viscoelasticity of the polymer melt. Axial profiles of the polymer velocity, temperature, tensile stress and rheological extra-pressure are computed. Influence of the Laval nozzle geometry, initial air compression, an initial melt temperature, a polymer mass output and the diameter of the melt extrusion die is discussed. The role of the polymer molecular weight, melt viscosity and relaxation time is considered. Example computations show the influence of important processing and material parameters. In the supersonic process, a high negative internal extra-pressure is predicted in the polymer melt under high elongation rates which may lead to cavitation and longitudinal burst splitting of the filament into a high number of sub-filaments. A hypothetical number of sub-filaments at the splitting is estimated from an energetic criterion. The diameter of the sub-filaments may reach the range of nano-fibers. A substantial influence of the Laval nozzle geometry is also predicted.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".