Computer modeling of pneumatic formation of superthin fibres
Bibliographic record
Abstract
Dynamics of a novel pneumatic process of superthin fibres formation from polymer melts in supersonic air jets in the Laval nozzle is studied using computer simulation. The approach bases on the mathematical k- models of air flow in the nozzle and air drawing of polymer filaments in the coaxial air jet. The aerodynamic fields can be considered as undisturbed by presence of a single row of thin polymer filaments and predetermined air conditions are used in the modeling. The air fields are simulated for several values of the air compressions in the nozzle inlet and two nozzle geometries. Driving force of the Laval nozzle process results from the air drag forces acting onto the filament surface. Mathematical model of stationary melt spinning in single-, thin-filament approximation is applied with the effects of non-linear viscoelasticity of the polymer melt accounted for. The model allows also to discuss non-linear stress-optical relationship reflecting online molecular orientation, as well as online crystallization of the polymer filament if it occurs. Negative rheological extra-pressure in the air-drawn filament is predicted, as resulting from non-linear viscoelasticity of the polymer melt subjected to high elongation rates. The negative extra-pressure could lead to cavitation and longitudinal burst splitting of each filament into a high number of superthin sub-filaments. A hypothetical mean diameter of the sub-filaments is estimated from an energetic criterion. Example computations of the dynamic profiles of air drawing and discussion concern isotactic polypropylene (iPP) subjected to the Laval nozzle 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".