Influence of a Meltblown Die with a Laval Airstream Channel on the Manufacturing Process of a Polymer Fiber Based on an Orthogonal Test and Simulation Analysis
Bibliographic record
Abstract
A Laval nozzle is a device that accelerates a low-speed airstream to form a high-speed airstream. In this work, we use a Laval nozzle in the airstream channel design of a meltblown die to improve the tensile properties of the fiber in the airstream field of the meltblown die. The features of the airstream field of the meltblown die are analyzed by numerical simulation. For a given parametrization, six factors may be tuned to optimize the performance of the Laval airstream channel of the meltblown die. We thus use a five-level, six-factor orthogonal test method to optimize the airstream channel of the meltblown die to determine the various factors that influence the airstream field beneath the meltblown die. The results show that the optimized Laval meltblown die performs better than the traditional die and that the widths of the larynx and expansion segment most strongly affect the airstream velocity beneath the Laval meltblown die. Compared with a traditional die, the Laval die optimized by orthogonal testing increases the peak airstream velocity by 17.54%, average velocity by 96.81%, average temperature by 12.32%, and peak pressure by 14.61% and produces weaker turbulence intensity near the spinneret. These characteristics make the airstream beneath the die more stable and uniform and accelerate the attenuation of the fiber diameter, producing more polymer nanofibers. These results demonstrate a valuable approach to the design and optimization of meltblown dies and provide a technical reference for the production and application of the meltblown fiber production equipment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".