Discrete particle simulations of solids compaction and conveying in a single‐screw extruder
Bibliographic record
Abstract
Abstract This paper studies granular flow and compaction behavior of high‐density polyethylene by discrete particle modeling in order to gain greater understanding of the stress distribution within the solids‐conveying zone of a single‐screw extruder. The contact force–displacement model used in the simulations was first validated by simulating uniaxial compression in a batch compaction cell. Subsequently, the discrete particle approach was used to model in 3D the movement of particles within the solids‐inflow and solids‐conveying zone of a 32‐mm single‐screw extruder. Results of the simulations showed that axial pressure development did not increase in an exponential manner, as suggested by continuum models, largely due to the compressibility of the solids. The nature by which pressure developed was shown to be further complicated by the retarding frictional forces of the granular bed, indicating Archimedean transport phenomena close to the feed opening when the head pressure was low and inadequate stress transmission occurred along the screw. In the cross‐channel direction, the anisotropic stress field predicted found that the highest pressure in the screw channel was located at the screw root, while the lowest pressure corresponded to the retreating flight. The results were subsequently discussed in comparison to available continuum models. POLYM. ENG. SCI., 48:62–73, 2008. © 2007 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".