Investigation of solids transport in a single‐screw extruder using a 3‐D discrete particle simulation
Bibliographic record
Abstract
Abstract A non‐isothermal, 3‐D discrete particle simulation based on the discrete element method (DEM) was developed to simulate the solids‐conveying zone and feed hopper of a single‐screw extruder. The method considers each particle in a granular assembly as a separate entity that can interact with other particles or boundaries through collisions or lasting contacts. By using DEM to model the extrusion environment, a priori knowledge of the solids flow was not required in order to simulate the motion of a granular assembly with reasonable accuracy, allowing studies to be conducted in the absence of the solid plug assumption typical of classical solids‐conveying models. In this paper, predicted results were limited to low levels of compaction in the solids assembly (i.e., no particle deformation), in order to understand the behavior of the polymer pellets in their most dynamic state. The results of the DEM model showed reasonably good agreement with experimental data, providing comparable bulk values like output rate, yet also demonstrating its ability to capture the dynamics of solids particle conveying. The model captured the inherent variability of extrusion such as the low‐amplitude, high‐frequency fluctuations referred to as “solids pulsing” and the recirculation of pellets in the feed throat. Polym. Eng. Sci. 44:2203–2215, 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 | 0.001 |
| 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".