Three-dimensional Numerical Study of the Mixing Behaviour of Twin-screw Elements
Bibliographic record
Abstract
Abstract In this work the numerical modeling of the flow inside co-rotating twin-screw extruders is performed and solutions are analyzed to determine the mixing behavior of two screw elements: conveying and mixing elements. The flow around intermeshing screws is computed using an immersed boundary finite element method capable of dealing with complex moving solid boundaries. The flow is considered isothermal and the material behaves as a generalized non-Newtonian fluid. Because the viscosity depends on the shear rate, solutions will be shown for various rotation velocities of the screw. The 3D solutions are then analyzed in order to determine various parameters characterizing the flow mixing such as the residence time and the linear stretch. Residence time distribution inside the twin-screw extruder is first computed by using a particle tracking algorithm based on a fourth order Runge-Kutta method. A large number of particles are tracked inside the extruder and the resulting particle data is used to determine the distribution of the residence time and of the linear stretch. The spatial distribution of the residence time is also computed by solving a transport equation tracking the injection time of the polymer melt. The methodology shows important differences in the mixing behavior of the screw elements considered.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".