A <scp>CFD‐DEM</scp> approach to study the breakup of fractal agglomerates in an internal mixer
Bibliographic record
Abstract
Abstract In this work we present a method to investigate the breakup of filler agglomerates in an internal mixer during a compounding operation. The method employs computational fluid dynamics (CFD) simulations along with discrete element method (DEM) simulations. CFD simulations are performed to compute the flow field inside a 2D section of a typical batch internal mixer with two tangential rotors. During the CFD simulation, we assume the filler agglomerates to behave as tracer particles, carried passively by the flow. The trajectory of the tracers, together with the experienced velocity gradients, are fed to a DEM code, built in the framework of Stokesian dynamics. The code computes the mechanical response of the agglomerates along the trajectory, from which it is finally possible to ascertain the occurrence of breakup. Simulations are performed to evaluate the robustness of the method on two different rotor speed ratio conditions and varying agglomerate strength.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".