Stokesian dynamics and the settling behaviour of particle–fibre‐mixtures
Bibliographic record
Abstract
Abstract For many industrial applications, like purification of waste water, some filtration processes and the paper recycling process, knowledge about the sedimentation and separation behaviour of fibres and particles is required. Experiments with many particles or fibres predict a great influence between them. From literature it is well known that the sedimentation behaviour is influenced by parameters like the concentration of the suspension, the physico–chemical interactions, etc. Nearly all reported experiments were performed with pure particle or pure fibre suspensions. The core difference between those suspensions is the shape of the fibres or long bodies which causes an orientation and agglomeration during the sedimentation. Hardly any experiments are reported with suspensions where particles and fibres settle together in the same suspension. To calculate such a sedimentation process with all the particle interactions one needs a suitable mathematical model, because direct numerical methods would take to much time. Therefore, in this paper we will reformulate the model of the Stokesian Dynamics Method. We will focus on the underlying assumptions and their effects and show that our simulations of some particles are in good agreement to the literature. Due to the validity of this method for spheres only, we approximate a rigid fibre as a chain of spheres and also give a comparison of this approximation. Finally we investigate the sedimentation behaviour of particle–fibre suspensions to show the influence of the density ratio between the fibres and particles and the influence of the length of the fibres on the separation behaviour.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".