Influence of inter‐particle collisions and agglomeration on cyclone performance and collection efficiency
Bibliographic record
Abstract
Abstract For further improving the understanding of particle separation in gas cyclones including the effect of agglomeration, numerical calculations based on the coupled Euler/Lagrange approach were conducted using an open source CFD code. The gas phase was simulated by LES (large eddy simulations) combined with a dynamic Smagorinsky sub‐grid‐scale (SGS) model for resolving the highly anisotropic turbulence structure. Two‐way coupling (i.e., the influence of the particles on the fluid flow field) was accounted for in the momentum equations as well as in the SGS turbulence. Solid particle agglomeration was modelled on the basis of the stochastic inter‐particle collision model.1 In that respect, it is also important to consider the effect of particle dispersion by SGS turbulence and wall roughness in particle‐wall collisions,2 which eventually will also modify inter‐particle collisions and agglomeration. For describing the agglomeration phenomenon, two different approaches were tested. The first one, the so‐called sphere model, considers that the agglomerate diameter is calculated from the sum of the volume of the involved primary particles (volume equivalent diameter). In the second approach, the agglomeration history model3 allows the calculation of the agglomerate porosity, which is used to calculate a more suitable hydrodynamic diameter and, therefore, allows a better prediction of the motion of newly formed agglomerates. The effect of inter‐particle collision and agglomeration on the performance of a cyclone was analyzed in a hypothetical study considering a high efficiency Stairmand cyclone.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".