Mechanism and control of hierarchical granule replacement in conveyor-driven filters
Bibliographic record
Abstract
Granular filters demonstrate promising dust removal capabilities. Hierarchical replacement strategies can mitigate depth-dependent clogging and sustaining performance of granular filters. This study utilizes the discrete element method (DEM) to investigate hierarchical granule replacement in conveyor-driven systems, focusing on the effect of conveyor velocity v and inclination θ, and outlet size D on the hierarchical replacement degree λ. Upstream granule replacement dominance (λ > 0) when both D and θ are relatively small, while downstream granule replacement dominance (λ < 0) when D and θ are comparatively large. v exhibits no discernible impact on the hierarchical replacement degree. The inclination angle θ modulates effective friction coefficient between the granules and conveyor belt to change the hierarchical replacement degree. Hierarchical granule replacement arises from two competing transport mechanisms: shear-driven upstream flow and arch-mediated downstream discharge. The upstream flow is mobilized by frictional shear stress. The downstream flow is intermittently blocked by the force arch, whose collapse triggers “avalanche” cascades, larger D destabilizes arches, accelerating downstream granules cascades. This work establishes a predictive framework for optimizing hierarchical replacement in granular dust-removal beds and informs the design of advanced granular material systems.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".