Vibration-induced dynamics of granular materials in the vibro-compaction of carbon anodes
Bibliographic record
Abstract
Vibro-compaction is a key stage in carbon anode production for aluminum manufacturing, where vertical vibration is used to rearrange particles, increase density, and enhance homogeneity. To simulate this process, we experimentally investigate the flow dynamics of representative dry and wet granular materials in a transparent container subjected to vertical vibration. Mixtures of calcined petroleum coke particles and glycerin simulate the behavior of anode paste. Using ultra-high-speed imaging, we analyze the effects of particle size, concentration, and vibration amplitude on flowability, characterized by bulk average height, angle of repose, frequency ratio, and phase lag. Our findings reveal a non-monotonic effect of particle concentration on the average height and angle of repose, with a peak at 85% concentration. In addition, increasing particle concentration decreases energy transfer efficiency between the vibration table and the material, resulting in complex subharmonic responses with significant deviations from sinusoidal behavior, particularly at higher vibration amplitudes. These subharmonic responses are visualized as so-called multi-loop Lissajous patterns, which describe the motion of the bulk material relative to the vibration table. Our findings enhance understanding of vibro-compaction and have implications for other vibration-based industrial processes, including pharmaceuticals, food processing, sediment transport, and powder metallurgy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".