Fluid viscosity effects on carbon anode vibro-compaction
Bibliographic record
Abstract
Carbon anodes, commonly made by vibro-compaction of anode paste, are an important part of the electrolytic reaction in aluminum production. The overall behavior of anode paste during vibro-compaction is greatly influenced by the properties of coke aggregates and the rheological characteristics of pitch, particularly its viscosity. To simulate this process and investigate the effect of fluid viscosity, we use mixtures of coke particles (80% by volume) combined with a fluid (20% by volume) consisting of glycerin and water. The glycerin-to-fluid volume fraction is varied across four ratios: 70%, 80%, 90%, and 100%. The reason for using a mixture of glycerin and water as a representative fluid is that its viscosity at room temperature resembles the viscosity of the binder matrix (a mixture of coal tar pitch and fine coke particles) at high temperatures in the vibra-compaction stage, as determined through dimensional analysis. Using ultra-high-speed imaging at 3,000 frames per second, we track the representative materials’ dynamics when poured into a transparent vessel and subjected to vertical vibration at 60 Hz frequency and 0.4 mm amplitude. Then, we analyze the effect of glycerine-to-water volume fraction on the bulk average height and height uniformity. Our results show that the vibration activates the void-filling mechanism, minimizes the voids between the particles, and compacts the bulk material through time. In addition, the compaction rate and final degree of uniformity are affected by the glycerin-to-fluid volume fraction. Specifically, mixtures with a lower glycerin-to-fluid volume fraction, indicating a lower viscosity, showed faster compaction and achieved a more uniform final surface profile. The reason is that the lower viscosity of water compared to glycerin reduces interparticle forces and facilitates particle rearrangement during vibration. Our findings contribute to a deeper understanding of vibro-compaction and have potential implications for various vibration-based industrial processes, including those in the pharmaceutical, food processing, and sediment transport. Furthermore, the experimental setup and image analysis techniques developed in this study can be used to investigate the effect of other parameters, such as particle size and vibration frequency, on the vibration-induced dynamics of granular materials.
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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 | 0.000 |
| 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".