Application of discrete element method simulation for studying fluidization of nanoparticle agglomerates
Bibliographic record
Abstract
Abstract The usefulness of discrete element method simulation for studying fluidization of nanoparticle agglomerates is explored. Nanoparticle agglomerates were simulated by using solid particles of equivalent sizes and densities. Validity of the present simulation was assessed through comparisons of simulation results and experimental observations of bed expansion, characteristic fluidization behaviour, and dense‐bed settling. The simulation was then used to investigate initial bed expansion and bed uniformity under particulate fluidization conditions. The role of inter‐agglomerate interparticle force in fluidization of nanoparticle agglomerates was examined. A stability analysis originally developed for addressing the transition from particulate to bubbling fluidization for conventional particles was used for predicting the start of bubbling in fluidized beds of nanoparticle agglomerates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".