Effective drag on spheres immersed in a fluidized bed at minimum fluidization—Influence of bulk solids properties
Bibliographic record
Abstract
Abstract The aims of this work are to elucidate the effects that bulk solids properties have on the effective drag experienced by large spheres immersed in an emulsion of group‐B solids under minimum fluidization conditions and to analyze the ways in which the different suspensions react towards different applied shear rates. To investigate this, magnetic particle tracking was applied to resolve the trajectory of falling‐sphere measurements in which the size, density, and sphericity of the bulk solids were varied as well as the size and density of the spherical tracers. The resulting experimental scope included both rising and sinking tracers as well as full segregation and in‐bed stagnation of the tracers. The set‐up provided highly resolved tracer trajectories, from which the drag experienced by the sphere can be calculated. For sinking tracers, the results showed that an increase in bulk solids size, angularity, and density reduced the terminal velocity of the sphere. This effect correlated well with the bed expansion and Hausner ratio, indicating that a reduced void space among the bulk solids is the main reason for the increase in motion resistance. At lower shear rates, namely, during the de‐acceleration towards the stagnant state, beds of larger, more angular, or denser bulk solids yield lower levels of shear stress. The angle of repose of the bulk solids correlated with the rate at which the emulsion thins with increasing shear rate. For rising tracers, shear stress did not show any significant dependency on the properties of the bulk solids.
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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) | 0.001 | 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".