Hydrodynamics of inhomogeneous agglomerates for a fractal dimension of 2.5 at intermediate <scp>R</scp>eynolds numbers: Effect of agglomerate sizes and <scp>R</scp>eynolds numbers
Bibliographic record
Abstract
Abstract In this study, the effect of agglomerate sizes for a fractal dimension (Df) of 2.5 on the hydrodynamics at intermediate Reynolds numbers (Re) of 1–120 was assessed. The results show that a core behaves like a solid sphere that exists in the central region inside the agglomerate. In addition, increasing the agglomerate diameter represents adding an extra permeable layer outside the agglomerate. For a larger Re or a smaller agglomerate diameter, the fluid can enter and penetrate through the agglomerate more easily, and the hydrodynamic characteristics of agglomerates deviate more from those of solid spheres. The effect of diameters on the velocity and pressure profiles becomes less significant with the increase in the diameter. Based on the simulated results, the drag ratio has an approximately linear relationship with Re, and its intercept has an exponential relationship with the dimensionless agglomerate diameter. Compared with homogeneous porous spheres, the drag ratio of the agglomerate is different. The effect of diameters on the drag ratio decreases as the diameter increases. It should be noted that the effect of radially varying permeability on inhomogeneous agglomerates should not be ignored and that the effect weakens as Re increases.
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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 | 0.000 | 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 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".