Pressure loss in packed beds of multicomponent mixtures of flat particles with particle overlap, including random chips
Bibliographic record
Abstract
Abstract Pressure loss measurements are presented for packed beds of multi‐component mixtures of thin angular parallelepipeds and of random wood chips for a Reynolds number range of 50 to 500. For flat particles like these, the degree to which the particles overlap is an essential factor in pressure loss, and this was measured using two different methods, including a novel technique involving progressive dismantling and photography of the bed. The experimental friction factors were found to be well represented by the Nemec and Levec pressure loss correlation, an Ergun‐type equation with an explicit dependence of the parameters on particle sphericity, with the equation expanded to include the effects of particle overlap and of packing anomalies at the wall. The friction losses of the mixtures were found to be somewhat higher than those of the individual component particles, requiring a minor change in the correlation parameters. Estimates of the tortuosity of the bed channels showed that the greater losses of the mixtures correspond to an increase in tortuosity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".