Numerical study on porosity distribution and analysis of ignition instability in packed beds of spheres
Bibliographic record
Abstract
Abstract Porosity distribution is an important factor that affects premixed flame stability in packed beds. In this paper, the discrete element method is used to simulate the sphere packing in cylindrical, conical, and pyramidal packed beds. The anisotropic characteristics of porosity and ignition temperature distributions in packed beds are calculated and analyzed. The results show that the distance of the oscillation period of the packed bed filled with spheres of the same diameter is approximately equal to the sphere diameter. The oscillation amplitude of radial and axial porosity curves can be reduced by filling with mixed spheres of different diameters in cylindrical packed beds. The influence factors on the oscillation period distance of the radial and axial porosity curves include the diameter difference of the spheres and the mixing ratio of the number of spheres. In the cylindrical double‐layer packed beds, the interfacial porosity can be decreased by the increase of the diameter difference among the spheres. Increasing the divergent angle of the conical packed beds can weaken the influence of wall effect on the radial and axial porosities, which makes the uniformity of the porosity in the conical packed beds better than that of the cylindrical and the pyramidal beds. The inhomogeneity of tangential porosity distribution in the packed beds is inherent. In the packed beds, the ignition temperature of premixed gas is negatively correlated with porosity, sphere diameter, equivalent ratio of mixed gas, and divergent angle of conical packed beds, and it is positively correlated with superficial velocity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".