Changes in Pore Structures of Porous Beds When Subjected to Vertical Vibration
Bibliographic record
Abstract
The objective of this study was to investigate the effect of vibration on critical pore structure parameters related to flow in porous beds. The discrete element method was used to simulate particle packing in porous beds of soybean subjected to vibration. The porous bed was simulated as an assembly of spherical particles with diameters randomly distributed between 5.5 mm and 7.5 mm. The simulated porous bed was subjected to vertical vibration at a fixed frequency of 15 Hz and multiple amplitudes from 0.5 to 4.0 mm, resulting in vibration intensities from 0.45g to 3.62g (g = gravitational acceleration). The location (coordinates) of each particle was tracked during vibration. Based on the simulated spatial arrangement of particles, critical flow-related parameters of the porous bed, including porosity, tortuosity, and pore throat width were calculated. It was found that vibration intensity of 1.81g resulted in the lowest porosity, whereas lower vibration intensity did not have enough energy to densify the bed and higher intensity produced less dense parking due to over-excitement. Local porosity fluctuated markedly during vibration, with a general trend of decrease as vibration progressed. Vibration noticeably affected the shape (tortuousness) of flow path. Tortuosity of the porous bed before vibration was higher (2 % to 9 %) than that after vibration. Vibration reduced the pore throat width by 18 % on average (from 3.3 mm before vibration to an average of 2.7 mm after vibration).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".