Characterizing tortuous airflow paths in a grain bulk using smoke visualization.
Bibliographic record
Abstract
An experiment was carried out to determine tortuosity and local velocity of airflow through a grain bulk (soybeans) by analyzing digital images of smoke-visualized airflow paths. A 27 × 25 × 2 cm rectangular box made of Plexiglas was used to simulate airflow in grain bins. Coloured smoke was introduced into the test box for flow visualization. Digital images recorded with a high speed camera were analyzed to track the smoke movement frame by frame, based on which airflow paths were constructed and tortuosity and airflow velocities were then determined. Three airflow rates (0.25, 0.45, and 0.60 L/s) and two bulk densities (632 and 755 kg/m3) were tested. The results showed that the tortuosity increased with both bulk density and airflow rate. This meant that tortuosity was not only a geometry-based property varying with the pore structure, but also dependent on flow rate. Tortuosity of the fastest paths was close to that of the shortest path at a lower flow rate. However, at the high flow rate, the tortuosity of the fastest path was close to that of the longest path. This implied that the shortest path was not always the fastest path inside a grain bulk.
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 | 0.001 | 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) | 0.001 | 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".