Bibliographic record
Abstract
Train sanders are ubiquitous in remediating low wheel–rail adhesion. Sanders operate by taking sand stored in a hopper, pneumatically conveying it through a nozzle, then spraying it into the wheel–rail interface. In this research, the wheel–rail–sander system was simulated experimentally in a laboratory. The system was optically accessible, and Particle Tracking Velocimetry was used to observe the trajectory of sand particles approaching the nip. The percentage of sand conveyed through the nozzle that makes it to the wheel–rail nip – the deposition efficiency – was measured gravimetrically. The maximum efficiency was found to be 91% for 1.15 mm mean diameter silica sand with a proprietary coating, and the minimum efficiency was 59% for uncoated aluminum oxide with a 0.91 mm mean diameter, both at a simulated train speed of 18 km/h. Irregular particles were found to be less efficient when compared to spherical particles with a similar size and composition. Increasing the size of silica sand from 0.18 mm to 1.05 mm in diameter slightly decreased the sanding efficiency from 67% to 60%. There was no statistically significant dependence of the efficiency on the particle coefficient of restitution. The single parameter most closely correlated with the deposition efficiency is the expansion of the particle-laden jet downstream of the nozzle. Larger, round particles typically had the smallest jet expansion and the highest efficiency, whereas rough particles with large diameters were found to have a large jet expansion and lowest efficiency. Finally, the effect of train speed on deposition efficiency was studied. The deposition efficiency was found to be very low at low train speeds and asymptotically approaches a fixed value at higher speeds. A simple physical model is proposed that explains the low efficiency at low speeds.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".