Computational fluid dynamics–discrete element method simulation of locomotive sanders
Bibliographic record
Abstract
Locomotive sanders are used to optimize the traction between the train wheels and the railhead by spraying sand into the interface. It has been previously shown that a large fraction of sand sprayed by the sanders does not make it through the wheel–rail nip, leading to sand wastage and thereby increasing the cost and refilling effort. In this study, pneumatic conveying of sand through the wheel–rail nip is numerically modeled through coupled computational fluid dynamics and discrete element method simulations. The gas phase, discrete phase, and coupled two-phase flows are separately validated against the literature, and the parameters affecting the deposition of sand into the nip are analyzed to determine their impact on sander efficiency. The aerodynamics associated with the particle-laden jet play a critical role in optimizing the amount of sand going through the wheel–rail interface, with the particle velocities being directly correlated with the sander efficiency. Particle–geometry interactions (e.g. particle bouncing) are found to have a negligible effect on the deposition. In the absence of crosswinds, it is recommended to employ particles with a smaller Stokes number to enhance the sander efficiency. A larger airflow rate through the nozzle is also recommended. Crosswinds strongly and adversely affect sander efficiency. The effects of crosswinds can be mitigated by reducing the nip–nozzle distance and using coarser particles.
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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".