Could laser-based profile measurements be used in wheel/rail contact and vehicle dynamics simulations?
Bibliographic record
Abstract
Contact-based profilometers have been the de facto standard in obtaining wheel and rail profiles for the purposes of vehicle-track and wheel-rail interaction studies. Still, they cannot collect profiles in large quantities. This paper explores the potential of laser-based profilometers for wheel and rail profile measurements in vehicle-track dynamic simulations. Comparisons between contact and laser-based profilometers were performed using sample rail profiles measured in the lab. Through contact simulations, contact preprocessing and multi-body dynamics simulations (MBD), differences in contact pressures, forces, and Y/Q were analyzed. The importance of data processing and profile smoothing is also discussed. Axle sum Y/Q showed the smallest differences between the two sets of profiles, with more than 80% of the MBD simulations having less than 10% difference. While far from perfect, the ability of wayside and vehicle-mounted laser profilometers can enable analyses to be performed at a much larger scale. The authors hope this work will lead to an open-minded approach to continue evaluating laser-based profiles in simulations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".