Predicted Wheel Wear and RCF Performance Using VAMPIRE Automation Routines
Bibliographic record
Abstract
Predicted wheel performance is an invaluable tool in developing new components such as wheel profiles and truck components and also in understanding and controlling wheel and rail wear and rolling contact fatigue (RCF). This paper outlines a vehicle dynamics trial using VAMPIRE Pro to compare the predicted wear and RCF performance of the WRISA2 wheel profile developed by the National Research Council of Canada (NRC) and the United Kingdom P8 wheel profile using measured wheel profiles from an in-service trial. WRISA2 and P8 profiles were fitted to two passenger trains running in normal service. Wheel profiles were measured every 10,000 miles. These measured profiles were used to predict wear and RCF damage for each wheel of the investigated rail vehicle, using a combination of VAMPIRE transient analysis and another program called the “Whole Life Rail Model” (WLRM). This process was repeated up to 190,000 miles run in service, allowing a clear comparison of the changing rail wear and RCF performance of the two profiles up to this mileage. This process was automated using new features within VAMPIRE that allow communication to 3rd party computer programs including the WLRM, Microsoft Excel, and Microsoft Visual Basic. This research process presents itself to be a very useful tool in predicting wheel wear performance for any number of new wheel and truck components.
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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".