Managing wheel/rail performance on Amtrak's Northeast corridor
Bibliographic record
Abstract
Amtrak's Northeast corridor (NEC), with 150 mph Acela passenger trains operating on tracks shared with much slower and heavier freight trains, places unique demands on the wheel/rail system. The traffic mix runs the gamut between slow moving heavy axle load trains operating at considerable under-balanced elevations and high speed trains running at up to 7 inches of cant deficiency over an alignment far more curved than any other high speed corridor around the world. This poses a particularly challenging maintenance environment in which to operate a high-speed service. Among its many efforts to facilitate higher speeds and more efficient services on passenger lines, the Federal Railroad Administration (FRA) has initiated a landmark project to help Amtrak engineer the wheel/rail interaction on the NEC for improved safety, ride quality and lower maintenance costs. This paper reports on the improved curved-rail profiles that have been designed to maximize the effectiveness of limited rail grinding resources during the 2002 grinding cycle. It reports on an alternate wheel profile that will soon be tested for its ability to reduce flange wear on the high-speed Acela vehicles and for its impact on stability, wheel climb, wear and contact fatigue. Beyond the important safety implications of this program, Amtrak is working to apply the findings to improve system reliability and to maximize the cost effectiveness of wheel and rail maintenance. The paper discusses maintenance management in general and reports on a wheel/rail management system being developed jointly between Amtrak and the National Research Council of Canada. This system will perform an integrated engineering analysis of an increasing number of data streams (e.g. measured rail and wheel profiles, track alignment and geometry measurements, and vehicle performance data) to provide guidance on wheel and rail maintenance policies and practices.
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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 | 0.001 | 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) | 0.007 | 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; both teacher heads agree on what is shown here.
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".