Validation of multi-body models for simulation in authorisation of rail vehicles
Why this work is in the frame
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Bibliographic record
Abstract
An application of multi-body simulations is to reduce the amount of vehicle on-track testing and present an opportunity for saving the time and costs of vehicle acceptance in regard to running characteristics. One of the objectives of the EU project DynoTRAIN was to define criteria and limits for vehicle model validation. The paper presents investigations carried out by comparing simulations with measurements from a testing campaign using a test train with 4 types of vehicles and a total of 10 force measuring wheelsets and accompanied with continuous measurement of track irregularities and rail profiles. The simulations were performed by using several vehicle models, built in different simulation tools by different partners. The results of the investigations and the criteria and limits proposed for the validation of multi-body vehicle models, intended for simulations of on-track tests, in the framework of railway vehicle authorisations are presented.
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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 | 0.002 | 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 it