Modelling and simulation of the train brake system in low adhesion conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes the current version of the Low Adhesion Braking Dynamic Optimisation for Rolling Stock (LABRADOR) simulation tool that can predict the train brake system performance and support decision-making in the design and optimisation of the braking system including wheel slide protection, sanders and the blending and control of friction and dynamic brakes in low adhesion conditions. The model has been developed in MATLAB/Simulink and is intended to mimic the braking performance of both older and newer generations of multiple unit passenger trains. LABRADOR models have been initially validated by comparing simulation results for a single car train (Class 153) and two-car train (Class 158) in dry conditions with experimental tests, for tare and crush laden vehicles. This project is supported by RSSB and a technical steering group composed of railway braking experts, suppliers and train operators and manufacturers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it