Aerodynamic prediction tools for high-speed trains
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
With high-speed trains, the need for efficient and accurate aerodynamic prediction tools increases, since the influence of the aerodynamics on the overall train performance raises. New requirements on slipstream velocities and head pressure pulse in the revised Technical Specification for Interoperability (TSI) for train speeds higher than 190 km/h are more challenging to fulfil for wide-body trains, like the Green train concept vehicle Regina 250, as well as higher trains, like double-deck trains. In this paper, we give an overview of the results from a project within the Green train programme, where the objective was to increase the knowledge on slipstream air flow of wide body trains at high speeds, to understand the implications of the new requirements on the front shape and to develop a prediction methodology in order to take this into account early in the design cycle. In addition, the front design was in parallel optimized with respect to head pressure pulse and drag.
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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 | 0.000 | 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