Advanced Modelling and Performance Evaluation of Hydrogen-Powered Heavy Haul Locomotive
Bibliographic record
Abstract
Abstract In recent years, there have been significant activities in the development of hybrid, battery electric and alternative fuel (e.g., LPG, LNG, CNG) locomotives. However, to date there is a limited number of publications on the usage of such modelling and simulation approaches for hydrogen-powered rail vehicles, and almost no publications on hydrogen-powered heavy haul locomotives. A conceptual heavy haul hydrogen-powered locomotive has been designed and studied with the application of advanced simulation techniques used in recent locomotive/train/track damage studies. The detailed locomotive model includes multibody subsystems for the mechanical system of the locomotive and a traction power system implemented in the Matlab/Simulink software package. The traction performance evaluation has been performed through the delivery of traction effort characteristics of the proposed locomotive through co-simulation between multibody software and Matlab/Simulink and the evaluation of locomotive traction performance in a train configuration where the developed hydrogen-powered locomotive has been placed in a head-end locomotive consist for hauling a heavy haul train. The paper presents a summary of the simulation results, and detailed discussion of the limitations that have been identified in the approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".