The exergetic and environmental impact assessment of underground electric train braking
Bibliographic record
Abstract
Within the present industrial society, some of the greatest challenges of humanity are related to achieving a sustainable industrial metabolism, which integrates technical activity and ecological systems. Electric traction drive systems using induction motors fed by variable voltage variable frequency (VVVF) inverters have provided high performance for urban electric trains. Moreover, power converter technology based on advanced techniques in control electronics and efficient anti-slip systems allows optimum traction characteristics and minimum energy consumption. For underground electric trains, however, it is also important to assess the environmental impact of braking. From the viewpoint of exergy and environment, the braking regime, particularly electric braking, is a special aspect of non-autonomous vehicles using electric traction. As electric drive systems are used with VVVF inverters and traction induction motors, these machines with appropriate controls can realize both traction and electric braking regimes for electric traction vehicles. Concerns regarding mechanical braking are associated with unrecovered energy and material utilization. Also, for underground electric trains during mechanical braking, the abnormal but frequent situation involving the unequal charge of the traction induction motors is a concern. These aspects of underground electric trains are analysed in this article so as to assist in improving performance.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".