Current mode performance of a traction linear induction motor driven from the voltage converter
Bibliographic record
Abstract
Background: The paper deals with the modelling of a traction Linear Induction Motor (LIM) for public transportation. Typical problems arising from the electromagnetic finite element model development are described. The end effect causes asymmetry of phase impedances of the LIM. Because of that, if the LIM is supplied from the voltage inverter, which is usually the case, the phase currents become asymmetric. This causes performance calculation discrepancies in models that assume phase current symmetry. Aim: The aim of the paper is to develop a method for calculating the imbalanced three-phase LIM currents to precisely predict the LIM performance. Methods: Here, a method is developed to calculate the LIM phase current asymmetry by means of a self-developed electromagnetic finite element program – ELMAG, capable of adapting mesh generation based on Reynolds, Péclet and skin-depth numbers. Results: The calculated asymmetric currents are used in a real size traction LIM calculation in COMSOL, to derive the performance characteristics for comparison with the results achieved when supplying the LLIM with the symmetric three phase current. Conclusion: These results show that the natural asymmetry of the currents is an important factor that must be considered in appropriately calculating the LIM 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".