Electromagnetic optimal design of a linear induction motor in linear metro
Bibliographic record
Abstract
An improved T-model equivalent circuit of a single-sided linear induction machine (SLIM) is proposed. The analysis utilizes a set of one-dimensional air gap flux linkage equations. The model takes longitudinal end and transversal edge effects into consideration. These have to account for primary terminal half-filled slots, secondary back-iron saturation and skin effect in the secondary conducting sheet. In the circuit, several coefficients are obtained by use of the dummy electric potential method in conjunction with consideration of the complex power equivalence between the primary and secondary sides. The coefficients derived include the longitudinal end effect coefficients K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , transversal edge effect coefficients C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , and skin effect coefficient K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> . The accuracy of the T-model is validated using comparison to a set of measured data under constant current - constant frequency conditions. These were taken from the Intermediate Capacity Transit System (ICTS) in Canada. An optimal design scheme for the SLIM is addressed. The application used for the optimization is a prototype propulsion system in a high temperature superconducting (HTS) maglev drive. The efficiency and primary weight are chosen as optimal objective functions while the thrust, power factor and other performance indexes are calculated.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".