Finite Element Analysis and Electromagnetic Field Optimization of Linear Synchronous Motor in High-Speed Maglev Systems
Bibliographic record
Abstract
Amidst the evolving dynamics of modern economic life, there emerges an escalating demand for faster modes of travel.In response, advancements in the realm of high-speed maglev train technology, targeting speeds of up to 600 km/h, are being persistently pursued in China.Central to ensuring the stable operation of these high-speed trains is a thorough understanding of the inherent magnetic fields and their electromagnetic interactions during high-speed transits.In this context, the Long Stator Linear Synchronous Motor (LSM) of Tongji University's maglev prototype is investigated.Through an analytical lens, a simplified model of LSM was dissected using the energy method.The distribution patterns of air gap magnetic flux were then ascertained through Fourier transformation coupled with the equivalent current layer, culminating in the derivation of a theoretical equation for electromagnetic forces.A twodimensional finite element model subsequently shed light on the magnetic induction intensity distribution intricacies inherent to the long stator linear motor.Concurrently, potential end effects impinging on the motor's performance were explored.This comprehensive analysis further revealed the interplay between electromagnetic force, excitation current, and armature current.The observations encapsulated distinct magnetic field distribution patterns, nonlinear interdependencies between current and magnetic force, and pronounced saturation characteristics.Collectively, these findings furnish a robust theoretical scaffold for the simplification and optimization of electromagnetic forces in high-speed maglev systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".