Modeling the temperature evolution of electromagnetic rails under multiphysics coupling
Bibliographic record
Abstract
A three-dimensional transient electro–magneto–thermo–mechanical coupled model was developed to investigate the thermal response of the rail during launch, incorporating contact pressure, contact resistance, and friction at the armature–rail interface. The results are as follows: (1) In spatial distribution, the rail temperature rises first and then falls along the direction of armature movement, with the peak located in the low- to medium-velocity stage. (2) In spatial heat evolution, the temperature transitions from a rapid increase to a plateau stage and then to a gradual decrease. (3) As the launch progresses, Joule heating from contact resistance dominates at the beginning, follow K to 887 K. The model incorporates material thermal softening. This effect, combined with electromagnetic force attenuation, leads to a progressive decrease in contact pressure. The reduced pressure, in turn, results in higher contact resistance and intensifies localized heating, which ultimately accelerates the overall rail temperature rise. These findings clarify the coupling between current and heating sources, providing theoretical insight and numerical support for thermal protection design and service life assessment of electromagn ed by frictional heating, contributing approximately 62% and 38% of the total heat input, respectively. The peak rail temperature rises from 293 etic launchers. • A 3D transient multiphysics model is developed to simulate rail temperature evolution. • The model couples electromagnetic, thermal, mechanical, and contact phenomena. • Spatiotemporal characteristics of heat sources vary across launch phases. • High-temperature zones tend to appear in low-to-medium velocity segments. • Local thermal stress gradients may induce surface fatigue and microcracks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".