Deformation Calculating of Electromagnetic Launcher’s Rail Subjected to Logarithmic Magnetic Pressure
Bibliographic record
Abstract
To solve the accurate calculation of force-deformation of the electromagnetic launcher’s rail , this is helpful to extend the rail life and improve the firing accuracy. Therefore, the electromagnetic launcher’s rail can be modeled as a beam on elastic foundation with simply supported beam by moving load. In this paper Euler beam theory is applied to build the Mechanical model and the analytical solution of the equation subjected to logarithmic magnetic pressure is derived in detail, which has successfully avoided the errors which are caused by using the uniform pressure to approximately replace the variable force. The numerical analysis brings from the elastic coefficient, the damping coefficient, the mass of rail and the load’s velocity have influence on the deformation of beam by the MATLAB software. The consequence shows that the elastic coefficient and the load’s velocity have quite obvious affect on the deformation of the beam while the damping coefficient and the mass of rail have not obvious affect on the deformation of the beam. It laid the foundation for solve the electromagnetic launcher’s rail subjected to magnetic pressure of arbitrary function and promote the practicality of the electromagnetic guns.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".