Analytical Calculation of the Magnetic Vector Potential of an Axisymmetric Solenoid in the Presence of Iron Parts
Bibliographic record
Abstract
This paper presents an analytical calculation method for the computation of the magnetic vector potential of an axisymmetric solenoid in the presence of an iron shield and a ferromagnetic core. The proposed method can be used as a fast analytical computation technique for accelerating the design optimization process of the solenoid systems. In this paper, the analysis of the current carrying coil in the presence of the ferromagnetic materials is treated as a boundary value problem. The solution approach is based on partitioning the solution domain into distinct regions. The general form of the solution to Maxwell's equation in each region along with the corresponding boundary conditions is obtained using the Fourier analysis and the separation of variables. The final solution to the boundary value problem is constituted by considering the continuity of the magnetic vector potential, as well as the magnetic field, on the interfaces between the regions. Finally, the proposed analytical computation method is applied on an electromagnetic actuator. The magnetic vector potential is computed over the entire solution domain, and the result is compared with that of the finite-element method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".