Mutual inductance and magnetic force calculations between thick bitter circular coil of rectangular cross section with inverse radial current and filamentary circular coil with constant azimuthal current
Bibliographic record
Abstract
In many engineering applications, the coils of different geometrical shapes are used. Usually, these coils (circular, right etc.) are with the constant currents in different directions. In the literature, there are many papers on the calculations of the magnetic fields of the circular coils with the constant azimuthal currents or the calculations of the mutual inductance and the magnetic force between them. In some applications, where the high intensity magnetic fields are required the circular metal plates and insulating spacers are used with the inverse radial current. Such configurations form an electromagnet named after its inventor Bitter. In this study, the authors calculate the mutual inductance and the magnetic force between the thick Bitter coil of rectangular cross‐section with the inverse radial current and the circular filamentary coil with the constant azimuthal current. The semi‐analytical and the analytical expressions of these quantities are obtained over complete elliptic integrals of the first and second kind as well as Heuman's lambda function. There is one simple integral which has to be solved numerically. The results of this method are compared by those obtained by the modified filament method for the presented configuration. All results are in an excellent agreement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".