Two-dimensional finite element model for a long rectangular eddy current surface coil
Bibliographic record
Abstract
An eddy current model for predicting the electrical impedance variation of a long rectangular surface coil due to a surface breaking defect in layered conductors is reported. The method is based on transforming a three-dimensional eddy current problem into the spatial frequency domain and solving the transformed diffusion problem for a limited number of the spatial spectrum components. The transformed problem has been assumed to be quasitwo dimensional (2D) for a long rectangular surface coil and formulated in terms of a two-component transformed vector magnetic potential Ā and scalar electric potential φ̄. The 2D problems have been solved with the finite element method using triangle elements. The system of algebraic equations was obtained using the Galerkin weak formulation and solved with the Gaussian elimination method. A high accuracy solution with ten spatial frequency components takes around 4 min on a Pentium 200 MHz PC. The accuracy of the solution has been tested experimentally at 200 kHz on coated stainless steel samples using a rectangular surface coil with the length-to-width ratio around 6. Agreement between theory and experiment is excellent. Discrepancies between the theory and experiment are within 15% and typically less. The method is useful for eddy current nondestructive evaluation and modeling, and can be also applied for the case of buried defects and for general multilayer coatings’ problem.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".