Evaluation of a Magnetic Dipole Model in a DC Magnetic Flux Leakage System
Bibliographic record
Abstract
One of the most common methods for performing non-destructive testing in steel tank floors is DC magnetic flux leakage (MFL). The magnetic dipole method is the most widely used mathematical technique to predict the MFL from defects in such structures. However, due to the complexity of an exact analytical description of an MFL system, researchers often make coarse approximations for the profile of the magnetic surface charge density σm, orientation of the magnetic field H, and variation of relative permeability μr. In this paper, the validity of these approximations is evaluated for 2-D rectangular defects in a steel plate, by comparing model predications with finite element results. The primary sources of deviation between the approximate solutions and true MFL profiles were found to be caused by assumptions that 1) σmon the specimen surface adjacent to a flaw is zero. This assumption is equivalent to treating the orientation of H to be parallel to the specimen surface, even at locations in close proximity to a flaw and 2) local variation in permeability around the defect can be ignored. This approximation was found to cause an underestimation of σmand, consequently, the predicted MFL. In contrast, approximating σmto be zero at the bottom of a flaw, and approximating uniform distribution for σmon the vertical defect sides of a slot defect was found to generate only minor errors in an estimate of flux leakage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".