MétaCan
Menu
Back to cohort
Record W2918574956 · doi:10.1109/tmag.2019.2897669

Evaluation of a Magnetic Dipole Model in a DC Magnetic Flux Leakage System

2019· article· en· W2918574956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Magnetics · 2019
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDipoleOrientation (vector space)Magnetic fieldMagnetic flux leakageMagnetic dipolePermeability (electromagnetism)Magnetic fluxRelative permeabilityAlgorithmPhysicsMaterials scienceNuclear magnetic resonanceComputer scienceMathematical analysisGeometryMathematicsChemistryComposite materialQuantum mechanics

Abstract

fetched live from OpenAlex

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 σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> , orientation of the magnetic field H, and variation of relative permeability μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> . 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) σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> on 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 σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> and, consequently, the predicted MFL. In contrast, approximating σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> to be zero at the bottom of a flaw, and approximating uniform distribution for σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> on the vertical defect sides of a slot defect was found to generate only minor errors in an estimate of flux leakage.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.243
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it