Bibliographic record
Abstract
Abstract Magnetic Flux Leakage (MFL) inspection tools are the most cost effective way to monitor corrosion on in-service oil and gas transmission lines. The MFL signal is used to derive defect depth and extent, from which calculations of Maximum Allowable Operating Pressure (MAOP) are made. Unfortunately MFL signals depend not only on defect geometry but also tool speed, tool configuration, pipe wall magnetic properties and stress. The combination of these makes accurate depth predictions difficult. Of these various factors, stress is the most complex and the least understood. Pipe wall operating stresses may exceed 70% of the yield strength, but much higher local stress levels are present around defects because of stress concentrations. Understanding how these stresses affect MFL signals is crucial to accurate defect depth predictions. We have conducted a number of experimental studies that investigate the effects of bulk, local and residual stresses on magnetic behaviour and MFL signals. MFL and Magnetic Barkhausen Noise (MBN) techniques were used to examine and characterize the magnetic behaviour samples in response to stress. In addition to experimental studies, we have conducted finite element analysis (FEA) to model the MFL signals from typical defects. Stress alters the magnetic behaviour of the pipe wall, making it anisotropic and also causing it to vary with position in the defect vicinity. The FEA models therefore are extremely complex, involving 3D modelling and incorporating anisotropic permeability that is spatially non-uniform. The results of experimental and FEA work will be presented and their relevance to MFL inspection discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".