Pulsed eddy current probe optimization for steel pipe wall thickness measurement
Bibliographic record
Abstract
Inspection of bulk wall loss, due to far side surface corrosion in ferromagnetic steel pipe, is a common requirement across multiple industries, including chemical processing, and oil and gas. In the nuclear industry, there is a requirement for inspection of tile holes and balance of plant inspections, where insulated pipe is present. Inspection is typically done using ultrasonic testing, which necessitates a coupling agent and removal of any insulation. Pulsed eddy current (PEC) technique does not require direct contact and has a larger spot size, thereby facilitating more rapid inspection without insulation removal. To further develop the potential of PEC to inspect under these conditions, a reflection type PEC probe was examined for inspection of thickness of steel plate. PEC data was taken for different plate thicknesses so that sensitivity to wall loss due to corrosion could be assessed. The PEC signal was analyzed by fitting the tail of the transient decay with an exponential curve, with curve fitting parameters correlated to wall thickness variation. An analytical model was developed and partially validated in order to examine a wider range of factors, such as probe dimensions, number of turns, and magnetic permeability and conductivity of the steel. The modelled data was also used to perform a sensitivity analysis on probe dimensions in order to realise the largest range of wall thickness with highest spatial resolution. A finite element method model of the reflection probe over steel plate was used to provide insights into the observation from the analytical model that outer pickup coil radius was the most significant optimization parameter.
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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 | 0.000 |
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 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".