Model based optimization of driver-pickup separation for eddy current measurement of gap
Bibliographic record
Abstract
The fuel channels in CANDU® (CANada Deuterium Uranium) nuclear reactors consist of a pressure tube (PT) contained within a larger diameter calandria tube (CT). The separation between the tubes, known as the PT-CT gap, ensures PT hydride blisters, which could lead to potential cracking of the PT, do not develop. Therefore, accurate measurements are required to confirm that contact between PT and CT is not imminent. Gap measurement uses an eddy current probe. However this probe is sensitive to lift-off variations, which can adversely affect estimated gap. A validated analytical flat plate model of eddy current response to gap was used to examine the effect of driver-pickup spacing on lift-off and response to gap at a frequency of 4 kHz, which is used for in-reactor measurements. This model was compared against, and shown to have good agreement with, a COMSOL® finite element method (FEM) model. The optimum coil separation, which included the constraint of coil size, was found to be 11 mm, resulting in a phase response between lift-off and response to change in gap of 66°. This work demonstrates the advantages of using analytical models for optimizing coil designs for measurement of parameters that may negatively influence the outcome of an inspection measurement.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".