Analytical Forward and Inverse Modelling of Bobbin Steam Generator Inspection Probes
Bibliographic record
Abstract
The Steam Generators (SGs) of CANadian Deuterium Uranium (CANDU®) nuclear reactors require consistent inspections to ensure their safe operation. Eddy Current Testing (ECT) is the primary method by which the SG tubes are inspected. Many conditions in the SG tubes affect the Eddy Current (EC) response, such as fretting, pitting, cracking, as well as tube expansion, the tubesheet and support structures. When two or more of these parameters overlap, the EC signal from a flaw may become difficult to distinguish from background signal variations. Multifrequency mixing can be used to separate certain features. However, this technique cannot completely filter out unwanted signals. In this work, analytical models that described the impedance of a bobbin coil 1) in a tube, 2) coaxial with a hole in a plate, and 3) with a plate encircling an arbitrary number of cylindrical conductors was developed. These models were validated against Finite Element Method (FEM) modelling and experiment. Models one and three, which are most relevant to SG inspection, were then used in a Gauss-Newton (GN) inversion algorithm to estimate physical parameters for given impedance values. The inversion of the first model, bobbin coil in a tube, performed well when experimental impedance values were used as inputs. In the inversion of the third model, impedances calculated by FEM with circumferential grooves, representing flaws, were used as inputs. When the algorithm encountered a groove on the tube’s inner diameter, the error output of the algorithm tended to increase, since these features had not been incorporated into the analytical model. When the groove was on the tube’s outer diameter, the estimates of the parameters tended to change. In both cases, the effect tended to be independent of the groove’s position with respect to the plate’s edge, indicating that the algorithm can separate multiparameter signals, and characterize flaws to a limited extent. Results demonstrate the potential of the GN algorithm to detect flaws under
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".