Selected CANDU Pressure Tube Degradation Mechanisms and Inspection Related Issues
Bibliographic record
Abstract
Ontario Power Generation; Inspection & Maintenance Services Division (IMS) has been operating a variety of inspection systems to perfor m volumetric and surface non-destructive examinations of the CANDU reactor pressure tubes using ultrasonics. Experience acquired over more than 20 years allows for relatively reliable d etection, sizing and characterization of various flaws and artifacts present in pressure tubes. The purpose of this paper is to discuss inspection related issues (detection, sizing and characterizat ion) related to selected pressure tube degradation mechanisms. The discussion will include degradation phenomena common to all CANDU PHWR plants (multi and single unit stations, AECL and Ontario Hydro designs) and phenomena specific to a certain plant configuration or power unit. There are several ways to categorize pressure tube flaws. The categorization can be based on source of flaw (manufacturing, installation or in-s ervice), flaw location (ID, OD or material) and flaw severity. There is also a variety of flaws wi thin the broadly defined categories. The proper categorization and characterization of detected fla ws is extremely important for the engineering assessment as some categories of flaws can be asses sed as blunt whereas others have to be assessed as sharp. This may have a serious impact on reactor's operating restrictions. Thanks to the large number of tubes inspected over the years, OPG-IMS has collected an extensive library of information with respect to the pressure tube flaws. Many of the flaws have been also replicated providing additional info rmation which complements or validates NDE data. This data is now available for both qualitat ive and quantitative assessments and comparisons.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".