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Record W2060430643 · doi:10.1115/pvp2008-61525

Some Issues in Fitness for Service Assessment of Wall Thinned CANDU Feeder Pipes

2008· article· en· W2060430643 on OpenAlexaffabout
John C. Jin, Seyun Eom, Raoul Awad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsCanadian Nuclear Safety Commission
Fundersnot available
KeywordsReliability engineeringService (business)ConservatismService lifeStress (linguistics)Computer scienceStructural engineeringEngineeringEnvironmental scienceBusiness

Abstract

fetched live from OpenAlex

Canadian CANDU® feeder pipes experiencing pipe wall thinning due to flow accelerated corrosion (FAC) are accepted for continued service after an engineering evaluation. This evaluation is based on the assumption that FAC degradation is manageable through a comprehensive inspection program and conservative engineering evaluations. The practice of the Canadian nuclear industry is to: establish a minimum acceptable wall thickness, compare the measured thickness to predictions from the previous outage to confirm the conservatism of the predictions in a condition assessment, and predict the thickness at the next inspection and compare against the minimum acceptable value in an operational assessment. If the thickness measured during outage does not meet the pre-established thickness criteria, the feeder should be replaced, unless it is demonstrated to be fit for service through a detailed analysis. The detailed analysis usually involves more complex methodologies which are subjected to regulatory reviews. Several issues have been raised in the fitness-for-service assessments of feeder pipes relating to the definition of primary membrane stress, interpretation of minimum thickness requirements, plasticity analysis, limit load analysis and the applicability of procedures given in Code Case N-597 to Class 1 feeder pipes. This paper presents the Canadian regulatory expectations on these issues.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.296
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2008
Admission routes2
Has abstractyes

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