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Record W2558554427 · doi:10.1115/pvp2016-63987

Development and Application of Oxide Wedging Crack Growth Model for CANDU Zr-2.5Nb Pressure Tubes

2016· article· en· W2558554427 on OpenAlexaff
Douglas A. Scarth, Preeti Doddihal, Monique Ip

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsBruce Power (Canada)Kinectrics (Canada)
Fundersnot available
KeywordsMaterials scienceTube (container)CoolantOxideZirconium alloyMetallurgyComposite materialStructural engineeringZirconiumNuclear engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Surface breaking and subsurface manufacturing flaws have been detected by in-service ultrasonic examination in a number of CANDU reactor Zr-2.5Nb pressure tubes. The manufacturing flaws are oriented in the circumferential direction in the Zr-2.5Nb pressure tube, and are axially aligned along the pressure tube. Metallographic examination of these flaws in an ex-service Zr-2.5Nb pressure tube revealed a series of parallel circumferentially oriented discontinuous features oriented at an angle of nominally 40 degrees relative to the axial direction in the pressure tube. Since the surface breaking flaws are exposed to the reactor coolant, it was considered prudent to evaluate potential growth of the flaws by an oxide wedging crack growth mechanism. Oxide wedging crack growth is a slow crack growth mechanism that can occur when zirconium oxide forms on the crack faces due to a corrosion reaction with the reactor coolant. An oxide wedging crack growth model was developed to predict crack growth rates and future flaw sizes as a part of the fitness-for-service evaluation of a Zr-2.5Nb pressure tube containing this type of manufacturing flaw. The model was then applied to predict crack growth from manufacturing flaws that were detected in an operating pressure tube, and the evaluation results were used as part of the justification for continued operation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.164

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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