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Record W2558926356 · doi:10.1115/pvp2016-64024

Validation of Weight Function Based Process-Zone Model for Evaluation of Delayed Hydride Cracking Initiation in Zr-2.5Nb Pressure Tubes

2016· article· en· W2558926356 on OpenAlexafffundabout
Steven X. Xu, Dennis Kawa, Jun Cui, Heather Chaput

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear LaboratoriesKinectrics (Canada)
FundersCANDU Owners Group
KeywordsMaterials scienceCrackingPressure vesselMetallurgyFrettingHydrogenComposite material

Abstract

fetched live from OpenAlex

In-service flaws in cold-worked Zr-2.5 Nb pressure tubes in CANDU(1) reactors are susceptible to a phenomenon known as delayed hydride cracking (DHC). The material is susceptible to DHC when there is diffusion of hydrogen atoms to a service-induced flaw, precipitation of hydrides on appropriately oriented crystallographic planes in the zirconium alloy matrix material, and development of a hydrided region at the flaw tip. The hydrided region could then fracture to the extent that a crack forms and DHC is said to have initiated. Examples of in-service flaws are fuel bundle scratches, crevice corrosion marks, fuel bundle bearing pad fretting flaws, and debris fretting flaws. These flaws are volumetric in nature. Evaluation of DHC initiation from the flaw is a requirement of Canadian Standards Association (CSA) Standard N285.8. This paper describes the validation of the weight function based process-zone model for evaluation of pressure tube flaws for DHC initiation. Validation was performed by comparing the predicted threshold load levels for DHC initiation with the results from DHC initiation experiments on small notched specimens. The notches in the specimens simulate axial in-service flaws in the pressure tube. The validation was performed for both un-irradiated and pre-irradiated pressure tube material.

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)0.002
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 score0.258
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.285
Teacher spread0.240 · 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 routes3
Has abstractyes

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