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Record W2765484516 · doi:10.1115/pvp2017-66116

Exploratory Analysis to Estimate Axial Fracture Toughness for Zr-2.5Nb Pressure Tubes Using Test Data From Small Curved Compact Specimens

2017· article· en· W2765484516 on OpenAlexaffabout
Steven X. Xu, Kim Wallin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsFracture toughnessMaterials sciencePressure vesselComposite materialTube (container)ToughnessFracture (geology)Structural engineeringEngineering

Abstract

fetched live from OpenAlex

Zr-2.5Nb pressure tubes are in-core, primary coolant containment of CANDU(1) nuclear reactors. Technical requirements for in-service evaluation of pressure tubes are provided in the Canadian Standards Associate (CSA) N285.8. These requirements include the evaluation of service conditions for protection against fracture of operating pressure tubes and demonstration of leak-before-break. Axial fracture toughness for pressure tubes is a key input in the evaluation of fracture protection and leak-before-break. The 2015 Edition of CSA N285.8 provides a pressure tube axial fracture toughness prediction model that is applicable to pressure tubes late life conditions. The fracture toughness prediction model in CSA N285.8-15 is based on rising pressure burst tests performed on pressure tube sections with axial cracks under simulated pressure tube late life conditions. Due to the associated high cost of testing and high consumption of pressure tube material, it is not practical to perform a large number of fracture toughness burst tests. On the other hand, more fracture toughness data is required to improve the existing pressure tube axial fracture toughness prediction model. There is strong motivation to estimate pressure tube axial fracture toughness using test data from small specimens. The estimated pressure tube fracture toughness using test data from small specimens can fill the gaps in the burst test toughness data, as well as provide information on material variability and data scatter. Against this background, an exploratory analysis of estimating pressure tube axial fracture toughness using test data from small curved compact specimens has been performed and is described in this paper. The estimated values of pressure tube axial fracture toughness using the test data from small curved compact specimens are compared with the measured toughness from burst tests of pressure tube sections with axial cracks to check the feasibility of this approach.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
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 score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.155
GPT teacher head0.353
Teacher spread0.198 · 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.

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
Published2017
Admission routes2
Has abstractyes

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