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Record W1716704253 · doi:10.1520/stp14308s

Size, Geometry, and Material Effects in Fracture Toughness Testing of Irradiated Zr-2.5Nb Pressure Tube Material

2000· book-chapter· en· W1716704253 on OpenAlexaff
PH Davies, RSW Shewfelt

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsAtomic Energy (Canada)Canadian Nuclear Laboratories
Fundersnot available
KeywordsMaterials scienceFracture toughnessComposite materialTube (container)

Abstract

fetched live from OpenAlex

The effect of initial crack size on the crack growth resistance (J-R) curves determined from burst tests on irradiated Zr-2.5Nb pressure tubes has been studied and the results compared with those obtained from matched curved compact specimens. The study used sections from three different tubes representative of material of low, intermediate, and high toughness. In each case a series of burst tests was conducted at 250°C with different starting crack sizes (from 35 to 85 mm) followed by small specimen testing. The toughness was characterized by means of deformation J-R curves using the d-c potential drop method to measure stable crack growth. Fractographic studies were also conducted in support of the J-R curve results. For tubes of low to intermediate toughness there is little evidence of a crack size effect on the J-R curves from the burst tests. However, for burst tests on tubes of higher toughness there is an increase in out-of-plane bending associated with the wider crack openings (bulging) that can promote earlier failure by slant/shear instability and a lowering of the J-R curve. Thus the J-R curves from such tubes exhibit more variability in toughness as well as an apparent crack size dependence. Such crack growth behavior overrides any increase in material toughness with irradiation temperature as revealed by the small specimens.

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 categoriesMeta-epidemiology (narrow), 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 score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0310.000

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 head0.011
GPT teacher head0.205
Teacher spread0.193 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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