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Record W1706040601 · doi:10.1520/stp14295s

Irradiation-Enhanced Deformation of Zr-2.5Nb Tubes at High Neutron Fluences

2000· book-chapter· en· W1706040601 on OpenAlexaff
A.R. Causey, RA Holt, N. Christodoulou, ETC Ho

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsHydro One (Canada)Atomic Energy (Canada)Canadian Nuclear Laboratories
Fundersnot available
KeywordsMaterials scienceNeutronIrradiationDeformation (meteorology)Neutron irradiationNuclear physicsRadiochemistryCrystallographyComposite materialChemistryPhysics

Abstract

fetched live from OpenAlex

The effects of neutron flux and temperature on irradiation creep of 10-mm- diameter Zr-2.5Nb tubes are being assessed in end-of-life experiments in the Osiris reactor in France. The tubes have crystallographic texture, grain shape, and dislocation densities that are similar to those of full-size CANDU reactor pressure tubes. The experiments are performed at nominal operating temperatures of 553 and 583 K in fast neutron fluxes up to 2.0 × 1018 n · m-2 · s-1 (E > 1 MeV). Transverse and axial strains are reported as functions of fluence for specimens internally pressurized to hoop stresses from 0 to 160 MPa and irradiated to fluences up to 1.5 × 1026 and 1.3 × 1026 n · m-2 at 553 and 583 K, respectively. The measured transverse and axial strain rates in these high-fluence experiments are shown to be in good agreement with a creep model relating crystallographic texture to the in-reactor deformation of CANDU pressure tubes. The anisotropy of irradiation creep does not change with fluence, but the temperature dependencies of creep and growth are changing; the activation temperature for creep is increasing, while the magnitude of the activation temperature for irradiation growth (a negative value) is becoming less negative with fluence.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.232
Threshold uncertainty score0.997

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)0.0520.004

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 head0.193
Teacher spread0.182 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2000
Admission routes1
Has abstractyes

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Same topicNuclear Materials and PropertiesFrench-language works237,207