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Record W2038345382 · doi:10.1520/jai101126

Manufacturing Variability, Microstructure, and Deformation of Zr-2.5Nb Pressure Tubes

2007· article· en· W2038345382 on OpenAlexaffabout
Grant A. Bickel, M. Griffiths

Bibliographic record

VenueJournal of ASTM International · 2007
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMicrostructureMaterials scienceDeformation (meteorology)MetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract The time-dependent deformation behavior of pressure tubes in CANDU™ (CANada Deuterium Uranium) reactors is an important property that has to be predicted for reactor life management. Measurements accumulated over many years have shown that there is considerable variability in deformation rates between different tubes. The deformation behavior not only varies from tube to tube but also varies along the length of each tube; this axial variation in itself is unique to each tube. The deformation behavior is determined by the microstructure and this is a function of the manufacturing history. An exhaustive study has been conducted to collect and compile deformation and manufacturing data for tubes in many different reactors. The manufacturing data have been analyzed with respect to the measured in-reactor performance of the pressure tubes. Data from pressure tubes fabricated over a 30-year period have been analyzed. During this time the manufacturing parameters have evolved to improve the workability and mechanical properties and also reduce tube-to-tube variability. The effect of manufacturing variables on in-reactor performance has been assessed for tubes that have been in service for many years and approaching the end of their design life. The analysis shows that the two most important factors that affect the deformation behavior of pressure tubes are the material source (ingot) and the extrusion conditions. These are related by the microstructure (texture, grain size, and dislocation density) to the deformation. The results of the statistical analyses will be presented and discussed in terms of the manufacturing conditions that bring about specific microstructures.

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 categoriesInsufficient 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 score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Citations15
Published2007
Admission routes2
Has abstractyes

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