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Record W2081870999 · doi:10.1520/jai103008

The Effect of Microstructure on Delayed Hydride Cracking Behavior of Zircaloy-4 Fuel Cladding—An International Atomic Energy Agency Coordinated Research Program

2010· article· en· W2081870999 on OpenAlexaff
C.E. Coleman, V. Grigoriev, V. Inozemtsev, В. А. Маркелов, M. Roth, Vidas Makarevičius, Y. S. Kim, Kanwar Liaqat Ali, J. K. Chakravarrty, R. Mizrahi, Ramanathan S. Lalgudi

Bibliographic record

VenueJournal of ASTM International · 2010
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsCladding (metalworking)Materials scienceZirconium alloyMicrostructureHydrideCrackingAtomic energyMetallurgyAgency (philosophy)Nuclear engineeringZirconiumComposite materialEngineeringMetal

Abstract

fetched live from OpenAlex

Abstract The rate of delayed hydride cracking (DHC) has been measured in Zircaloy-4 fuel cladding in several metallurgical conditions using the pin-loading tension technique. In light water reactor (LWR) cladding in the cold-worked and cold-worked and stress-relieved conditions, the cracking rate followed Arrhenius behavior up to about 280 °C, but at higher temperatures the rate declined with no cracking above 300°C. Non-LWR cladding appeared to behave in the same manner. In LWR cladding in the recrystallized condition, the cracking rate was highly variable because it depended on KI within the test range up to 25 MPa√m, whereas in the other LWR claddings, cracking rate was independent of KI, indicating that KIH was below 11 MPa√m. The main role of microstructure was to control the material strength; the cracking rate increased as the strength increased. Although all the claddings had a radial texture, it did not protect the cladding from DHC. The DHC fracture surface consisted of flat broken hydrides, often in arcs, but no striations were observed, except in one specimen subjected to thermal cycles.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
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 head0.017
GPT teacher head0.339
Teacher spread0.322 · 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

Citations27
Published2010
Admission routes1
Has abstractyes

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