MétaCan
Menu
Back to cohort
Record W2765513451 · doi:10.1115/pvp2017-66052

Fracture Initiation in Pressure Tube Specimens of Hydrided Irradiated Zr-2.5Nb Materials With Split Circumferential Hydrides

2017· article· en· W2765513451 on OpenAlexafffund
Shin-Jang Sung, Jwo Pan, Poh-Sang Lam, Douglas A. Scarth

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsKinectrics (Canada)
FundersCANDU Owners Group
KeywordsMaterials scienceFracture toughnessIrradiationFracture (geology)Composite materialFracture mechanicsStress (linguistics)Strain (injury)Tube (container)High pressurePressure vesselThermodynamicsNuclear physics

Abstract

fetched live from OpenAlex

Fracture initiation for axial cracks in pressure tube (PT) specimens of hydrided irradiated Zr-2.5Nb materials with split circumferential hydrides at room temperature is examined by conducting three-dimensional finite element analyses. With a strain-based fracture criterion with consideration of stress triaxiality, the location for the earliest crack initiation is determined near the middle of the axial crack front. For PT specimens with split circumferential hydrides, three types of strain concentration are observed in the ligaments ahead of the crack front. The computational results suggest that the internal pressure for crack initiation of hydrided irradiated PT specimens with many randomly distributed split circumferential hydrides needs only 55% to 70% of that for unhydrided irradiated PT specimens. The computational results can be used to explain the near 40% reduction of the fracture toughness at room temperature obtained from hydrided irradiated PT specimens when compared with that from unhydrided irradiated ones.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicNuclear Materials and PropertiesFrench-language works237,207