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Record W2905196231 · doi:10.3139/124.110946

Investigations of the hydrogen diffusion and distribution in Zirconium by means of Neutron Imaging

2018· article· en· W2905196231 on OpenAlexaff
M. Große, J.R. Santisteban, Johannes Bertsch, Burkhard Schillinger, Anders Kaestner, Mark R. Daymond, Nikolay Kardjilov

Bibliographic record

VenueKerntechnik · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersPaul Scherrer Institut
KeywordsZirconium alloyHydrogenMaterials scienceZirconiumCladding (metalworking)Zirconium hydrideAlloyHydrideDiffusionCrackingHydrogen embrittlementNeutron diffractionMetallurgyComposite materialCorrosionThermodynamicsCrystallographyChemistry

Abstract

fetched live from OpenAlex

Abstract Absorbed hydrogen degrades the mechanical properties of zirconium alloys used for nuclear fuel claddings. Not only the total amount of hydrogen absorbed in the cladding tube but also the zirconium hydride orientation and its distribution influence the toughness of the material. For instance, the so-called delayed hydride cracking is caused by the diffusive re-distribution of hydrogen into the dilative elastic strain field ahead of crack tips. The paper presents in-situ and ex-situ neutron imaging investigations of hydrogen uptake, diffusion and distribution in zirconium alloys used for claddings. An overview about results of in-situ experiments studying the hydrogen uptake in strained Zircaloy-4, as well as ex-situ investigations of the diffusion of hydrogen in cold rolled Zircaloy-2 and Zr-2.5 % Nb alloy depending on temperature, rolling direction and thermal treatment and of the hydrogen re-distribution in the β-phase of Zircaloy-4 during a Three-Point-Bending-Test at 600 °C are presented.

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 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 score0.008
Threshold uncertainty score0.142

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)0.0000.000

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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