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Overview of Computational Methods for Hydrogen Diffusion Coupled With Stress and Temperature Gradients in Zirconium Reactor Components

2006· article· en· W2084206933 on OpenAlexaffabout
Don R. Metzger, R. G. Sauve ́, T.P. Byrne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsHydrideCrackingFinite element methodHydrogenZirconiumMaterials scienceNuclear engineeringStress (linguistics)Zirconium alloyCoupling (piping)DiffusionZirconium hydrideMetallurgyThermodynamicsStructural engineeringComposite materialChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

In order to better understand and predict hydride blister formation and hydride cracking in zirconium alloy CANDU(1) fuel channels, specialized computational methods are required. Hydride blister formation involves the coupled action of gradients in temperature and hydrogen concentration, while hydride cracking involves coupling of stress and concentration gradients. Hydride accumulation and crack growth in a leaking crack involves a complete coupling of concentration, stress and temperature gradients. In all cases, the action of dissolution or precipitation of hydride adds complexity to the numerical analysis procedure. Dedicated finite difference and finite element programs have been developed and applied to blister formation and uniform temperature cracking problems. On the basis of experience gained in the use of such specialized codes, a fully coupled capability has been integrated into a general-purpose finite element program. This program can more realistically address complex load and temperature histories that may be encountered during fuel channel operation. An overview of important computational features is given along with applications relevant to current experimental research and fuel channel assessments. (1)CANDU is a registered trademark of Atomic Energy of Canada Limited.

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 score0.107
Threshold uncertainty score0.267

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 head0.029
GPT teacher head0.291
Teacher spread0.263 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

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