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Record W4399531665 · doi:10.3390/met14060692

A Model for the Temperature Distribution in a Rolled Joint in a CANDU Reactor Exploiting the Decomposition of the β-Zr Phase

2024· article· en· W4399531665 on OpenAlexaff
G.A. McRae, M. Griffiths, C.E. Coleman

Bibliographic record

VenueMetals · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceArrhenius equationShrinkagePhase (matter)DecompositionNeutron fluxThermodynamicsAlloyMetastabilityActivation energyMetallurgyNeutronComposite materialNuclear physicsChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A competing-rates model is presented to account for operational changes in the metastable β-Zr phase of the Zr-2.5Nb alloy used to make CANDU reactor pressure tubes and is used to predict temperature gradients at the outlet rolled joints using the decomposition of the β-Zr phase as a proxy for temperature. High temperatures decompose the β phase by enhancing the formation of small particles of ω and α phases. Fast neutron flux causes the ω and α phases to shrink. This process is assumed to depend on the total volume of the particles, because they are comparable to, or smaller than, the size of the neutron displacement cascades. The barrier energy for thermal growth was determined to be 2.43 eV, when an Arrhenius A factor of 1013/s was assumed. The cross section for (ω+α)-phase shrinkage is 24.5 barns for Zr-2.5Nb irradiated in CANDU reactors. Assuming that the shrinkage is dominated by the migration of self-interstitial point defects, a defect production efficiency of 1.4% was found.

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 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 score0.156
Threshold uncertainty score0.148

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)0.0000.000

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 head0.053
GPT teacher head0.302
Teacher spread0.249 · 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
Published2024
Admission routes1
Has abstractyes

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