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Record W2607277259 · doi:10.5006/c2016-07237

Corrosion Assessment of Nickel-based Alloys for SCWR Fuel Cladding Application

2016· article· en· W2607277259 on OpenAlexaffabout
Yimin Zeng, Babak Shalchi Amirkhiz, Xin Pang, Maciej Podlesny, Magdalene Matchim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCorrosionMaterials scienceCladding (metalworking)MetallurgyNickel

Abstract

fetched live from OpenAlex

Abstract Supercritical water-cooled reactor (SCWR) is an innovative Generation IV reactor and merits further research and development with the intent of being pursued for implementation in the next 30 years. The SCWR is a high temperature and high pressure water-cooled reactor in which supercritical water (SCW) is used as the primary coolant for a direct once-through cycle to achieve noticeable advantages. For optimum thermal efficiency, the Canadian SCWR concept requires a fuel core outlet temperature of 625 °C at 25 MPa with a predicted peak temperature as high as 800 °C. As a result, material selection for the fuel cladding is one of the most challenging aspects for the realization of the SCWR. Detailed materials assessments based on public data have carried out by Canadian research groups and several nickel-based alloys, such as UNS N06625, UNS R20033 and UNS N07214 alloys, are selected for the fuel cladding. However, significant knowledge gaps exist in determining whether the alloys can be used for the fuel cladding in the SCWR. This paper introduces our most recent laboratory results on corrosion and stress corrosion cracking (SCC) of the nickel-based alloys in SCW.

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 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 score0.185
Threshold uncertainty score1.000

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)0.0010.000

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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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