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Record W3094898893 · doi:10.1115/pvp2020-21518

Study of the Influence of Microstructure and Intergranular Carbides on the Oxidation Behavior of a Nickel Base Alloy 690 TT in Supercritical Water Nuclear Reactor Conditions

2020· article· en· W3094898893 on OpenAlexaff
Alberto Sáez-Maderuelo, María Luisa Ruiz Lorenzo, Francisco Javier Perosanz, Patricie Halodová, Jan Procházka, Hygreeva Kiran Namburi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsIntergranular corrosionAlloyMaterials scienceCarbideSupercritical fluidMetallurgyStress corrosion crackingMicrostructureAuger electron spectroscopyScanning electron microscopeCorrosionOxideComposite materialChemistryNuclear physics

Abstract

fetched live from OpenAlex

Abstract Alloy 690, which was designed as a replacement for the Alloy 600, is widely used in the nuclear industry due to its optimum behavior to stress corrosion cracking (SCC) under nuclear reactor operating conditions. Because of this superior resistance, alloy 690 has been proposed as a candidate structural material for the Supercritical Water Reactor (SCWR), which is one of the designs of the next generation of nuclear power plants (Gen IV). In spite of this, striking results were found [1] when alloy 690 was tested without intergranular carbides. These results showed that, contrary to expectations, the crack growth rate is lower in samples without intergranular carbides than in samples with intergranular carbides. Therefore, the role of the carbides in the corrosion behavior of Alloy 690 is not yet well understood. Considering these observations, the aim of this work is to study the effect of intergranular carbides in the oxidation behavior (as a preliminary stage of degenerative processes SCC) of Alloy 690 in supercritical water (SCW) at two temperatures: 400 °C and 500 °C and 25 MPa. Oxide layers of selected specimens were studied by different techniques like Scanning Electron Microscope (SEM) and Auger Electron Spectroscopy (AES).

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 score0.055
Threshold uncertainty score0.215

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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