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Record W2908879681 · doi:10.1115/1.4042500

Effect of Steam Temperature and Pressure on the Oxidation of Potential Coating Alloy FeCrAl for Supercritical Water-Cooled Reactor Application

2019· article· en· W2908879681 on OpenAlexaff
Bingjie Xiao, Xiao Huang, Qi Yang, Prakash Patnaik

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2019
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsSupercritical fluidMaterials scienceAlloyScanning electron microscopeCoatingCorrosionOxideMicrostructureMetallurgyPhase (matter)Atmospheric temperature rangeLayer (electronics)Composite materialChemistry

Abstract

fetched live from OpenAlex

MCrAl (M = Fe, Ni, or Co) alloys have exceptional corrosion and oxidation resistance and can be used as both oxidation resistant structural materials and coatings. As coatings, they protect high temperature steels or Ni based alloys by forming a dense alumina layer on the surface and thus impeding further oxidation. In order to assess its potential usage as an overlay coating on components used in supercritical water-cooled nuclear reactors (SCWRs), an Fe-2 3Cr-5Al alloy in the form of wire was tested under two different super-heated steam (SHS) conditions (625 °C and 800 °C) and also in supercritical water (SCW) (625 °C and 26 MPa), for 500 h. The corrosion behavior of samples was assessed by measuring the weight change per unit surface area and by examining the surface, cross-sectional microstructure and the phase compositions using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The tested samples showed different oxidation behavior after exposure to these three conditions. SEM and XRD results showed that FeCrAl has the ability to form protective Al- and Cr-containing oxide(s) under all three conditions. Based on the findings, it is concluded that the oxidation behavior of Fe–23Cr–5Al is highly influenced by pressure and temperature within the range of testing conditions. SHS exposure at low temperature led to greater weight gain while that in SCW resulted in weight loss. Overall, its performance is better under SHS conditions compared to CoCrWC S16 but worse under the SCW condition.

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 score0.221
Threshold uncertainty score0.169

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 head0.003
GPT teacher head0.201
Teacher spread0.198 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

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