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Preliminary Stress Corrosion Cracking Modeling Study of a Dissimilar Material Weld of Alloy (Inconel) 182 with Stainless Steel 316 in Pressurized Water Nuclear Reactor

2014· article· en· W2128130309 on OpenAlexfundvenueno aff
Omar Fernandes Aly, Miguel Mattar Neto, Mônica Maria De Abreu Mendonça Schvartzman

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
FundersComissão Nacional de Energia NuclearIndigenous and Northern Affairs CanadaUniversidade Federal de Minas GeraisCentro de Desenvolvimento da Tecnologia NuclearCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsInconelStress corrosion crackingMaterials scienceMetallurgyAlloyWeldingCrackingCorrosionStress (linguistics)Composite material

Abstract

fetched live from OpenAlex

Dissimilar welds (DW) are normally used in many components junctions in structural project of PWR (Pressurized Water Reactors) in Nuclear Plants. One had been departed of a DW of a nozzle located at a Reactor Pressure Vessel (RPV) of a PWR reactor, that joins the structural vessel material with an A316 stainless steel safe end. This weld is basically done with Inconel/Alloy 182 with a weld buttering of Inconel/Alloy 82. It had been prepared some axial cylindrical specimens retired from the Alloy 182/A316 weld end to be tested in the slow strain rate test machine located at CDTN laboratory. Based in these stress corrosion susceptibility results, it was done a preliminary semi-empiric modeling application to study the failure initiation time evolution of these specimens. The used model is composed by a deterministic part, and a probabilistic part according to the Weibull distribution. It had been constructed a specific Microsoft Excel worksheet to do the model application of input data. The obtained results had been discussed according with literature and also the model application limits.

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 score0.203
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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
Published2014
Admission routes2
Has abstractyes

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