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Record W1971287230 · doi:10.1115/icone17-75617

Analytical Investigation of Deaerator Steam Inlet Nozzle Corrosion Allowance in a CANDU Station

2009· article· en· W1971287230 on OpenAlexafffund
Ali Keshavarz, Andrew K. Ali, Randy K. Lall

Bibliographic record

VenueVolume 2: Structural Integrity; Safety and Security; Advanced Applications of Nuclear Technology; Balance of Plant for Nuclear Applications · 2009
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsCanadian Nuclear Safety CommissionOntario Power Generation
FundersUniversity of Ontario Institute of Technology
KeywordsPipingDeaeratorNozzleAllowance (engineering)InletCarbon steelCorrosionMaterials scienceStress (linguistics)Stress corrosion crackingPressure vesselHead (geology)MetallurgyStructural engineeringEngineeringComposite materialMechanical engineeringGeology

Abstract

fetched live from OpenAlex

Flow-accelerated corrosion (FAC) is a phenomenon that results in metal loss from piping, vessels and equipment made of carbon steel. This metal loss can lead to stress to occur at the steam inlet nozzle side, where it is located at the side of the deaerator. This paper presents a method to find the thickness critical of the steam inlet nozzle. A Finite Element (FE) model of the pressure vessel head was created to perform a stress analysis using NX Nastran 5.0. By applying materials properties, loads and constraints to the model, the results obtained are required to satisfy the following criterion: vonMises≥SySy=YieldStrength The results obtained from the stress analysis were analyzed to obtain a corrosion allowance and it was compared to the recommended value from a normal deaerator design, which is roughly 0.25 inches. From the FE model, and by continuously reducing the thickness of the nozzle, it was determined that the corrosion allowance is 0.229 inches, and that the percentage error was 8.4%.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 designTheoretical or conceptual
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
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueVolume 2: Structural Integrity; Safety and Security; Advanced Applications of Nuclear Technology; Balance of Plant for Nuclear ApplicationsSame topicNon-Destructive Testing TechniquesFrench-language works237,207