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Record W2008446849 · doi:10.1115/pvp2013-97751

Probabilistic Evaluation of the Integrity of Steam Generator Tubes Subjected to Flow Induced Vibrations

2013· article· en· W2008446849 on OpenAlexafffund
Marwan Hassan, Jovica Riznic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsCanadian Nuclear Safety CommissionUniversity of Guelph
FundersCanadian Nuclear Safety Commission
KeywordsFrettingFretting wearHeat exchangerTube (container)VibrationVortex-induced vibrationTurbulenceMaterials scienceStructural engineeringBoiler (water heating)CrackingFlow (mathematics)Mechanical engineeringEngineeringMechanicsComposite materialWaste managementAcousticsPhysics

Abstract

fetched live from OpenAlex

Flow-Induced Vibrations (FIV) continue to affect the operations of nuclear power plant components such as heat exchanger tube bundles. The negative effect of FIV is in the form of tube fatigue, cracking and fretting wear at the supports. Fretting wear at the supports is the result of tube/support impact and friction. Fluidelastic and turbulence forces are the two main excitation mechanisms that feed energy into the system causing these violent vibrations. To minimize this effect all support clearances must be kept at a very small value. This paper investigates the consequences of losing the effectiveness of a particular support as a result of corrosion or excessive fretting wear. A full U-bend tube subjected to both fluidelastic and turbulence forces is utilized in this work.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.025
GPT teacher head0.244
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2013
Admission routes2
Has abstractyes

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