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Record W2069410247 · doi:10.1115/pvp2009-78078

Design Specifications to Ensure Flow-Induced Vibration and Fretting-Wear Performance in CANDU™ Steam Generators and Heat Exchangers

2009· article· en· W2069410247 on OpenAlexaff
Victor P. Janzen, Yingke Han, M. J. Pettigrew

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsPolytechnique MontréalAtomic Energy (Canada)
Fundersnot available
KeywordsHeat exchangerBoiler (water heating)FrettingVibrationFretting wearMechanical engineeringVortex-induced vibrationEngineeringNuclear engineeringStructural engineeringWaste management

Abstract

fetched live from OpenAlex

Preventing flow-induced vibration and fretting-wear problems in steam generators and heat exchangers requires design specifications that bring together specific guidelines, analysis methods, requirements and appropriate performance criteria. This paper outlines the steps required to generate and support such design specifications for CANDU™ nuclear steam generators and heat exchangers, and relates them to typical steam-generator design features and computer modeling capabilities. It also describes current issues that are driving changes to flow-induced vibration and fretting-wear specifications that can be applied to the design process for component refurbishment, replacement or new designs. These issues include recent experimental or field evidence for new excitation mechanisms, e.g., the possibility of in-plane fluidelastic instability of U-tubes, the demand for longer reactor and component lifetimes, the need for better predictions of dynamic properties and vibration response, e.g., two-phase random-turbulence excitation, and requirements to consider system “excursions” or abnormal scenarios, e.g., a main steam line break in the case of steam generators. The paper describes steps being taken to resolve these issues.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.398

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 head0.034
GPT teacher head0.206
Teacher spread0.171 · 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 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

Citations7
Published2009
Admission routes1
Has abstractyes

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