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Record W1996148004 · doi:10.1115/pvp2009-78076

Flow-Induced Vibration and Fretting-Wear Performance of CANDU™ Steam Generator U-Tubes: Instrumentation

2009· article· en· W1996148004 on OpenAlexafffund
Atef Mohany, Victor P. Janzen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsAtomic Energy (Canada)
FundersAtomic Energy of Canada Limited
KeywordsBundleBoiler (water heating)Nuclear engineeringInstrumentation (computer programming)EngineeringVapor qualityVibrationMechanical engineeringFlow (mathematics)Materials scienceMechanicsWaste managementComputer scienceHeat exchangerPhysicsAcoustics

Abstract

fetched live from OpenAlex

This paper describes a test program that was developed to measure the dynamic response of a bundle of steam generator U-tubes with Anti-Vibration Bar (AVB) supports, subjected to Freon two-phase cross-flow. The tube bundle has similar geometrical conditions to those expected for future CANDU™ steam generators. Future steam generators will be larger than previous CANDU steam generators, nearly twice the heat transfer area, with significant changes in process conditions in the U-bend region, such as increased steam quality and a broader range of flow velocities. This test program is one of the initiatives that AECL is undertaking to demonstrate that the tube support design for future CANDU steam generators meets the stringent requirements associated with a 60 year lifetime. The main objective of the tests is to address the issue of in- and out-of-plane fluidelastic instability and random turbulent excitation of a U-tube bundle with AVB supports. Details of the test rig, measurement techniques and preliminary instrumentation results are described in the paper.

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 score0.800
Threshold uncertainty score0.295

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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