MétaCan
Menu
Back to cohort
Record W4255681962 · doi:10.1115/1.4052399

Pitch and Pattern Effects on Streamwise Fluidelastic Instability in Tube Arrays

2021· article· en· W4255681962 on OpenAlexaff
Marwan A. Hassan, David Weaver

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsTube (container)Transverse planeInstabilityMechanicsFlow (mathematics)Square (algebra)PhysicsVibrationParametric statisticsWakeGeometryStructural engineeringAcousticsMathematicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Fluidelastic instability (FEI) is well known to be a critical flow-induced vibration concern for the integrity of the tubes in nuclear steam generators. Traditionally, this has been assumed to occur in the direction transverse to the direction of flow but the tube failures at San Onofre Nuclear Generating Station (SONGS) in Los Angeles proved that this assumption is not generally valid. A simple tube-in-channel theoretical model was previously developed to predict streamwise as well as transverse FEI in a parallel triangular tube array. This predicted that this array geometry was particularly sensitive to streamwise FEI for high mass-damping parameters and small pitch ratios, the conditions in which the SONGS failures occurred. The advantage of this simple modeling approach is that no new empirical data are required for parametric studies of the effects of tube pattern and pitch ratio on FEI. The tube-in-channel model has been extended to in-line square, normal triangular, and rotated square tube arrays and the stability of these geometric patterns are analyzed for the effects of varying pitch ratio and the mass-damping parameter. The results are compared with the available experimental data and conclusions are drawn regarding the relative vulnerability of these different tube array geometries to streamwise FEI.

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 score0.511
Threshold uncertainty score0.433

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicFluid Dynamics and Vibration AnalysisFrench-language works237,207