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Record W4312837713 · doi:10.1115/pvp2022-86694

Effect of Pitch Ratio and Flat Bar Support Conditions on FEI Threshold for Low MDP Parallel Triangular Array – Single-Phase Water-Flow

2022· article· en· W4312837713 on OpenAlexaff
Amro Elhelaly, Marwan Hassan, David Weaver, Soha Eid Moussa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsBar (unit)Flow (mathematics)Tube (container)Instrumentation (computer programming)Materials sciencePhase (matter)Stability (learning theory)MechanicsComputer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract An intensive research program has been initiated to provide a better understanding of the fundamental aspects of FEI in tube bundles. This paper reports the findings of an experimental investigation utilizing a newly commissioned experimental rig. Initially, a series of water-flow experiments were conducted in a fully flexible, low mass damping parameter, parallel triangular tube array (with a cluster of 9 brass rods). To investigate the effect of pitch ratio on the onset of FEI, four different pitch-to-diameter ratios of 1.27, 1.37, 1.48, and 1.62 were utilized and presented in this study. Moreover, to illustrate the dynamic effect of flat bar support conditions on the stability threshold of FEI, a series of experiments were conducted in each pitch ratio with varied tube/support clearances. A full description of the flow loop, including the utilized instrumentation, and troubleshooting challenges encountered in commissioning the test rig is discussed. Moreover, the obtained results of the single-phase flow experiments are presented. The current results show good agreement with the available data from previous investigations, which established the confidence level needed to start two-phase experiments.

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 categoriesInsufficient payload (model declined to judge)
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 score0.484
Threshold uncertainty score0.999

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)0.0020.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 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
Published2022
Admission routes1
Has abstractyes

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