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Record W2022054721 · doi:10.1115/fedsm-icnmm2010-30029

Experimental Study of Fluidelastic Instability in a Parallel Triangular Tube Array

2010· article· en· W2022054721 on OpenAlexaff
Ahmed A. Khalifa, David Weaver, Samir Ziada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInstabilityTube (container)StiffnessMechanicsFlow (mathematics)Mechanism (biology)Work (physics)RowStructural engineeringPhysicsEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Fluidelastic instability is a short term failure mode that occurs in tube bundles subjected to cross flow. It is believed that instability occurs due to two possible mechanisms; one is related to fluid coupling of neighboring tubes, the so called “stiffness mechanism”, and the other is related to a “negative fluid damping mechanism” i.e., fluidelastic forces in phase with tube velocity. The usage of a single flexible tube in a rigid array will eliminate the stiffness mechanism effect and leave only the damping mechanism, which makes the problem less complex. This paper presents a fundamental study of fluidelastic instability in a parallel triangular tube array subjected to air cross flow. It is found that a single flexible tube located in the third row of a rigid parallel triangular array does become fluidelastically unstable at essentially the same velocity as for a fully flexible array. However, when the single flexible tube is located in the first, second, fourth, or fifth rows, no instability behavior is detected. It is concluded from this work that, the tube location inside the array affects significantly its fluidelastic instability behavior when tested as a single flexible tube in a rigid array. It follows that a single flexible tube can be used for fundamental study of the phenomenon but not generally to generate stability maps for practical use.

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 score0.874
Threshold uncertainty score0.423

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 head0.009
GPT teacher head0.240
Teacher spread0.230 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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