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Record W2179038138 · doi:10.1115/pvp2015-45600

The Effect of Streamwise Tube Motion on the Unsteady Fluid Forces in a Normal Triangle Tube Array

2015· article· en· W2179038138 on OpenAlexaff
Salim El Bouzidi, Marwan Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of GuelphMcMaster University
Fundersnot available
KeywordsMechanicsReynolds numberPhysicsInstabilityPerturbation (astronomy)Strouhal numberClassical mechanicsBundleAmplitudeTransverse planeFlow (mathematics)TurbulenceOpticsMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Fluidelastic instability is generally regarded as the most severe type of flow excitation mechanism. When this mechanism prevails, it could cause serious damage to tube arrays in a very short period of time. This mechanism is characterized by a critical flow velocity beyond which the tubes undergo unstable oscillations. Recently, a number of experimental investigations showed that it is possible to have instability in the streamwise direction; previously, it was believed that fluidelastic instability was only a concern in the direction transverse to the flow. The purpose of this study is to characterize the flow in the channels surrounding a vibrating tube in a normal triangular bundle with P/d = 1.5. The tube is oscillating in the streamwise direction with a constant amplitude. Numerical simulations were conducted by solving the unsteady Reynolds Averaged Navier-Stokes equations (uRANS) cast in Arbitrary Lagrangian-Eulerian (ALE) form. The unsteady flow perturbation is estimated along the flow channel. The pressure perturbation is used to compute the streamwise unsteady force coefficients in the context of Chen’s model. The perturbation phase and decay are extracted and utilized in the framework of the Lever & Weaver model to study the stability of tube bundles due to tube motion in the streamwise direction.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.210
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

Citations3
Published2015
Admission routes1
Has abstractyes

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