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Record W2898594570 · doi:10.1115/pvp2018-85143

A Quasi-Steady In-Plane Fluidelastic Instability Analysis of a Rotated Triangular Tube Array in Two-Phase Cross Flow

2018· article· en· W2898594570 on OpenAlexaff
Stephen Olala, Njuki Mureithi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInstabilityMechanicsDragDisplacement (psychology)Flow (mathematics)PhysicsPlane (geometry)StiffnessWork (physics)Tube (container)Classical mechanicsMaterials scienceGeometryMathematicsThermodynamics

Abstract

fetched live from OpenAlex

In-plane fluidelastic instability is a dynamic phenomenon requiring fluid coupling of at least two degrees-of-freedom, in this case, at least two flexible tubes. Due to the nature of the mechanism causing streamwise fluidelastic instability, a purely experimental or an unsteady determination would require intensive experimental effort. As a compromise between experimental effort and prediction accuracy, the quasi-steady model is used in the current study. In the present work, previously measured quasi-steady and unsteady forces are used to estimate the time delay first between the displacement of an oscillating tube and the forces generated on itself then the time delay between the displacement of a central oscillating tube and the forces induced on the adjacent tubes. The estimated time delays are then used together with drag coefficients and derivatives to predict the in-plane fluidelastic instability in a rotated triangular tube array of P/D = 1.5 subjected to two-phase flow. The results closely replicate dynamic test results and confirm the predominance of the stiffness controlled mechanism and the potential of the quasi-steady model in accurately predicting streamwise fluidelastic instability in arrays subjected to two-phase flows.

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 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 score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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