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Record W2321953291 · doi:10.1115/fedsm-icnmm2010-30082

On the Feasibility of Using Linear Fluid Dynamics in an Overall Nonlinear Model for the Dynamics of Cantilevered Cylinders in Axial Flow

2010· article· en· W2321953291 on OpenAlexaff
Mojtaba Kheiri, Michael P. Paı ̈doussis, Marco Amabili

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsGalerkin methodNonlinear systemDiscretizationBifurcationInstabilityMathematicsFlow (mathematics)Equations of motionFluid dynamicsMathematical analysisMechanicsClassical mechanicsPhysicsGeometry

Abstract

fetched live from OpenAlex

A curiosity-driven study is presented here which introduces and tests an analytical model to be employed for describing the dynamics of cantilevered cylinders in axial flow. This model is called “hybrid” because it encompasses linear fluid dynamics and nonlinear structural dynamics. Also, both the linear and fully nonlinear models are recalled here. For all these models Galerkin’s method is used to discretize the nondimensional equation of motion. For the hybrid and nonlinear models a numerical method based on Houbolt’s Finite Difference Method (FDM) is used to solve the discretized equations, as well as AUTO, which is a software used to solve continuation and bifurcation problems for differential equations. The capability of the hybrid model to predict the dynamical behaviour of cantilevered cylinders in axial flow is assessed by examining three different sets of parameters. Here, the main focus is put on the onset of instabilities and the amplitude of the predicted motion. According to the results given in the form of bifurcation diagrams and several tabulated numerical values, the hybrid model is proved to be unacceptable although it can predict the onset of first instability, and even the onset of post-divergence instability in some cases.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.907

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 head0.032
GPT teacher head0.275
Teacher spread0.243 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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