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Record W2884771189 · doi:10.1139/tcsme-2017-0071

A parametric study of energy extraction from vortex-induced vibrations

2018· article· en· W2884771189 on OpenAlexaffvenue
Olivier Paré-Lambert, Mathieu Olivier

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDimensionless quantityReynolds numberMechanicsTurbineCylinderPhysicsParametric statisticsVortex sheddingVibrationVortexWakeAmplitudeVortex-induced vibrationClassical mechanicsMathematicsAcousticsGeometryTurbulenceThermodynamicsOptics

Abstract

fetched live from OpenAlex

This paper presents a parametric investigation of an oscillating-cylinder turbine concept based on vortex-induced vibrations. The parametric space includes four parameters: the Reynolds number, the mass ratio, the dimensionless stiffness, and the dimensionless damping. The damping–stiffness space is explored for four different mass ratios at a fixed Reynolds number of 200. Also, the influence of the parameters on the amplitude of cylinder displacement and on the efficiency of power harnessing is discussed. Vortex-shedding patterns observed within the parametric space are investigated. The 2S, 2P, and C(2S) wake modes are observed and are related to turbine performance. Preliminary results show a maximum efficiency of 10.6%, which is obtained with low mass ratios.

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: none
Teacher disagreement score0.711
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.216
Teacher spread0.203 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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