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Record W2566417270 · doi:10.1088/1755-1315/49/7/072003

CFD evaluation of added damping due to fluid flow over a hydroelectric turbine blade

2016· article· en· W2566417270 on OpenAlexafffund
Jean-Philippe Gauthier, A M Giroux, Stéphane Étienne, Frédérick P. Gosselin

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsHydro-QuébecPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMechanicsVibrationTurbine bladeTurbineFinite element methodFlow (mathematics)Structural engineeringFluid–structure interactionComputational fluid dynamicsDynamic mode decompositionModal analysisReynolds numberPropellerModalVortex-induced vibrationEngineeringPhysicsMaterials scienceAcousticsMechanical engineeringTurbulenceMarine engineering

Abstract

fetched live from OpenAlex

To estimate structural fatigue, vibrational response to realistic spectrum of excitations and associated equivalent damping are of paramount importance. In this paper, an approach to quantify flow-induced damping of a relatively heavy fluid on a vibrating hydraulic turbine blade using numerical simulations is presented. First, mode shapes and frequencies of the immersed structure are obtained by modal analysis using the finite element method. Then, forced oscillatory modal motion is prescribed on the structural boundary of unsteady Reynolds-averaged Navier-Stokes flow simulations. Damping is finally computed as the normalized work done by the resulting fluid load on the structure. Validation is achieved by comparing the numerical results with available experimental data for a steel hydrofoil oscillating in flowing water. For this case, the linear increase in the damping ratio with the flow velocity is reproduced within 10% of the experimental values. Application of the method to an actual hydroelectric propeller turbine blade yields a fluid damping value of around 15% of critical damping for its first vibration mode.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.666

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)0.0010.000

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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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 designBench or experimental
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

Citations10
Published2016
Admission routes2
Has abstractyes

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