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Record W4328121256 · doi:10.3390/en16062885

Oscillating-Foil Turbine Performance Improvement by the Addition of Double Gurney Flaps and Kinematics Optimization

2023· article· en· W4328121256 on OpenAlexafffund
Benoît Genest, Guy Dumas

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsTrailing edgeTurbineAirfoilFOIL methodReynolds-averaged Navier–Stokes equationsKinematicsLeading edgeMechanicsMarine engineeringEngineeringMechanical engineeringComputational fluid dynamicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Refinement of the performance of a fully constrained oscillating-foil turbine is carried out via the addition of passive double Gurney flaps. Flaps ranging from hGF=0.005c to 0.075c are added at the trailing edge of the NACA 0015 blade of turbines operating in high-efficiency regimes without leading-edge vortex shedding (LEVS). Performance improvements are determined using 2D numerical simulations with an unsteady Reynolds-averaged Navier–Stokes (URANS) approach. Based on a recent study of the double Gurney flaps on stationary foils, instantaneous power-extraction coefficients are analyzed and modifications of the foil’s kinematics are tested in order to fully benefit from the Gurney flaps’ performance improvements. Modifications to the pivot point location of the foil, to the pitch-heave phase, and to the pitching amplitude of the turbine are considered. Improvements are found for all turbine cases studied, including some of the previously optimal cases reported in the literature. The double Gurney flaps, being a simple and passive device, offer great practical application potential. They represent an efficient refinement to already robust and high-performance oscillating-foil turbines operating without the perceived benefit of leading-edge vortex shedding, an essential characteristic for actual, finite-span applications.

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 score0.133
Threshold uncertainty score0.220

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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