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Record W4379987428 · doi:10.1017/aer.2023.46

Experimental wind-tunnel study of the dynamics of inverted foils for energy harvesting

2023· article· en· W4379987428 on OpenAlexafffund
Manuel Flores Salinas, Ruxandra Mihaela Botez, Mohammad Tavallaeinejad, Michael P. Paı̈doussis

Bibliographic record

VenueThe Aeronautical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsMcGill UniversityÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaÉcole de technologie supérieure
KeywordsFOIL methodWind tunnelReynolds numberAmplitudeOscillation (cell signaling)AerodynamicsMechanicsRange (aeronautics)Trailing edgeEnergy harvestingWind powerEnergy (signal processing)Water tunnelPower (physics)PhysicsAerospace engineeringAcousticsMaterials scienceElectrical engineeringEngineeringOpticsTurbulenceThermodynamics

Abstract

fetched live from OpenAlex

Abstract This paper describes the methodology used to analyse oscillations of foils of a wide range of aspect ratios, 0.5 ≤ AR ≤ 4, and Reynolds numbers, 10 4 ≤ Re ≤ 10 5 , for energy harvesting purposes. The foils were fixed at their trailing edge, and their dynamical behaviour was captured as the wind speed was varied. The foil response was then analysed as a function of velocity, Reynolds number, oscillation amplitude and frequency. Additionally, the forces and moments acting on the foils were measured, utilising an aerodynamic scale, designed and built in-house. An empirical power generation equation was derived to determine the foil characteristics for maximum energy harvesting production. The results show that a flexible foil with AR = 3 with oscillations in the large-amplitude regime is the most effective for energy harvesting.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.035
GPT teacher head0.260
Teacher spread0.225 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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