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Record W4386831742 · doi:10.1063/5.0157096

Steady and unsteady characteristics of circular arc airfoils for water pumping windmills

2023· article· en· W4386831742 on OpenAlexafffund
Itoje H. John, David Wood

Bibliographic record

VenueJournal of Renewable and Sustainable Energy · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAirfoilWind tunnelStall (fluid mechanics)Angle of attackDragLift-to-drag ratioMechanicsAerodynamicsCamber (aerodynamics)Lift-induced dragReynolds numberMarine engineeringWater tunnelWakeStructural engineeringLift (data mining)Aerospace engineeringEngineeringPhysicsVortexComputer scienceTurbulence

Abstract

fetched live from OpenAlex

Accurate airfoil lift and drag data at low Reynolds number, Re, and high angles of attack, α, are needed to analyze the performance of small wind turbines, particularly their starting. In the current study, the steady and unsteady aerodynamic characteristics of circular arc airfoils (CAAs) with and without spars, as used in water-pumping windmills, were examined in a wind tunnel of 1 m2 cross section at Re<106. The tunnel was configured as an open jet and a closed section. The effects of varying geometrical characteristics on the CAA performance were investigated using a combination of thickness, camber, aspect ratio, and airfoil chord-to-tunnel height ratio. Using force transducers, the aerodynamic forces acting on the airfoils were measured directly for both increasing and decreasing α. The decreasing α measurements produce a higher lift–drag ratio than the increasing measurements, mostly in the post-stall region. In addition, “second stall,” was observed at large α in both the open and closed tunnels depending on the Re and tunnel blockage, but was much more prominent in the closed tunnel due to its wall constraining the wake and preventing the flow from switching from one regime to another. It was shown that the performance of all tested airfoils was sensitive to low Re under steady and unsteady conditions. The latter measurements for an airfoil oscillated at reduced frequencies, k≤0.06, are the first for CAAs. Results from this investigation provide a comprehensive airfoil dataset for the accurate blade element theory modeling of CAAs aerodynamic and starting performances.

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 score0.226
Threshold uncertainty score0.459

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

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