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Record W4401543396 · doi:10.2514/1.j063884

Validated Dynamic Stall Simulation of Pitching Low Reynolds Number Airfoils

2024· article· en· W4401543396 on OpenAlexafffund
Alison Zilstra, David A. Johnson

Bibliographic record

VenueAIAA Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStall (fluid mechanics)AirfoilLeading edgeReynolds numberMechanicsAngle of attackLift coefficientPhysicsWind tunnelFlow separationAerodynamicsBoundary layerMathematicsTurbulence

Abstract

fetched live from OpenAlex

Deep dynamic stall is one of several complex behaviors that result in extreme variation of the aerodynamic loads on small wind turbine (SWT) blades during unsteady wind conditions. In this study, unsteady Reynolds-averaged Navier–Stokes simulations are performed for two low Reynolds number (Re) airfoils where sinusoidal pitching is applied to replicate the dynamic stall that occurs on rotating SWT blades. The SD 7037 airfoil is simulated at [Formula: see text] and a pitching reduced frequency of [Formula: see text], and the S833 airfoil is at [Formula: see text] and [Formula: see text]. The simulated lift coefficient and dynamic stall timing agree with experimental data, which is attributed to the wall-normal resolution of the mesh and is an advancement from the early prediction of stall seen consistently in previous numerical studies. The accurate prediction of dynamic stall is found to be dependent on the correct simulation of the bursting of the laminar separation bubble (LSB), which initiates the complete separation of the boundary layer and the formation of a leading-edge vortex. The [Formula: see text] [Formula: see text] model combined with the use of a fine mesh at the airfoil leading edge results in an accurate simulation of the bursting LSB and the correct prediction of the deep dynamic stall.

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 score0.085
Threshold uncertainty score0.479

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 head0.005
GPT teacher head0.239
Teacher spread0.234 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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