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Record W3144726955 · doi:10.1002/we.2627

Modeling dynamic loads on oscillating airfoils with emphasis on dynamic stall vortices

2021· article· en· W3144726955 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWind Energy · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Calgary
FundersLuonnontieteiden ja Tekniikan Tutkimuksen Toimikunta
KeywordsStall (fluid mechanics)AirfoilAerodynamicsMechanicsVortexChord (peer-to-peer)Aerodynamic forceVortex sheddingPitching momentControl theory (sociology)PhysicsComputer scienceAngle of attackReynolds number

Abstract

fetched live from OpenAlex

Abstract We present a modified version of the ONERA dynamic stall model for improving the prediction of the unsteady forces and load overshoots generated by the shedding of dynamic stall vortices. The modifications include modeling the chord‐axis forces instead of the wind‐axis forces used originally. A novel approach for defining the onset of a dynamic stall is based on the behavior of the chordwise force without correlating the onset empirically. Overshoots in the unsteady aerodynamic loads caused by vortex shedding are modeled by sine‐shaped functions added to the normal force and moment. The onset and duration of these pulses are empirically described in the time domain for convenient use in time‐marching simulations. The modified dynamic stall model is calibrated using a genetic algorithm and compared to experimental data of different airfoils relevant to wind turbine applications. The results show an excellent correlation with the experimental data, particularly in deep dynamic stall, which are characterized by large fluctuations in the aerodynamic loads.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

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 head0.194
Teacher spread0.188 · 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