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Record W2004998459 · doi:10.1115/fedsm-icnmm2010-30043

Non-Stationary Signal Analysis of the von Ka´rma´n Vortex Shedding in the Wake of a Fluttering Airfoil

2010· article· en· W2004998459 on OpenAlexafffund
Amin Fereidooni, Abhijit Sarkar, Dominique Poirel, Aze ́mi Benaissa, Vincent Me ́tivier, Guy Dumas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité LavalRoyal Military College of CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsU.S. Department of Defense
KeywordsHilbert–Huang transformVortex sheddingAirfoilStrouhal numberWakeInstantaneous phaseAngle of attackHilbert spectral analysisReynolds numberMathematicsMathematical analysisHilbert transformHarmonicAcousticsKármán vortex streetFourier transformPhysicsAerodynamicsComputer scienceMechanicsSpectral densityEnergy (signal processing)Filter (signal processing)TurbulenceStatistics

Abstract

fetched live from OpenAlex

Stationary data lend themselves well to the Fourier decomposition into harmonic components. Conversely, spectral characteristics of non-stationary data vary with time, and hence do not generally admit the application of Fourier transform. In order to investigate the localized time-frequency characteristics of non-stationary data, the notions of instantaneous frequency and amplitude are invoked. These concepts are applied to the von Ka´rma´n vortex shedding observed in the wake of a self-sustained pitching airfoil. For this range of Reynolds numbers (104 – 105), it has been reported that at any given airspeed the shedding frequency of the vortex street varies with angle of attack (AOA), ranging from the Strouhal number St ≈ 0.6 at zero AOA and tending to St ≈ 0.1 for high AOA. For the pitching motion, which originates from a positive energy transfer from the flow to the airfoil due to negative aerodynamic damping, the von Ka´rma´n vortex shedding frequency varies with pitch angle hence with time. Hilbert transform provides a robust estimate of instantaneous frequency through the definition of analytic signals. However, Hilbert transform provides meaningful instantaneous frequency for only monocomponent signals. To overcome this difficulty, the Hilbert-Huang transform is commonly exploited. In this paper, both the Hilbert and Hilbert-Huang transforms are applied in order to capture the instantaneous vortex shedding frequency. For multicomponent signals Empirical Mode Decomposition (EMD) splits the signal to monocomponent signals, namely Intrinsic Mode Functions, through a so-called sifting process. Application of Hilbert transform to these functions produces instantaneous frequencies and amplitudes. Therefore the time-frequency-amplitude representation of the signal appears to be a promising tool for obtaining more physical insight into the time-varying vortex shedding frequency in the wake of a pitching airfoil.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2010
Admission routes2
Has abstractyes

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