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Record W4402849295 · doi:10.2514/1.j063953

Wake Three-Dimensionality of Profiled Blunt Trailing Edge Bodies with Varying Chord Length

2024· article· en· W4402849295 on OpenAlexafffund
Ross Cruikshank, Wenyi Zhao, Philippe Lavoie

Bibliographic record

VenueAIAA Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWakeTrailing edgeChord (peer-to-peer)Angle of attackBluntMechanicsGeometryPhysicsGeologyStructural engineeringMathematicsMaterials scienceComputer scienceAerodynamicsEngineering

Abstract

fetched live from OpenAlex

This paper investigates experimentally the structure of turbulent blunt trailing edge body wakes with varying boundary-layer thicknesses controlled through the freestream velocity and the chord length. The intrinsic effect of transition to turbulence on the vortex-shedding frequency, strength, and its three-dimensional structure is explored. At large-scales, the vortex shedding is characterized by significant phase variations along the span, which vary stochastically and are punctuated by vortex dislocations when the phase differences grow large. These features of the vortex shedding are quantitatively examined in a streamwise–spanwise plane of the wake with particle image velocimetry. When the point of transition shifts upstream due to an increasing Reynolds number, greater phase variations in the vortex shedding along the span and more frequent dislocation events are produced. These changes are linked to the spanwise correlation of the streamwise velocity in the wake. In the turbulent boundary-layer regime, it is found that the phase drift does not change significantly. The decline in the spanwise correlation with increasing boundary-layer thickness in this regime is instead linked to the relative strength of the vortex shedding compared to the random turbulent fluctuations in the wake.

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 score0.187
Threshold uncertainty score0.377

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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
Published2024
Admission routes2
Has abstractyes

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