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Record W2937057795 · doi:10.1063/1.5079634

Wake dynamics and surface pressure variations on two-dimensional normal flat plates

2019· article· en· W2937057795 on OpenAlexafffund
Arman Hemmati, David Wood, Robert J. Martinuzzi

Bibliographic record

VenueAIP Advances · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundAlberta Innovates - Technology Futures
KeywordsVortex sheddingVortexWakeMechanicsPhysicsReynolds numberDragCompressibilitySurface pressureKármán vortex streetPressure gradientClassical mechanicsTurbulence

Abstract

fetched live from OpenAlex

The relationship between vortex dynamics and surface pressure fluctuations on the leeward face of a two-dimensional normal thin flat plate was studied using Direct Numerical Simulations for incompressible flow at a Reynolds number of 1200. The vortex shedding frequency was observed in the spectra of pressure fluctuations on both faces of the plate, while a lower frequency spectral peak was only evident on the leeward pressure fluctuations. Local sharp peaks of low pressure in the separated shear layers coincide with increases in the leeward face pressure fluctuations. These observations are tied to the vortex dynamics using the invariant Q, commonly used for vortex identification. Q is also proportional to the source term for the Poisson equation for the instantaneous pressure. The pressure, which is the only contributor to the plate drag, varies significantly in response to alterations to the vortex shedding, which can be observed in differences of vortex trajectories. At minimum drag, the pressure fluctuations are small, which is attributed to lower values of Q associated with weaker vortices.

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 score0.020
Threshold uncertainty score0.380

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 head0.003
GPT teacher head0.201
Teacher spread0.198 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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