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Record W2315192578 · doi:10.1115/fedsm-icnmm2010-30536

Experimental and Numerical Study of the Three Dimensional Instabilities in the Wake of a Blunt Trailing Edge Profiled Body

2010· article· en· W2315192578 on OpenAlexaff
Arash Naghib Lahouti, Lakshmana Sampat Doddipatla, Horia Hangan, Kamran Siddiqui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsWakeReynolds numberTrailing edgeMechanicsVortexFlow visualizationPhysicsParticle image velocimetryGeometryFlow (mathematics)TurbulenceMathematics

Abstract

fetched live from OpenAlex

The wake of nominally two dimensional bluff bodies is dominated by von Ka´rma´n vortices, which are accompanied by three dimensional instabilities beyond a threshold Reynolds number. These three dimensional instabilities initiate as dislocations in the von Ka´rma´n vortices near the trailing edge, which evolve into pairs of counter-rotating vortices further downstream. The wavelength of the three dimensional instabilities depends on profile geometry and Reynolds number. In the present study, the three dimensional wake instabilities for a blunt trailing edge profiled body, composed of an elliptical leading edge and a rectangular trailing edge, have been studied in Reynolds numbers ranging from 500 to 1200, based on the thickness of the body. Numerical simulations, Laser Induced Fluorescence (LIF) flow visualization, and Particle Image Velocimetry (PIV) methods have been used to identify the instabilities. Proper Orthogonal Decomposition (POD) has been used to analyze the velocity field data measured using PIV. The results confirm the existence of three dimensional instabilities with an average wavelength of 2.0 to 2.5 times thickness of the body, in the near wake. The findings are in agreements with the values reported previously for different Reynolds numbers, and extend the range of Reynolds numbers in which the three dimensional instabilities are characterized.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.136

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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