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Record W4402413337 · doi:10.1115/fedsm2024-123929

Effects of Aspect Ratio on Asymmetric Wake Flow Structure Around Right-Trapezoidal Prisms

2024· article· en· W4402413337 on OpenAlexaff
Aleyna Guney, Fati Bio Abdul-Salam, Amir Sagharichi, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWakeFlow (mathematics)MechanicsAspect ratio (aeronautics)Materials sciencePhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Abstract In this paper, the turbulent flow characteristics around right-trapezoidal prisms with varying streamwise aspect ratios (AR, ratio of upper length to prism height) of AR = 0.6, AR = 2, and AR = 2.5 in a uniform flow were investigated using particle image velocimetry (PIV). The Reynolds number (based on the free-stream velocity and body height) is Reh = 10000, and the results are analyzed in terms of the mean velocities, Reynolds stresses, and probability density function (PDF). The results show that two recirculation bubbles form in the wake region of all prisms. The bubbles are asymmetrical, with the upper recirculation bubble larger than the lower one. For the AR = 2.5, an additional bubble is observed on the upper surface of the prism. Contours of the streamwise Reynolds normal stress show dual peaks, which are located above and below the centerline in the wake region. The magnitude of the peak above the centerline is comparably higher for AR = 2.5 than for AR = 0.6 and 2.0. Conversely, the vertical Reynolds normal stress shows a single peak with increasing magnitude as the aspect ratio rises. Regardless of aspect ratio, the PDF of streamwise fluctuating velocity displays an unimodal distribution, while vertical velocity fluctuation shows a bimodal distribution at the point of maximum vertical fluctuating velocity.

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 score0.712
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.216
Teacher spread0.213 · 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
Published2024
Admission routes1
Has abstractyes

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