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Record W4312418271 · doi:10.1115/fedsm2022-87650

Turbulent Characteristics of the Wake Flow Around Rectangular and Trapezoidal Prisms in Uniform Flow

2022· article· en· W4312418271 on OpenAlexaff
Jinhao Kang, Sedem Kumahor, Amir Sagharichi, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWakeReynolds numberPrismTurbulenceParticle image velocimetryFreestreamGeometryMechanicsFlow (mathematics)OpticsPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract The turbulent characteristics of flow separation induced by a trapezoidal and two rectangular prisms immersed in a uniform flow were studied experimentally using particle image velocimetry. The streamwise aspect ratios (AR) of the two rectangular prisms were 3 and 4. The streamwise AR of the trapezoidal prism was based on its upper length and was 3. The Reynolds number based on the freestream velocity and the prism height was 14700. The flow topology of symmetrical wake flows over and behind the rectangular prisms and asymmetrical wake flow over and behind the trapezoidal prism were analyzed using the mean flow and Reynolds stresses. The results indicate that the mean flow reattaches onto the rectangular prisms but does not reattach onto the trapezoidal prism. Dual local regions of elevated streamwise Reynolds normal stress occur in the wake region irrespective of AR and the body geometry. However, the dual elevated regions are only symmetric about the horizontal centerline of two rectangular prisms. Probability density function (PDF), two-point correlations and proper orthogonal decomposition (POD) analyses were employed to investigate the dynamics of the coherent structures formed over and behind the prisms.

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 score0.049
Threshold uncertainty score0.227

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 head0.004
GPT teacher head0.171
Teacher spread0.166 · 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
Published2022
Admission routes1
Has abstractyes

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