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Record W3036490558 · doi:10.1186/s42774-020-00038-7

Effects of aspect ratio and inclination angle on aerodynamic loads of a flat plate

2020· article· en· W3036490558 on OpenAlexafffund
Mehrdad Shademan, Arash Naghib-Lahouti

Bibliographic record

VenueAdvances in Aerodynamics · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAspect ratio (aeronautics)WakeReynolds numberChord (peer-to-peer)VortexMechanicsVortex sheddingInclination angleGeometryDragLift (data mining)Flow separationLift-to-drag ratioAerodynamicsOpticsVortex liftAngle of attackFlow visualizationMaterials scienceLift coefficientFlow (mathematics)PhysicsMathematicsTurbulenceComposite material

Abstract

fetched live from OpenAlex

Abstract Large Eddy Simulations are carried out to analyze flow past flat plate in different configurations and inclinations. A thin flat plate is considered at three inclination angles (α = 30°, 60° and 90°) and three aspect ratios (AR = 0.5, 2 and 5). The Reynolds number based on the free stream velocity and chord length of the plate at different inclination angles varies between 75,000 to 150,000. An increase in the inclination angle while the aspect ratio (span to chord) is constant results in higher drag and lower lift on the plate. Increasing the aspect ratio at a constant inclination angle increases the mean aerodynamic loading except for the α = 30° and AR = 0.5 case where the mean forces are larger than the other aspect ratios for this specific inclination angle. The small aspect ratio suppresses and blocks the separation of the flow from the top and bottom edges causing larger aerodynamic forces relative to AR = 2, 5. Visualization of the flow structures shows the tip vortices have a significant role in controlling the shedding vortices from the top and bottom edges. At α = 30° and AR = 0.5, the two tip vortcies control and suppress the flow separation from the top and bottom edges. A stable wake was found for this case with no fluctuation. As the aspect ratio increases, the influence of the tip vortices on flow separation from the top and bottom edges reduces. As a result, larger fluctuations were found for cases with higher aspect ratios.

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 score0.059
Threshold uncertainty score0.717

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 head0.202
Teacher spread0.199 · 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

Citations32
Published2020
Admission routes2
Has abstractyes

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