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Record W2555579567

Computational Prediction of Tip-Vortex of aSwept Wing

2013· article· en· W2555579567 on OpenAlexaboutno aff
Sangram Keshari Samal, Dilip A. Shah, M Bharath, Abhinash Sahoo

Bibliographic record

VenueInternational Journal of Innovative Research in Science Engineering and Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
Fundersnot available
KeywordsVortexComputational fluid dynamicsMechanicsWingAngle of attackTrailing edgePhysicsWind tunnelSwept wingFlow (mathematics)Horseshoe vortexWingtip vorticesCirculation (fluid dynamics)Vortex ringGeometryAerodynamicsMathematicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

The near-field flow structure of a wing tip vortex behind a sweptback and tapered NACA 0015 wing was investigated using Computational Fluid Dynamics (CFD) code at Re of 1.81 x 105. In this paper, Numerical simulations of the tip vortices was carried out at a geometric angle of attack 8° and the numerical results of tangential velocity of tip vortex is compared with the experimental result done by P. Gerontakos and T. Lee at McGill University low speed wind tunnel at the velocity equal to 35 m/s. The numerical result that was obtained by using CFD code shows a good agreement with the experimental result. The coordinates of the vortex-core location are closely captured along the downstream of flow and the variation of the vortex flow quantities with different angle of attack are investigated. Also circulation at three different downstream location of the trailing edge are calculated

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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