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Record W6980314263

Boundary layer behaviour on a morphing airfoil: simulation and wind tunnel tests

2009· article· en· W6980314263 on OpenAlexfundvenueno aff

Bibliographic record

VenueNPARC · 2009
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsAirfoilMorphingSwept wingLaminar flowAerodynamicsWingDragNACA airfoilAngle of attackWind tunnel
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the boundary layer transitional flow behaviour past a morphing experimental wing. The objective of the present investigation is to reduce aerodynamic drag through laminar-turbulent transition location delay to promote large laminar region on the wing surface. The airfoil shape configurations of the adaptable part of the wing were optimized to extend the laminar flow on the upper surface of the airfoil. The optimizations were preformed with two different approaches. The classical approach uses a mathematical curve to model the flexible part. The multidisciplinary approach integrates the finite element model of the adaptable wing structure into the aerodynamic optimization process. The latter approach avoids the difficult task of reproducing the geometric representations of the airfoils, leading to the optimum performances offered by the morphing wing system. The optimizations are performed for various subsonic incompressible regimes using the computational fluid dynamics code Xfoil (Drela 2006) and the structural analysis software ANSYS coupled with a genetic optimization algorithm (Genial v1.1). Simulation and experimental results are presented and show that the morphing system is able to delay the transition location downstream by up to 30% of the chord length and to reduce the airfoil drag by up to 22%. Infra red maps and unsteady pressure frequency spectra measured on of the upper surface of the wing are also presented and show the laminar run extension.

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 score0.266
Threshold uncertainty score0.371

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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