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Record W3202539074 · doi:10.3390/biomimetics6040054

New Aerodynamic Studies of an Adaptive Winglet Application on the Regional Jet CRJ700

2021· article· en· W3202539074 on OpenAlexafffund
Marine Segui, Federico R. Abel, Ruxandra Mihaela Botez, Alessandro Ceruti

Bibliographic record

VenueBiomimetics · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsWingtip deviceAerodynamicsLift-induced dragLift coefficientPitching momentDragLift-to-drag ratioClimbAerospace engineeringWingLift (data mining)Computational fluid dynamicsComputer scienceMechanicsStructural engineeringPhysicsEngineeringAngle of attackTurbulenceReynolds number

Abstract

fetched live from OpenAlex

This study aims to evaluates how an adaptive winglet during flight can improve aircraft aerodynamic characteristics of the CRJ700. The aircraft geometry was slightly modified to integrate a one-rotation axis adaptive winglet. Aerodynamic characteristics of the new adaptive design were computed using a validated high-fidelity aerodynamic model developed with the open-source code OpenFoam. The aerodynamic model successively uses the two solvers simpleFoam and rhoSimpleFoam based on Reynold Averaged Navier Stokes equations. Characteristics of the adaptive winglet design were studied for 16 flight conditions, representative of climb and cruise usually considered by the CRJ700. The adaptive winglet can increase the lift-to-drag ratio by up to 6.10% and reduce the drag coefficient by up to 2.65%. This study also compared the aerodynamic polar and pitching moment coefficients variations of the Bombardier CRJ700 equipped with an adaptive versus a fixed winglet.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 head0.050
GPT teacher head0.295
Teacher spread0.245 · 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 designBench or experimental
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

Citations22
Published2021
Admission routes2
Has abstractyes

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