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Record W2410940196 · doi:10.1017/aer.2016.61

Analysis of UAS-S4 Éhecatl aerodynamic performance improvement using several configurations of a morphing wing technology

2016· article· en· W2410940196 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Aeronautical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMorphingWingAerodynamicsAngle of attackAerospace engineeringComputer scienceWing twistSolverLift-to-drag ratioActuatorDragLift (data mining)Structural engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT The paper presents the results of the aerodynamic optimisation of an Unmanned Aerial System's wing using a morphing approach. The shape deformation of the wing is achieved by placing actuator lines at several positions along its span. For each flight condition, the optimal displacements are found by using a combination of the new Artificial Bee Colony algorithm and a classical gradient-based search routine. The wing aerodynamic characteristics are calculated with an efficient nonlinear lifting line method coupled with a two-dimensional viscous flow solver. The optimisations are performed at angles of attack below the maximum lift angle, with the aim of improving the Hydra Technologies UAS-S4 wing lift-to-drag ratio. Several configurations of the morphing wing are proposed, each with a different number of actuation lines, and the improvements obtained by these configurations are analysed and compared.

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.

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 score0.323
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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