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Actuation System Design for a Morphing Aileron

2015· article· en· W2240817763 on OpenAlexfundaboutno aff
Gianluca Amendola, Ignazio Dimino, Antonio Concilio, Rosario Pecora, Francesco Amoroso

Bibliographic record

VenueApplied Mechanics and Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsnot available
FundersConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsAileronMorphingTrailing edgeEngineeringActuatorAerodynamicsMechanism (biology)HingeMechanical engineeringAerospace engineeringComputer scienceStructural engineering

Abstract

fetched live from OpenAlex

In the field of European and International morphing structures projects, the CRIAQ MD0-505 enables collaboration among Italian and Canadian research centers and industries paying particular attention to the development of innovative design in the area of adaptive technologies. The main project goals involve the design of a wing trailing edge device capable to improve aerodynamic efficiency in all the flight envelope leading to fuel consumption reduction with positive impact on aircraft weight. This paper deals with the design and modeling of a novel actuation system of a full scale morphing aileron for a regional aircraft wing. The proposed aileron architecture is characterized by segmented adaptive ribs. Each rib is composed of two movable blocks connected by means of rotational hinges in which are housed bearings and bushing in a finger-like mechanism. Rib actuation is guaranteed by an actuation system composed of a dedicated kinematic chain derived from a quick-return mechanism. In order to achieve the aileron target shapes, the system is driven by a set of servo-rotary electro mechanical actuators that permit a highly integrated design which lay the groundwork for the technological transition from the torque shaft to the distributed actuation architecture.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

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)0.0030.000

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 head0.033
GPT teacher head0.207
Teacher spread0.174 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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