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Record W2322552448 · doi:10.2514/6.2015-2499

Control validation of a morphing wing in an open loop architecture

2015· article· en· W2322552448 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAIAA Modeling and Simulation Technologies Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsUniversité du Québec
FundersConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsMorphingComputer scienceArchitectureWingLoop (graph theory)Artificial intelligenceEngineeringAerospace engineeringMathematicsGeography

Abstract

fetched live from OpenAlex

Aircraft wings are generally designed and optimized to give the best possible performance for cruise flight conditions. Using conventional control surfaces such as flaps, ailerons, variable wing sweep and spoilers, the structure of aircraft wings is changed for other flight conditions. With the introduction of wing morphing, the flow over an aircraft’s wings can be modified locally to improve the overall wing and aircraft performance during the different flight steps. The goal of this research work is to develop an actuation control principle using a grid consisting of four similar miniature electromechanical actuators for a new morphing wing mechanism. The actuators modify the flexible upper surface of the wing so that the upper flow is modified and consequently the transition point from laminar to turbulence is delayed. The flexible upper wing surface is closed to the wing tip, while the skin is made of composite materials. The first actuation line is located at 32% and the second actuation line is at 48% of the chord. The actuators are fixed on the wing ribs and the top is attached to the flexible skin with screws. A database that relates the actuator displacements and the optimized skin is tailored for different flight conditions. A smart controller based fuzzy logic is designed to control the position of the actuator in real time so that the desired optimized skin corresponding to the desired displacements is obtained and maintained during the flight tests. The feasibility and the effectiveness of the control method are demonstrated experimentally.

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: none
Teacher disagreement score0.503
Threshold uncertainty score0.425

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 head0.066
GPT teacher head0.283
Teacher spread0.217 · 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