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Record W2320448628 · doi:10.2514/6.2016-1082

Self-adaptive morphing wing model, smart actuated and controlled by using a multiloop controller based on a laminar flow real time optimizer

2016· article· en· W2320448628 on OpenAlexafffund
Teodor Lucian Grigorie, Ruxandra Mihaela Botez, Andrei Vladimir Popov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsUniversité du Québec
FundersConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsMorphingComputer scienceLaminar flowControl theory (sociology)Controller (irrigation)Flow (mathematics)WingControl engineeringEngineeringAerospace engineeringArtificial intelligenceControl (management)Mathematics

Abstract

fetched live from OpenAlex

The modeling, the design, the numerical simulation and the experimental testing of the control system for a self-adaptive morphing wing model are here exposed. The study was performed during a multidisciplinary research project, involving industrial partners, a research institute and three academic entities. The developed control system is a multiloop one, being designed, simulated and tested in two major steps, correlated with the validation phases of the aerodynamic gains provided by the morphed wing model in terms of the laminar flow improvement over its upper surface. The two validation phases were suggestively called open loop, respectively closed loop; in the first phase the aerodynamic validation was made just by comparing the experimentally obtained results with the numerical optimization obtained ones, while in the second phase the morphing wing model was left free, to adapt itself based on the information related to the transition point position provided by some pressure sensors installed on its upper surface. The used wing model was a rectangular one, equipped with a composite made flexible upper surface, morphed along of two lines by using some shape memory alloy actuators. For the open loop phase a database with some optimized airfoils was generated and a smart controller based fuzzy logic was designed to control the position of the actuators in real time so that the desired optimized skin corresponding to the desired displacements to be obtained and maintained during the flight tests. The closed loop architecture was realized by using a real-time optimization algorithm, which included the actuators controller as inner loop. The algorithm was developed in order to generate real-time optimized airfoils starting from the information received from the pressure sensors and targeting the morphing wing main goal: the improvement of the laminar flow over the wing upper surface.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2016
Admission routes2
Has abstractyes

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