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Record W4205148641 · doi:10.2514/6.2022-2573

Dynamic Loading of an Aircraft Morphing Winglet

2022· article· en· W4205148641 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

VenueAIAA SCITECH 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWingtip deviceMorphingAerospace engineeringComputer scienceAeronauticsEngineeringWingComputer graphics (images)

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-2573.vid This paper introduces an aircraft morphing winglet and associated dynamic loading system. The morphing winglet was designed as a two six-bar branch parallel mechanism that converted the horizontal actuation in the wingbox to the vertical rotation of the winglet for the cant angle change. The two-branch parallel mechanism formed an actuation redundant system that is compact yet sufficient to morph the winglet under the required aerodynamic load. The dynamic loading system was designed as a single four-bar linkage that was able to apply a simulated point load at the aerodynamic center and maintain its perpendicularity to the winglet surface mimicking the lift force during morphing. The dynamic loading system formed the second actuation redundant system. The entire system was actuated hydraulically under the standard aircraft supply pressure. Discussed in this paper is the method proposed for the implementation of the dynamic loading test for the developed morphing winglet. There were two main control challenges: i) synchronization of the two branches within the morphing mechanism and ii) synchronization of the morphing mechanism and the dynamic loading system. Different from the traditional optimal distributed redundant system control methods, we proposed a master-slave control strategy by assigning a branch as the leader (master) and another as the follower (slave). Through thorough mechanism design synthesis, we obtained a quasi-linear relation between the actuator’s strokes and the winglet angle as well as a quasi-constant relation between the stroke speed and the winglet angular velocity (rate of morphing). Through judicious calibration, the follower was able to accurately synchronize with the leader to morph the winglet together. To prevent force fighting between the two branches, the control system was designed with several feedback sensors including the encoders on the winglet for misalignment measurement and the strain gauges on the critical links for stress monitoring. A gain-scheduling feedback control law was implemented to account for the non-linearity inherent in the flow control valve. Furthermore, the control of dynamic loading system was integrated by adding a loadcell at the application point. Another gain-scheduling feedback control system was implemented for the load control with the goal to maintain the tension on the winglet all the time during morphing in either direction of cant up or cant down. The entire integrated control system was programmed using LabVIEW to have both on-site and remote-control functions. Three control methods were tested: i) pure position, ii) position-velocity, iii) position-velocity-load. The first control test showed that the morphing mechanism indeed provided a linear relation between the stroke inputs and the winglet angles. The second control test revealed that the same mechanism indeed provided a quasi-relation between the stroke speeds and the winglet angular velocity (rate of morphing). The third control test demonstrated that the morphing mechanism was able to sustain the required load to the full amount.

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 score0.271
Threshold uncertainty score0.692

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)0.0010.000

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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