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Record W4281866781 · doi:10.2514/1.c036846

Aircraft Flutter and Aerodynamic Work

2022· article· en· W4281866781 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

VenueJournal of Aircraft · 2022
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsTransport Canada
Fundersnot available
KeywordsFlutterAeroelasticityAirspeedAerodynamicsAerodynamic forceAerospace engineeringControl theory (sociology)EngineeringWork (physics)Computer scienceStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

This paper is about an application of an energy approach to computational aeroelasticity. A frequency-domain calculation of aerodynamic work is presented in a form that has not been previously discussed. In large aeroelastic systems such as aircraft flutter models, the obtained expression allows us to quantify the roles played in flutter by the generalized coordinates, the phases between them, and the generalized aerodynamic forces. This is exemplified in a body-freedom flutter analysis of a flying-wing aircraft: the X-56A. Another interesting feature that is proposed is a diagram of the aerodynamic work as a function of airspeed. Such functions allow an observance of an evolution with the airspeed of the terms that are most reflective of the aeroelastic stability changes: for example, a phase between the two dominant modes. These diagrams can complement typical flutter trends in analysis documentation and aid in flutter suppression. The approach also permits a perspective on flutter as an interaction of aircraft surfaces rather than vibrational modes. Once a sensitivity of an aeroelastic eigenvalue to a surface area is measured with the presented approach, it can be used in the aircraft design to mitigate flutter and other undesirable aeroelastic responses associated with lightly damped eigenvalues. An illustration of this idea is provided. Finally, energy-based computations allow posing energy-efficient active flutter suppression problems. This has been presented before in the literature. Examples of this aspect are made here with aircraft models (for the first time, as far as the author knows).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.424

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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