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Record W2126972453 · doi:10.1115/detc2005-85312

Stability, Bifurcation and Jumping Phenomenon in a 2-D Model of Supersonic Lifting Surfaces

2005· article· en· W2126972453 on OpenAlexaff
Pei Yu, Zhe Chen, Liviu Librescu, Pier Marzocca

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsFlutterAeroelasticityControl theory (sociology)Center manifoldNonlinear systemAerodynamicsMathematicsBifurcationSupersonic speedLimit (mathematics)Hopf bifurcationStability (learning theory)Boundary (topology)PhysicsMathematical analysisMechanicsComputer scienceControl (management)

Abstract

fetched live from OpenAlex

This paper is concerned with the linear/nonlinear aeroelastic control of 2-D supersonic lifting surfaces. Its goal is to provide the feedback control mechanism enabling one to enlarge the flight envelope by increasing the flutter speed, and also to control the character, benign/catastrophic of the flutter instability boundary. Structural and aerodynamic nonlinearities are included in the aeroelastic governing equations, and linear and nonlinear feedback controls in both plunging and pitching are employed in conjunction with proportional velocity feedback controls. The attention of the paper is focused on multiple Hopf bifurcations. In particular, the jumping phenomenon found in our previous work will be further investigated to reveal the physical implications. It is found that such a jumping occurs when the system has multiple families of limit cycles bifurcating from a same set of parameter values with multiple solutions for frequencies. The case investigated in this paper is restricted to zero structure damping. Center manifold reduction and normal form theory are applied to consider the stability of post-flutter solutions and the associated jumping phenomenon. Numerical simulations are presented to show the implications of time delay in the considered controls.

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 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 score0.022
Threshold uncertainty score0.260

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 head0.014
GPT teacher head0.199
Teacher spread0.185 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2005
Admission routes1
Has abstractyes

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