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Record W4243117390 · doi:10.22215/etd/2017-11781

Nonlinear Aeroelastic Modeling of a Flexible Wing and Comparison with Experiments

2017· dissertation· en· W4243117390 on OpenAlexafffund
Leandro Rocha Da Costa

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsCarleton University
FundersMitacs
KeywordsFlutterAirfoilAeroelasticityAerodynamicsInviscid flowAmplitudeNonlinear systemMechanicsLaminar flowReynolds numberWind tunnelPhysicsAerodynamic forceLimit cycleMathematicsClassical mechanicsTurbulenceOptics

Abstract

fetched live from OpenAlex

A wind tunnel experimental investigation of limit cycle oscillations (LCO) of a uniform flexible airfoil with root pitch motion occurring at transitional Reynolds number regime (4.510 4 Re c 1.3 10 5 ) is presented. Depending on the initial conditions, two stable limit cycle regimes are observed: small and large amplitude LCO. The origin of the large amplitude LCO is determined to be coalescence flutter for which the exponential growth of the amplitude is limited by the stall at large angles of attack. On the other hand, the small amplitude LCO are attributed to laminar boundary layer separation related to transitional Reynolds number aerodynamics. In addition, the nonlinear equations of motion for a cantilever is developed considering chordwise and flapwise bending, torsion and base rotation. The nonlinearities arise from two main sources: the structural flexibility and the coupling of the bending and torsional motions with the base rotation. Furthermore, a linear inviscid aerodynamic model is considered using both quasisteady and unsteady forcing terms. Despite the linear aerodynamics approximations, a parallel to the experiments can be drawn with the numerical simulations presented for small and large amplitude LCO at the vicinity of the linear flutter speed, with a dominant cause factor being a coalescence flutter.

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 score0.054
Threshold uncertainty score0.725

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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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