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Record W2735149808 · doi:10.2514/1.j054849

Three-Dimensional Aeroelastic Solutions via the Nonlinear Frequency-Domain Method

2017· article· en· W2735149808 on OpenAlexafffund
Pierre-Olivier Tardif, Siva Nadarajah

Bibliographic record

VenueAIAA Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsAeroelasticityFlutterSolverNonlinear systemFrequency domainFinite element methodNewton's methodAerodynamicsAirfoilComputer scienceApplied mathematicsMathematicsControl theory (sociology)Mathematical analysisMathematical optimizationPhysicsMechanicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

An aeroelastic solver is developed using a nonlinear frequency domain flow solver coupled to a plate-bending finite element linear structural solver. A methodology for determining the flow conditions leading to flutter and limit-cycle oscillations is proposed, based on a root-finding Newton–Raphson iterative method. The novelty of the approach lies in the constant size of the Newton–Raphson system of equations, regardless of the number of degrees of freedom of the structural model. To serve this purpose, a new method of computing mesh velocities for nonlinear frequency-domain flow solvers is developed, and a technique for solving the geometric conservation law within the nonlinear frequency-domain framework is presented accordingly. The new approach for computing mesh velocities is validated against existing experimental data on the Lockheed, Air Force, NASA and Netherlands wing (run 73, CT5), whereas the aeroelastic solver is validated via experimental results of the AGARD I.-wing weakened model 3 and solid model 2 in air and R-12, respectively. The proposed framework is expected to perform limit-cycle oscillation computations an order of magnitude faster than a typical aeroelastic time-marching approach.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.646
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.013
GPT teacher head0.250
Teacher spread0.236 · 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.

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

Citations17
Published2017
Admission routes2
Has abstractyes

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