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Record W2106715796 · doi:10.1115/pvp2007-26006

Nonlinear Aeroelasticity Modeling Using a Reduced Order Model Based on Proper Orthogonal Decomposition

2007· article· en· W2106715796 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

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAeroelasticityTransonicComputational fluid dynamicsNonlinear systemModel order reductionProper orthogonal decompositionApplied mathematicsControl theory (sociology)AerodynamicsComputer scienceTurbulenceMechanicsMathematicsPhysicsAlgorithm

Abstract

fetched live from OpenAlex

Investigations of nonlinear aeroelasticity of flexible structures subjected to unsteady transonic flows were carried out by means of an aeroelasticity model coupled with a reduced order CFD model based on POD (proper orthogonal decomposition) method. The reduced order model is a three-dimensional with moving fluid boundaries. The CFD model order was reduced from more than 150000 of the full order model to 200 of the reduced order model and Limit Oscillation Cycle (LCO) was observed. The dynamic responses of the system were simulated with the coupled model. Qualitatively, the numerical simulations on AGARD 445.6 from the nonlinear aeroelasticity model coupled with the reduced order CFD model agree with those from the model coupled with the full order CFD model.

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: none
Teacher disagreement score0.474
Threshold uncertainty score0.572

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 head0.036
GPT teacher head0.308
Teacher spread0.272 · 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

Quick stats

Citations9
Published2007
Admission routes1
Has abstractyes

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