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Record W2105940130 · doi:10.1177/1077546307081315

Aeroservoelasticity Analysis Method Based on an Error Analytical Form Applied on a Business Aircraft

2008· article· en· W2105940130 on OpenAlexaff
Djallel Eddine Biskri, Ruxandra Mihaela Botez, Nicholas Stathopoulos, Sylvain Thérien, M. Dickinson, A. Rathe

Bibliographic record

VenueJournal of Vibration and Control · 2008
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsBombardier (Canada)École de Technologie Supérieure
Fundersnot available
KeywordsAeroelasticityAerodynamicsAerodynamic forceFlutterLaplace transformMathematicsControl theory (sociology)Structural engineeringComputer sciencePhysicsMechanicsMathematical analysisEngineering

Abstract

fetched live from OpenAlex

Aeroservoelasticity is a multidisciplinary study that combines the following disciplines: aerodynamics, aeroelasticity and servo-controls. For aeroelasticity studies, the Doublet Lattice Method (DLM) is used to calculate the aerodynamic unsteady forces for a set of reduced frequencies k and Mach numbers M on a business aircraft in the subsonic flight regime. There are three classical methods in the aeroservoelasticity used to approximate these forces Q (k, M) by rational functions in the Laplace domain Q(s): Least Square (LS), Matrix Padé (MP) and Minimum State (MS). A new method called Corrected Least Square (CLS) is presented. This new method uses an analytical form of the error as a function of Laplace variable similar to the analytical form of the aerodynamic forces calculated by use of the LS method. The new CLS method does not take additional time to the computation of the unsteady aerodynamic forces when compared to the LS method, and the aerodynamic forces calculated with the CLS method are closer to the aerodynamic forces data in the frequency domain than the aerodynamic forces calculated by the standard LS method. The new CLS method applied on a business aircraft gives better results (flutter speeds and frequencies) and faster (as it uses smaller number of lag terms) than the LS method.

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: none
Teacher disagreement score0.894
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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