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Record W2808960413 · doi:10.22214/ijraset.2018.3331

Flutter Analysis of Typical Aircraft Wing using Doublet Lattice Method

2018· article· en· W2808960413 on OpenAlexaff
Chetan Kumar

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsFlutterWingAeronauticsAerospace engineeringStructural engineeringPhysicsComputer scienceMechanicsEngineeringAerodynamics

Abstract

fetched live from OpenAlex

Flutter is a dynamic instability. Flutter occurs at a velocity when the aerodynamic forces balances the elastic forces, beyond which the exciting forces exceed the restoring forces and the amplitude of disturbance will grow without limit. Thus flutter is a self-excited vibration. Wing flutter is the one that is most often encountered and the most important, among the types of flutter categorized as lifting surface flutter. In the simplest kind of example of binary flutter, known as flexure-torsion flutter, the only motion of importance are bending and twisting of the wing, wherein it is sufficient to take one generalized co-ordinate for flexure and one for torsion. The objective of the present work is to find on set of dynamic instability in particular, flutter for a typical wing structure using commercial code doublet lattice method in MSC/NASTRAN. And also studying the parametric sweep angles of wing and their flutter characteristics. The FE Modeling will be done in HYPERMESH and the flight load module of the MSC/PATRAN will be used for aero mesh generation. The flutter module of the MSC/NASTRAN is used as the solver.

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)0.002
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 score0.392
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.405
Teacher spread0.356 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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