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Record W2114191893 · doi:10.3970/icces.2011.020.071

Theory, Analysis and Design of Fluid-Shell Structures

2011· article· en· W2114191893 on OpenAlexaff
A. A. Lakis, M. H. Toorani, Youcef Kerboua, Mitra Esmailzadeh, Farhad Sabri

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFlutterShell (structure)Finite element methodVibrationAeroelasticityIsotropyMechanicsBoundary value problemStructural engineeringFluid–structure interactionNonlinear systemMaterials scienceEngineeringMechanical engineeringAerodynamicsPhysicsAcousticsOptics

Abstract

fetched live from OpenAlex

Summary This paper review focuses mainly on development and application of a hybrid finite element approach used for linear and geometrically nonlinear vibration analysis of isotropic and anisotropic plates and shells, with and without fluid-structure interaction. Development of a hybrid element for different geometries of plates and shells is briefly discussed. In addition, studies dealing with particular dynamic problems such as dynamic stability and flutter of plates and shells coupled to flowing fluids are also discussed. This paper is structured as follows: after a short introduction on some of the fundamentals of the developed model applied to vibrations analysis of shells and plates in vacuo and in fluid, the dynamic analysis of anisotropic structural elements is discussed. Studies on dynamic response of plates in contact with dense fluid (submerged and/or subjected to liquid) follow. These studies present very interesting results that are suitable for various applications. Dynamic response of shell type structures subjected to random vibration due to a turbulent boundary layer of flowing fluid is reviewed. Aeroelasticity analysis of shells and plates (including the problem of stability; divergence and flutter) in contact with light fluids (gases) are also discussed.

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: Methods · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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
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

Citations3
Published2011
Admission routes1
Has abstractyes

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