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Record W2092696443 · doi:10.1115/fedsm-icnmm2010-30386

Effect of Fluid Flow Nonlinearities on the Dynamic Behaviour of Cylindrical Shells Subjected to a Supersonic Flow

2010· article· en· W2092696443 on OpenAlexaff
Redouane Ramzi, A. A. Lakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAerodynamicsSupersonic speedMechanicsShell (structure)Nonlinear systemFlutterPhysicsVibrationChoked flowFinite element methodAeroelasticityCompressible flowEquations of motionClassical mechanicsMaterials scienceCompressibilityAcoustics

Abstract

fetched live from OpenAlex

An analytical model is presented to predict the influence of nonlinearities associated with supersonic fluid flow on the dynamic and stability behavior of thin isotropic cylindrical shells. The method developed is a combination between finite element method, sander’s shell theory and nonlinear aerodynamic theory (third-order piston theory). The shell is subdivided into cylindrical finite elements, the displacements functions are derived from exact solutions of Sanders equations for thin cylindrical shells and the influence of stress stiffening due to internal or external pressure and axial compression is also taken into account. Expressions for the masse and stiffness matrices are determined by exact analytical integration. With the nonlinear dynamic pressure, we develop nonlinear matrices: stiffness, damping and coupling matrices for flow. The nonlinear equation of motion is then solved using a fourth-order Runge-kutta numerical method. Frequency variations are determined with respect to the amplitude of the motion for different cases. This is a powerful model to predict linear, nonlinear vibrations and stability characteristics of cylindrical shells subjected to external supersonic flow that can be applied for the aeroelastic design of aerospace vehicles.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.213
Teacher spread0.210 · 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
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
Published2010
Admission routes1
Has abstractyes

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