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Record W2326964213 · doi:10.1115/imece2009-12998

Multi-Tuned Optimum Vibration Absorbers for Curved Beams

2009· article· en· W2326964213 on OpenAlexafffund
Fan Yang, Ramin Sedaghati, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsOntario Tech UniversityConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTuned mass damperVibrationSequential quadratic programmingFinite element methodBeam (structure)StiffnessOptimal designDamperVibration controlStructural engineeringControl theory (sociology)Genetic algorithmPosition (finance)Quadratic programmingComputer scienceEngineeringMathematicsPhysicsAcousticsMathematical optimization

Abstract

fetched live from OpenAlex

Detailed investigations on the vibration suppression of beam-type structures using Multiple Tuned Mass Damper (MTMD) technology has been carried out in this study. A general curved beam has been utilized as a case study to illustrate the developed optimum design methodology. The governing differential equations of motion for the curved beam with the attached MTMD systems have been derived, and then solved using the finite element method. A hybrid optimization methodology, which combines the global optimization method based on Genetic Algorithm (GA) and the local optimization technique based on Sequential Quadratic Programming (SQP), has been developed. This has been utilized to find the optimum design parameters (damping coefficient, spring stiffness and position coordinate) of the attached Tuned Mass Damper (TMD) systems in order to suppress the vibration levels at a particular mode or several modes, simultaneously. Finally, a design principle for vibration suppression of beam-type structures using the MTMD technology has been proposed through extensive numerical investigations.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.298

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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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