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Record W2071271841 · doi:10.1115/detc2010-28251

Vibration Control of Vehicles With Flexible Structure Using Optimum Tuned-Mass Dampers

2010· article· en· W2071271841 on OpenAlexaff
Avesta Goodarzi, Neda Pandarathil, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsTuned mass damperVibrationFlexibility (engineering)Vibration controlDamperStructural engineeringEngineeringComputer scienceAutomotive engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Vibrations resulting from flexural motion of elastic vehicle structures are of particular significance to the ride comfort of vehicles. Tuned-mass dampers (dynamic vibration absorbers) are among the most commonly used devices for vibration suppression. In this paper the application of optimum tuned-mass dampers is addressed, that can be used to reduce annoying flexibility originated vibration. A newly developed flexible model is representing the vehicle with eight-degree-of-freedom (8-DOF) with a systematic approach to select the optimum locations of the absorbers. Using passive vibration absorbers and applying the min-max optimization method to find the optimum values of the design parameters. Simulation results for a case study bus with flexible structure confirm the strength of the proposed design. For those who wish to pursue future research on this subject few suggestions are given.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.393

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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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
Published2010
Admission routes1
Has abstractyes

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