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Record W1581262194 · doi:10.4271/2009-01-2127

Design of Shape for Visco-Elastic Vibration Isolation Element by Topological and Shape Optimization Methods

2009· article· en· W1581262194 on OpenAlexaff
Hwan-Youp Oh, Kwang-Joon Kim

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2009
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsVibration isolationShape optimizationVibrationTopology (electrical circuits)Topology optimizationViscoelasticityComputer scienceElement (criminal law)Finite element methodMaterials scienceStructural engineeringAcousticsPhysicsEngineeringComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

Design of geometric shape for visco-elastic vibration isolation elements is frequently based on experiences, intuitions, or trial and errors. Such practices make it often difficult for drastically different or new concepts to come out. In this paper, both a topological method and a shape optimization method are combined together to find out a most desirable isolator shape efficiently by using two commercial engineering programs, ABAQUS and MATLAB. The procedure is divided into two steps. At first, the topology optimization method is employed to find an initial shape, where material density of each finite element is chosen as either 0 or 1 for physical realization. Based on the initial shape, then, finer tuning is done by the boundary movement method. An illustration of the procedure is presented for a vehicle engine mount and the effectiveness will be 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2009
Admission routes1
Has abstractyes

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