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Record W2379761015

Optimal design for partial constrained layer damping

2011· article· en· W2379761015 on OpenAlexaff
Lu Qiuhai

Bibliographic record

VenueJournal of Tsinghua University(Science and Technology) · 2011
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsTopology optimizationModalFinite element methodConstrained-layer dampingVibrationOptimal designLayer (electronics)Topology (electrical circuits)Structural engineeringEngineeringControl theory (sociology)Computer scienceVibration controlMaterials scienceAcousticsPhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

Constrained layer damping is widely used in vibration suppression for plate like structures,especially the partial constrained layer damping(PCLD).Optimal design for PCLD has been studied in this paper by using Ansys.The finite element model of PCLD is established in Ansys firstly,then,in order to improve the designated modal damping ratios of a plate,topology optimization design is carried out based on the FE model with Cellular Automata(CA) algorithm and the modified version(CAM) by the author.Finally,integrated optimization method of multiparameter design and topology shape design of the PCLD is presented in this paper.Numerical simulations show that the method presented in this paper gains higher damping effectiveness.It can provide a simple design guideline for PLCD optimization in engineering applications with plates and shells.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.202
Teacher spread0.181 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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