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Record W1986267420 · doi:10.1115/detc2011-48050

Optimal Level Set Vibration Control of Plate Structures

2011· article· en· W1986267420 on OpenAlexaff
Masoud Ansari, Amir Khajepour, Ebrahim Esmailzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsOntario Tech UniversityUniversity of Waterloo
Fundersnot available
KeywordsVibrationTopology optimizationVibration controlModalCantileverSet (abstract data type)Level set methodControl theory (sociology)Mode (computer interface)Topology (electrical circuits)Optimal controlEnergy (signal processing)Structural engineeringModal analysisComputer scienceMathematicsAcousticsEngineeringControl (management)Materials scienceMathematical optimizationPhysicsFinite element methodArtificial intelligence

Abstract

fetched live from OpenAlex

This research is motivated by the need for control of flexural vibrations of lightweight plates. It addresses application of the level set method in optimal control of vibrations in plate-like structures. One of the most commonly practiced methods in control of vibration is to apply constrained layer damping patches to the surface of a structure. In order to consider the weight efficiency of the structure, the best shape and locations of the patches should be determined to achieve the optimum vibration suppression with lowest amount of damping patch. A novel topology optimization approach is proposed that is capable of finding the optimum shape and locations of the patches simultaneously. A 2D cantilever plate, undergoing flexural vibrations, will be considered. The optimal damping set will be found in the structure, such that the lowest modal energy in the fundamental vibration mode of the system is achieved. The proposed level set topology optimization method shows capability of determining the optimum damping set in structures accurately.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.574

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 head0.021
GPT teacher head0.207
Teacher spread0.186 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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