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Record W2101209839 · doi:10.1109/tmag.2006.885859

Modeling and Analysis of Eddy-Current Damping for High-Precision Magnetic Levitation of a Small Magnet

2006· article· en· W2101209839 on OpenAlexaff
Çağlar Elbüken, Ehsan Shameli, Mir Behrad Khamesee

Bibliographic record

VenueIEEE Transactions on Magnetics · 2006
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEddy currentLevitationMagnetMagnetic levitationElectrodynamic suspensionPhysicsMechanicsElectromagnetic coilEddy current brakeVibrationMagnetic fieldAcousticsNuclear magnetic resonanceMagnetic energyMagnetization

Abstract

fetched live from OpenAlex

This paper presents modeling and analysis of eddy-current damping that is formed by a conductive plate placed below the levitating object in order to suppress vibrations and ensure stability. It is demonstrated that vibrations should be damped to preserve stability and precision especially for stepwise motion. The levitated object is a small permanent magnet in our experiments. A magnetic drive unit is used for vertical motion of the magnet. Eddy-current distribution in the plate is calculated by solving diffusion equation for vector magnetic potential. The eddy force applied to the object is derived by a coil model representation. It is shown that if a 20 mm radius, 9 mm thick aluminum circular plate is used for eddy-current damping, the levitated object can closely follow a step input with a steady-state precision varying between 0.04 and 0.07 mm depending on the plate object distance. Eddy-current damping is a key technique that improves levitation performance to increase the diversity of applications of magnetic levitation systems in micromanipulation and microelectronic fabrication

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

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